New Upstream Snapshot - r-cran-vim

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• Maintainer email: r-pkg-team@alioth-lists.debian.net
• Automatic publish policy: main: push-derived , pristine-tar: push-derived , upstream: push-derived
• Last processed: 2023-01-20T07:50 (took 9m17s)
• Branch URL: https://salsa.debian.org/r-pkg-team/r-cran-vim.git -b master (taken from version 6.2.2+dfsg-1)
• Queue position: 155625 (a 92w1d wait)

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Summary

Merged new upstream version: 6.2.2+git20221220.0.96c912c+dfsg (was: 6.2.2+git20220823.1.1c1aac6+dfsg).

Resulting package

Built on 2023-01-20T07:50 (took 9m17s)

The resulting binary packages can be installed (if you have the apt repository enabled) by running one of:

apt install -t fresh-snapshots r-cran-vim-dbgsymapt install -t fresh-snapshots r-cran-vim

Diff

diff --git a/DESCRIPTION b/DESCRIPTION
index c7be8c8..5787fe7 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,5 +1,5 @@
 Package: VIM
-Version: 6.2.2
+Version: 6.2.3
 Title: Visualization and Imputation of Missing Values
 Authors@R: c(
   person("Matthias", "Templ", email = "matthias.templ@gmail.com", role = c("aut","cre")),
@@ -8,9 +8,9 @@ Authors@R: c(
   person("Gregor", "de Cillia", email = "gregor.decillia@statistik.gv.at", role = c("aut")),
   person("Bernd", "Prantner", role = c("ctb")),
   person("Wolfgang", "Rannetbauer", role = c("aut")))
-Depends: R (>= 3.5.0),colorspace,grid
-Imports: car, grDevices, magrittr, robustbase, stats, sp,
-        vcd,MASS,nnet,e1071,methods,Rcpp,utils,graphics,laeken,ranger,
+Depends: R (>= 4.1.0),colorspace,grid
+Imports: car, grDevices, robustbase, stats, sp, vcd, nnet, e1071,
+        methods, Rcpp, utils, graphics, laeken, ranger, MASS,
         data.table(>= 1.9.4)
 Suggests: dplyr, tinytest, knitr, rmarkdown, reactable, covr, withr
 Description: New tools for the visualization of missing and/or imputed values
@@ -28,11 +28,12 @@ License: GPL (>= 2)
 URL: https://github.com/statistikat/VIM
 Repository: CRAN
 LinkingTo: Rcpp
-RoxygenNote: 7.1.2
+RoxygenNote: 7.2.3
 Encoding: UTF-8
+Roxygen: list(markdown = TRUE)
 VignetteBuilder: knitr
 NeedsCompilation: yes
-Packaged: 2022-08-23 14:44:03 UTC; alex
+Packaged: 2023-01-20 07:46:23 UTC; root
 Author: Matthias Templ [aut, cre],
   Alexander Kowarik [aut] (<https://orcid.org/0000-0001-8598-4130>),
   Andreas Alfons [aut],
@@ -40,4 +41,3 @@ Author: Matthias Templ [aut, cre],
   Bernd Prantner [ctb],
   Wolfgang Rannetbauer [aut]
 Maintainer: Matthias Templ <matthias.templ@gmail.com>
-Date/Publication: 2022-08-25 12:12:42 UTC
diff --git a/MD5 b/MD5
deleted file mode 100644
index 7a57279..0000000
--- a/MD5
+++ /dev/null
@@ -1,154 +0,0 @@
-7b5703cbc28438164bc7f89272e8e56d *DESCRIPTION
-eaef8e2486a89807f9bc278ebd6f3e9c *NAMESPACE
-73e012b42c45a0d3cbc13d086a17627a *NEWS.md
-e75ccf53126bb7ca75c13e472cee8c5c *R/RcppExports.R
-98e2c815ef72833d657069ebfdc05360 *R/VIM-package.R
-b93f5f637a159d996e23d43eb46a5861 *R/aggr.R
-4e3571c7a7c32136f4de16ee35fb6051 *R/alphablend.R
-869dd36f5a6ab0c39e448c28a314ed6d *R/barMiss.R
-5d99b5141cfa159310f7394a05b87344 *R/bgmap.R
-c371921b3d522111072daf9e25648926 *R/colSequence.R
-2c7718da2d2c9f198c2f14c31b30dcca *R/colormapMiss.R
-453f7bf83841ab83aa77943b7439fad8 *R/evaluation.R
-c6de46824dd21d3ddb4186e649f966cc *R/gapMiss.R
-7ffd8ac737aaf411f9d9d5d3f1f60f51 *R/gowerD.R
-c9ff12941819814b08b912b7639770a0 *R/growdotMiss.R
-48e70d75a69562f356f7ac0cd159ec36 *R/histMiss.R
-dc3e8b18acc1adc2085d5f594e34ba9c *R/hotdeck.R
-ca67e898790fe0fd3bbf29f94e365a05 *R/impPCA.R
-91988d29dbe1a79a7de6e1753eb8e08a *R/irmi.R
-ea9a1b1bcd354a60b1734124ec89e50b *R/kNN.R
-94dfed4fb32fe7d9f4c7df08f3275070 *R/mapMiss.R
-30cf7a333fd8fec361f3256db716168f *R/marginmatrix.R
-e8b83eda65f31f60ddb094d64ab469e3 *R/marginplot.R
-50fdccb49d42086b925d557c62b73938 *R/matchImpute.R
-ee4d9681b431bd74ccb476e113cf7639 *R/matrixplot.R
-90bcb034b5fafd5dd20614d380add10f *R/maxCat.R
-5b05292abdb40f7482735f64440b0840 *R/medianSamp.R
-9e7c801996f194edcf005733f6c2a5c9 *R/mosaicMiss.R
-d44121e9bffd9353a8cee27064d3720b *R/pairsVIM.R
-cf550341743d7ac534d16d73b9b72840 *R/parcoordMiss.R
-5d7e6c79923d48b4cbbf140ef47ca1a6 *R/pbox.R
-d7bd60f4169890d6b22fa04ff27c6b33 *R/prepare.R
-9bb1be565e109b7dff2ebae5d818dd06 *R/rangerImpute.R
-e9b456587619c89999a5fdb5f20f0480 *R/regressionImp.R
-a623adfe864ff0f129542ee5788b7495 *R/rugNA.R
-b611fc62d1896da127a9366aa67a2aaf *R/sampleCat.R
-781b5ab73aa959c5767a668bff299b7a *R/scattJitt.R
-f09a7aaa680795a96e4c7994b17076be *R/scattMiss.R
-93882fe3d46c9347c29a6b23794b298c *R/scattmatrixMiss.R
-89cf7527938fe2415bc3a23dd7c209f9 *R/spineMiss.R
-3a9de749ac6b03d8838e91e8454a3d45 *R/tableMiss.R
-be6204d1ed4e0d06b5158319abfde96d *R/utils.R
-f5e88e3e18466bbb08b70430e50e2286 *R/zzz.R
-7b66afde4ab0e989dc5246359d4768cc *build/vignette.rds
-c1a475ba98d07a35a5dae3ee66b41477 *data/Animals_na.rda
-6cbe78d4577345db89c24ca45920537c *data/SBS5242.rda
-777bf8c883afeb12c26dc33c68f93ce5 *data/bcancer.rda
-778187cf265f5cb38628ca69dde90997 *data/brittleness.rda
-d34c5ba20381b8b02fcb9986796e24c8 *data/chorizonDL.rda
-7ef4e6d82ff7ac449cac0653a799c01d *data/colic.rda
-54a11b1b9b76407318599c9081cf1166 *data/collisions.rda
-e5f6f2c04c11df7e0baa0d5c3ad8fae3 *data/diabetes.rda
-6b40f0e6f891bf82694e26a0649df07e *data/food.rda
-a8b1d9566717357c8964fa4741e1446e *data/kola.background.rda
-52b7b07bd3f6810477e731ed5f45d8f0 *data/pulplignin.rda
-e2d0c95ba8bf2c2b7ee8a0df279e0148 *data/sleep.rda
-86683a73bf298b488911df6f3531df3c *data/tao.rda
-ef9e5ab2c0347213e3c5f6a6e88c0715 *data/testdata.RData
-dff86b698a49472b3def808507329355 *data/toydataMiss.rda
-4985b44cf72e70d3c4b3212cfded17c6 *data/wine.rda
-c95aa002ebc5c2721fb8abe8f73bd17c *inst/CITATION
-58647498db51b9a89cc2657a6ba51df6 *inst/doc/VIM.R
-171b0158aa41dc207995df819a89e159 *inst/doc/VIM.Rmd
-b22935308ad5b753f221d4788fe6ffc7 *inst/doc/VIM.html
-774944cd47bcdea8b4df0574b7582685 *inst/doc/VisualImp.R
-3552ef8f7f15755962ff746911887b80 *inst/doc/VisualImp.Rmd
-8944ac300cb519f34b06e84367f1140d *inst/doc/VisualImp.html
-9bbd83ad34e9c9ce6e0cd083d690355e *inst/doc/donorImp.R
-66cbdf5d2371a205c8cc8d6fa2148d61 *inst/doc/donorImp.Rmd
-ada83e1358f64c4aebb6b2cbfc2dde69 *inst/doc/donorImp.html
-88af0220fbd045447f8057dd170679db *inst/doc/irmi.R
-eb49c602f041f7fb289d6296a2677538 *inst/doc/irmi.Rmd
-700cf34c2e6201492f4bebbeda16620e *inst/doc/irmi.html
-e08620c46d8e655b020d1b7f8a88026e *inst/doc/modelImp.R
-6e6c7fbd5ff895af4dd1550bdc189f4d *inst/doc/modelImp.Rmd
-74b1143f3847289781407f30d2f68a2b *inst/doc/modelImp.html
-6d17aaede45e63e1805f74947369108f *inst/tinytest/test_IRMI_ordered.R
-58331ba2fbfcce32d11a88b5dc72d4f5 *inst/tinytest/test_aggFunctions.R
-7527af96e3bbdd26a09e18d7bad94ebf *inst/tinytest/test_data_frame.R
-0f88f9acbc6e5687a735233952b8c2a5 *inst/tinytest/test_gowerDind.R
-efec162b4d28b05f76b4858ea023f1c4 *inst/tinytest/test_graphics.R
-f686bab6d9e5ec931df65e35cf6f26a4 *inst/tinytest/test_hotdeck.R
-595c99ed297262adc219bddf5d78ef3f *inst/tinytest/test_impNA.R
-ab861718ed401b9cb4ba18e45dc05d8f *inst/tinytest/test_impPCA.R
-706b3488b004fe73cca1b71fb87d9ac6 *inst/tinytest/test_irmi_types.R
-ea2218ceefbf1d603d14d51b3a2757b2 *inst/tinytest/test_kNN.R
-96033c7b7566d1ac286fc33a113f8dbe *inst/tinytest/test_kNN_exact.R
-c83da650b1284c01c56f380ea2a206b2 *inst/tinytest/test_kNN_iqr.R
-9f54aeb2b3f9ac965a60e01cec45650e *inst/tinytest/test_kNN_ordered.R
-43b337acb1bd68a6703be6aacacf1059 *inst/tinytest/test_rangerImpute.R
-423ee5ba43e1b134c022b9e842aa039e *inst/tinytest/test_regressionImp.R
-3378d7fb0c8957360a73b62bd9df3d9b *man/Animals_na.Rd
-3ad6bedcd873a9832dfcd23e83efaaed *man/SBS5242.Rd
-6b22f76e6ad7a6ce868921d73549efc6 *man/VIM-package.Rd
-bbb2536ec543196478656fe7edef79b3 *man/aggr.Rd
-f60e6ddae9e8e6eb948afb4a146aa547 *man/alphablend.Rd
-6522fb865719627043c4370154ed71f9 *man/barMiss.Rd
-64ee8fde10a94b1c0f57c36e3654f0ef *man/bcancer.Rd
-3465b43b632a05c8c2e4feda0850b027 *man/bgmap.Rd
-8d2a0b360abc1d2b899ede217ba84aa9 *man/brittleness.Rd
-78890d290e2893899c74178f7f60941a *man/chorizonDL.Rd
-e7289833eca4c4a42bf0d4f1b17ff835 *man/colSequence.Rd
-41077adb85ecde447161814407e8b5cc *man/colic.Rd
-df36a9537b7de46396da289a85e67a3b *man/collisions.Rd
-613fea4b3aced5bf3ba8eaa15f0a3428 *man/colormapMiss.Rd
-b2fb3823686a3513ec5d1f9d381da31b *man/countInf.Rd
-875e09d605103f5c3315b49c05c359e4 *man/diabetes.Rd
-59350d27338d1efdc5cdf0a281ac2cc1 *man/evaluation.Rd
-b935912a770cc71c28155426ccd2df92 *man/food.Rd
-3c85413511426052ac76b8bc9bf3d0ee *man/gapMiss.Rd
-9fb36012c36d702441b38c0bf589df45 *man/gowerD.Rd
-739809136e50d78cac7f4c4f842efe65 *man/growdotMiss.Rd
-cef500f3e2de49f84f2426c59ef23224 *man/histMiss.Rd
-76b7db4d8ad26a43e9e678909845ca1f *man/hotdeck.Rd
-34895896e17822c8591d6090ec989a07 *man/impPCA.Rd
-f46f8d24fbcd5e55710871bd17d66a51 *man/initialise.Rd
-9bcbc37ed7768cceb1362b2649a248ce *man/irmi.Rd
-f29d9150ee5ec798fe32021fa9e4cc9d *man/kNN.Rd
-e168a022bb41658e4a7c8a14edbd64f4 *man/kola.background.Rd
-ff2bb32468a12b251f32b67cdad5c6a2 *man/mapMiss.Rd
-0f9453e6bd98cad3e78dd8c733734092 *man/marginmatrix.Rd
-47532215c3c48bfbfcf1f12911e26319 *man/marginplot.Rd
-2be71235b0f271acbfa19e84078fe93a *man/matchImpute.Rd
-96a775aad018ed5321e4f4093bff48b7 *man/matrixplot.Rd
-ab3a0133fac74f73c39f269b720badb1 *man/maxCat.Rd
-954765eba74be957c1a48bd12396f1aa *man/medianSamp.Rd
-f22f505e21d4097cc50b8702f91f0b8e *man/mosaicMiss.Rd
-17a1dbea06c05b7aa996aaee278205ab *man/pairsVIM.Rd
-492abcb17842d68e4f7b5549fcd9dcc8 *man/parcoordMiss.Rd
-cdebfd71cf71a4ddb435893d4c505f91 *man/pbox.Rd
-bb40b19f7f20ef21c2d4f4b70218c1e6 *man/prepare.Rd
-2bbdeef43c3b912364a318cdf112e387 *man/pulplignin.Rd
-f124a4b9f52c706ea93d3a41a4047752 *man/rangerImpute.Rd
-0abdcbe97e91b5d426d39b72768a6b90 *man/regressionImp.Rd
-29f6e65caf14fd48cc1ff10c9a4ff314 *man/rugNA.Rd
-cd9363b82eb956983003f2188c4409bc *man/sampleCat.Rd
-0f303a04e7938b46edae94f211ed7bf9 *man/scattJitt.Rd
-b970c8b39403b63ac880f910537c1812 *man/scattMiss.Rd
-6bcbe734af67eed35b64feb8cf409053 *man/scattmatrixMiss.Rd
-6a45e1962a913b24a3b53e5b16d665ae *man/sleep.Rd
-61be6749b644b9696ce5ee9002a4052e *man/spineMiss.Rd
-994f96fb1f9dc10af7a7b08abe71e819 *man/tableMiss.Rd
-4dad89141d7a46116de0cd9bf3dc985d *man/tao.Rd
-190e4f8b2500272e86094e490a79edec *man/testdata.Rd
-963287c51089463da54f0d690c5ae2cd *man/toydataMiss.Rd
-70faabe658b2f382c2dfc64d0089df2d *man/wine.Rd
-bd91c38e130d0474ed74caf4ba815125 *src/RcppExports.cpp
-885c4dc2ad2cfdf17ae67f4a0b05fdb0 *src/gowerD.cpp
-31eb58d01404dd35c646c62d2cb71dbd *tests/tinytest.R
-171b0158aa41dc207995df819a89e159 *vignettes/VIM.Rmd
-3552ef8f7f15755962ff746911887b80 *vignettes/VisualImp.Rmd
-66cbdf5d2371a205c8cc8d6fa2148d61 *vignettes/donorImp.Rmd
-eb49c602f041f7fb289d6296a2677538 *vignettes/irmi.Rmd
-6e6c7fbd5ff895af4dd1550bdc189f4d *vignettes/modelImp.Rmd
diff --git a/NAMESPACE b/NAMESPACE
index 323e647..539f0c1 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -50,7 +50,6 @@ export(scattMiss)
 export(scattmatrixMiss)
 export(spineMiss)
 export(tableMiss)
-import(MASS)
 import(Rcpp)
 import(colorspace)
 import(data.table)
@@ -62,6 +61,10 @@ import(nnet)
 import(robustbase)
 import(sp)
 import(stats)
+importFrom(MASS,lqs)
+importFrom(MASS,polr)
+importFrom(MASS,rlm)
+importFrom(MASS,stepAIC)
 importFrom(car,bcPower)
 importFrom(car,powerTransform)
 importFrom(graphics,Axis)
diff --git a/NEWS.md b/NEWS.md
index 3e4c8c3..c1b90ed 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,7 +1,11 @@
+# VIM 6.2.3
+- default robust regression method for irmi for numeric variables changes from rlm to lmrob.
+
 # VIM 6.1.1
 - ordFun as parameter of kNN to control the function applied to ordinal variables
 - methodStand option in gowerD and kNN to switch between range and interquartile range for the standardization of numerical variables
 - donorcond in kNN and hotdeck extended so it also accepts NULL as list element and multiple conditions as character vector
+
 # VIM 6.0.2
 - error message in `hotdeck()` when ord_var and variable overlap
 - family argument of class 'family' now work in regressionImp
diff --git a/R/VIM-package.R b/R/VIM-package.R
index 43c3391..dd08f9a 100644
--- a/R/VIM-package.R
+++ b/R/VIM-package.R
@@ -4,7 +4,6 @@
 #' @import sp
 #' @import stats
 #' @import methods
-#' @import MASS
 #' @import nnet
 #' @import e1071
 #' @import grid
@@ -20,6 +19,7 @@
 #' @importFrom graphics polygon rect strheight strwidth text title
 #' @importFrom utils capture.output flush.console head
 #' @importFrom ranger ranger importance
+#' @importFrom MASS stepAIC lqs polr rlm
 #' @useDynLib VIM
 NULL
 
diff --git a/R/hotdeck.R b/R/hotdeck.R
index d44b8b0..313fd27 100644
--- a/R/hotdeck.R
+++ b/R/hotdeck.R
@@ -225,6 +225,9 @@ imputeHD <- function(xx,variableX,varTypeX,imp_varX,imp_suffixX,
             
             # remaining missing values will be set to a random value from the group
             if(length(Don)>0){
+              if(sum(!TFindex)==0){
+                stop("No donor available for ", v, " in a certain group.")
+              }
               Don[TFindex] <- Don[!TFindex][sample(sum(!TFindex),1)]    
             }
             
diff --git a/R/irmi.R b/R/irmi.R
index 3f915fa..917d3cd 100644
--- a/R/irmi.R
+++ b/R/irmi.R
@@ -28,8 +28,9 @@
 #'   residuals.
 #' @param noise.factor amount of noise.
 #' @param force if TRUE, the algorithm tries to find a solution in any case,
-#'   possible by using different robust methods automatically.
-#' @param robMethod regression method when the response is continuous.
+#'   possible by using different robust methods automatically. 
+#' @param robMethod regression method when the response is continuous. Default is
+#'   MM-regression with `lmrob`.
 #' @param force.mixed if TRUE, the algorithm tries to find a solution in any
 #'   case, possible by using different robust methods automatically.
 #' @param addMixedFactors if TRUE add additional factor variable for each
@@ -52,7 +53,6 @@
 #'   status
 #' @return the imputed data set.
 #' @author Matthias Templ, Alexander Kowarik
-#' @seealso [mi::mi()]
 #' @references M. Templ, A. Kowarik, P. Filzmoser (2011) Iterative stepwise
 #' regression imputation using standard and robust methods.  *Journal of
 #' Computational Statistics and Data Analysis*, Vol. 55, pp. 2793-2806.
@@ -97,7 +97,7 @@
 #' @export
 irmi <- function(x, eps = 5, maxit = 100, mixed = NULL, mixed.constant = NULL,
     count = NULL, step = FALSE, robust = FALSE, takeAll = TRUE, noise = TRUE,
-    noise.factor = 1, force = FALSE, robMethod = "MM", force.mixed = TRUE,
+    noise.factor = 1, force = FALSE, robMethod = "lmrob", force.mixed = TRUE,
     mi = 1, addMixedFactors = FALSE, trace = FALSE, init.method = "kNN",
     modelFormulas = NULL, multinom.method = "multinom", imp_var = TRUE,
     imp_suffix = "imp") {
diff --git a/build/vignette.rds b/build/vignette.rds
index 517afc7..c6bc25b 100644
Binary files a/build/vignette.rds and b/build/vignette.rds differ
diff --git a/debian/changelog b/debian/changelog
index 93c8c4e..80a4775 100644
--- a/debian/changelog
+++ b/debian/changelog
@@ -1,8 +1,13 @@
-r-cran-vim (6.2.2+dfsg-2) UNRELEASED; urgency=medium
+r-cran-vim (6.2.2+git20221220.0.96c912c+dfsg-1) UNRELEASED; urgency=medium
 
+  [ Andreas Tille ]
   * Disable reprotest
 
- -- Andreas Tille <tille@debian.org>  Mon, 19 Sep 2022 13:09:31 +0200
+  [ Debian Janitor ]
+  * New upstream snapshot.
+  * New upstream snapshot.
+
+ -- Andreas Tille <tille@debian.org>  Fri, 20 Jan 2023 07:46:30 -0000
 
 r-cran-vim (6.2.2+dfsg-1) unstable; urgency=medium
 
diff --git a/inst/doc/VIM.html b/inst/doc/VIM.html
new file mode 100644
index 0000000..66135af
--- /dev/null
+++ b/inst/doc/VIM.html
@@ -0,0 +1,430 @@
+<!DOCTYPE html>
+
+<html>
+
+<head>
+
+<meta charset="utf-8" />
+<meta name="generator" content="pandoc" />
+<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
+
+<meta name="viewport" content="width=device-width, initial-scale=1" />
+
+
+
+<title>VIM</title>
+
+<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+  var i, h, a;
+  for (i = 0; i < hs.length; i++) {
+    h = hs[i];
+    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
+    a = h.attributes;
+    while (a.length > 0) h.removeAttribute(a[0].name);
+  }
+});
+</script>
+
+<style type="text/css">
+  code{white-space: pre-wrap;}
+  span.smallcaps{font-variant: small-caps;}
+  span.underline{text-decoration: underline;}
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+<body>
+
+
+
+
+<h1 class="title toc-ignore">VIM</h1>
+
+
+
+<p>VIM introduces tools for visualization of missing and imputed values.
+Forthermore, methods to impute missing values are featured. This
+vignette will give a brief look at a common imputation scenario and
+showcase how VIM can be used to both impute the data and also interpret
+the results visually.</p>
+<div id="visualize-missing-values" class="section level2">
+<h2>Visualize missing values</h2>
+<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(VIM)</span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(sleep)</span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>a <span class="ot">&lt;-</span> <span class="fu">aggr</span>(sleep, <span class="at">plot =</span> <span class="cn">FALSE</span>)</span>
+<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(a, <span class="at">numbers =</span> <span class="cn">TRUE</span>, <span class="at">prop =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+<p>The left plot shows the amount of missings for each column in the
+dataset <code>sleep</code> and the right plot shows how often each
+combination of missings occur. For example, there are 9 rows wich
+contain a missing in both <code>NonD</code> and <code>Dream</code>.</p>
+<p>For simplicity, we will only look at the variables <code>Dream</code>
+and <code>Sleep</code> for the remainer of this vignette. Bivariate
+datasets can be passed to special functions that visualize the structure
+of missings such as <code>marginplot()</code>.</p>
+<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>x <span class="ot">&lt;-</span> sleep[, <span class="fu">c</span>(<span class="st">&quot;Dream&quot;</span>, <span class="st">&quot;Sleep&quot;</span>)]</span>
+<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="fu">marginplot</span>(x)</span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+<p>The <strong><font color="red">red</font></strong> boxplot on the left
+shows the distrubution of all values of <code>Sleep</code> where
+<code>Dream</code> contains a missing value. The
+<strong><font color="#87ceeb">blue</font></strong> boxplot on the left
+shows the distribution of the values of <code>Sleep</code> where
+<code>Dream</code> is observed.</p>
+</div>
+<div id="impute-missing-values" class="section level2">
+<h2>Impute missing values</h2>
+<p>In order to impute missing values, <code>VIM</code> offers a spectrum
+of imputation methods like <code>kNN()</code> (k nearest neighbour),
+<code>hotdeck()</code> and so forth. Those functions can be applied to a
+<code>data.frame</code> and return another <code>data.frame</code> where
+missings are replaced by imputed values.</p>
+<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>x_imputed <span class="ot">&lt;-</span> <span class="fu">kNN</span>(x)</span></code></pre></div>
+<p>To learn more about all implemented imputation methods, three
+vignettes are available</p>
+<ul>
+<li><code>vignette(&quot;donorImp&quot;)</code> explains the donor-based
+imputation methods <code>hotdeck()</code> and <code>kNN()</code></li>
+<li><code>vignette(&quot;modelImp&quot;)</code> gives insight into the model-based
+imputation methods <code>regressionImp()</code> and
+<code>matchImpute()</code></li>
+<li><code>vignette(&quot;irmi&quot;)</code> showcases the <code>irmi()</code>
+method.</li>
+</ul>
+</div>
+<div id="visualize-imputed-values" class="section level2">
+<h2>Visualize imputed values</h2>
+<p>The same functions that visualize missing values can also visualize
+the imputed dataset.</p>
+<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">marginplot</span>(x_imputed, <span class="at">delimiter =</span> <span class="st">&quot;_imp&quot;</span>)</span></code></pre></div>
+<p><img src="data:image/png;base64,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style="display: block; margin: auto;" /></p>
+<p>In this plot three differnt colors are used in the top-right. These
+colors represent the structure of missings.</p>
+<ul>
+<li><strong><font color="#8b5a00">brown</font></strong> points represent
+values where <code>Dream</code> was missing initially</li>
+<li><strong><font color="#ffa500">beige</font></strong> points represent
+values where <code>Sleep</code> was missing initially</li>
+<li><strong>black</strong> points represent values where both
+<code>Dream</code> and <code>Sleep</code> were missing initially</li>
+</ul>
+<p>The <code>kNN()</code> method seemingly preserves the correlation
+between <code>Dream</code> and <code>Sleep</code>.</p>
+</div>
+
+
+
+<!-- code folding -->
+
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+  (function () {
+    var script = document.createElement("script");
+    script.type = "text/javascript";
+    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+    document.getElementsByTagName("head")[0].appendChild(script);
+  })();
+</script>
+
+</body>
+</html>
diff --git a/inst/doc/VisualImp.R b/inst/doc/VisualImp.R
index 49954f3..cf5d677 100644
--- a/inst/doc/VisualImp.R
+++ b/inst/doc/VisualImp.R
@@ -8,7 +8,6 @@ knitr::opts_chunk$set(
 
 ## ----setup, message=F---------------------------------------------------------
 library(VIM)
-library(magrittr)
 dataset <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")] # dataset with missings
 dataset$BodyWgt <- log(dataset$BodyWgt)
 dataset$Span <- log(dataset$Span)
@@ -58,9 +57,9 @@ dataset <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")]
 dataset$BodyWgt <- log(dataset$BodyWgt)
 dataset$Span <- log(dataset$Span)
 imp_knn <- kNN(dataset, variable = "NonD") 
-dataset[, c("NonD", "Span")] %>% 
+dataset[, c("NonD", "Span")] |> 
   marginplot()
-imp_knn[, c("NonD", "Span", "NonD_imp")] %>% 
+imp_knn[, c("NonD", "Span", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
 
 ## ----warning=FALSE------------------------------------------------------------
diff --git a/inst/doc/VisualImp.Rmd b/inst/doc/VisualImp.Rmd
index fa6fe51..e7e98cc 100644
--- a/inst/doc/VisualImp.Rmd
+++ b/inst/doc/VisualImp.Rmd
@@ -29,7 +29,6 @@ The following example demonstrates the functionality of the plotting functions u
 
 ```{r setup, message=F}
 library(VIM)
-library(magrittr)
 dataset <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")] # dataset with missings
 dataset$BodyWgt <- log(dataset$BodyWgt)
 dataset$Span <- log(dataset$Span)
@@ -140,9 +139,9 @@ dataset <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")]
 dataset$BodyWgt <- log(dataset$BodyWgt)
 dataset$Span <- log(dataset$Span)
 imp_knn <- kNN(dataset, variable = "NonD") 
-dataset[, c("NonD", "Span")] %>% 
+dataset[, c("NonD", "Span")] |> 
   marginplot()
-imp_knn[, c("NonD", "Span", "NonD_imp")] %>% 
+imp_knn[, c("NonD", "Span", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
 ```
 
diff --git a/inst/doc/VisualImp.html b/inst/doc/VisualImp.html
new file mode 100644
index 0000000..3c3dab3
--- /dev/null
+++ b/inst/doc/VisualImp.html
@@ -0,0 +1,543 @@
+<!DOCTYPE html>
+
+<html>
+
+<head>
+
+<meta charset="utf-8" />
+<meta name="generator" content="pandoc" />
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+
+<meta name="viewport" content="width=device-width, initial-scale=1" />
+
+<meta name="author" content="Wolfgang Rannetbauer" />
+
+
+<title>Supportive Graphic Methods</title>
+
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+
+
+
+
+<h1 class="title toc-ignore">Supportive Graphic Methods</h1>
+<h4 class="author">Wolfgang Rannetbauer</h4>
+
+
+
+<div id="overview" class="section level2">
+<h2>Overview</h2>
+<p>In addition to imputation methods, <code>VIM</code> provides a number
+of functions, which can be used to plot results in sophisticated
+ways.</p>
+<p>This vignette showcases selected plotting functions, which are very
+supportive in context with visualizing missing and imputed values.</p>
+<div id="data" class="section level3">
+<h3>Data</h3>
+<p>The following example demonstrates the functionality of the plotting
+functions using a subset of <code>sleep</code>. In order to emphasize
+the features of the methods, the missing values in the dataset are
+imputed via <code>kNN()</code> or <code>regressionImp()</code>. Both are
+powerful donor-based imputation methods and also included in the
+<code>VIM</code> package. (see <code>vignette(&quot;donorImp&quot;)</code>)</p>
+<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(VIM)</span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>dataset <span class="ot">&lt;-</span> sleep[, <span class="fu">c</span>(<span class="st">&quot;Dream&quot;</span>, <span class="st">&quot;NonD&quot;</span>, <span class="st">&quot;BodyWgt&quot;</span>, <span class="st">&quot;Span&quot;</span>)] <span class="co"># dataset with missings</span></span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>dataset<span class="sc">$</span>BodyWgt <span class="ot">&lt;-</span> <span class="fu">log</span>(dataset<span class="sc">$</span>BodyWgt)</span>
+<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>dataset<span class="sc">$</span>Span <span class="ot">&lt;-</span> <span class="fu">log</span>(dataset<span class="sc">$</span>Span)</span>
+<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>imp_knn <span class="ot">&lt;-</span> <span class="fu">kNN</span>(dataset) <span class="co"># dataset with imputed values</span></span></code></pre></div>
+<p>To keep things as simple as possible, the plotting functions in
+<code>VIM</code> uses three main colors. Each color represents a
+property:</p>
+<ul>
+<li><strong><font color="#00a2ff">BLUE</font></strong> observed values
+are highlighted in blue</li>
+<li><strong><font color="#ff0000">RED</font></strong> missing values are
+highlighted in red</li>
+<li><strong><font color="#ffa500">ORANGE</font></strong> imputed values
+are highlighted in orange</li>
+</ul>
+</div>
+</div>
+<div id="function-aggr" class="section level2">
+<h2>Function aggr()</h2>
+<p>The <code>aggr()</code> function calculates or plots the amount of
+missing/imputed values in each variable and the amount of
+missing/imputed values in certain combinations of variables.</p>
+<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="fu">aggr</span>(dataset) </span></code></pre></div>
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" style="display: block; margin: auto;" /></p>
+<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">aggr</span>(imp_knn, <span class="at">delimiter =</span> <span class="st">&quot;_imp&quot;</span>) </span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+<p>The plots indicate that all missing values in the dataset are imputed
+via <code>knn()</code>. (All the previously red bars changed their color
+to orange)</p>
+</div>
+<div id="function-barmiss" class="section level2">
+<h2>Function barMiss()</h2>
+<p>The <code>barMiss()</code> function provides a barplot with
+highlighting of missing/imputed values in other variables by splitting
+each bar into two parts. Additionally, information about missing/imputed
+values in the variable of interest is shown on the right hand side.</p>
+<p>If <code>only.miss=TRUE</code>, the missing/imputed values in the
+variable of interest are visualized by one bar on the right hand side.
+If additional variables are supplied, this bar is again split into two
+parts according to missingness/number of imputed missings in the
+additional variables.</p>
+<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co"># for missing values</span></span>
+<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>x <span class="ot">&lt;-</span> sleep[, <span class="fu">c</span>(<span class="st">&quot;Exp&quot;</span>, <span class="st">&quot;NonD&quot;</span>, <span class="st">&quot;Sleep&quot;</span>)]</span>
+<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a><span class="fu">barMiss</span>(x, <span class="at">only.miss =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
+<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co"># for imputed values</span></span>
+<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>x_IMPUTED <span class="ot">&lt;-</span> <span class="fu">regressionImp</span>(NonD <span class="sc">~</span> Sleep, <span class="at">data =</span> x)</span>
+<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; There still missing values in variable NonD . Probably due to missing values in the regressors.</span></span>
+<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a><span class="fu">barMiss</span>(x_IMPUTED, <span class="at">delimiter =</span> <span class="st">&quot;_imp&quot;</span>, <span class="at">only.miss =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" />
+The plot indicates that there are still some missings in NonD. This is
+because the regression model could not be applied to observations, where
+Sleep is unobserved.</p>
+</div>
+<div id="function-scattmiss" class="section level2">
+<h2>Function scattMiss()</h2>
+<p>In addition to a standard scatterplot, lines are plotted in
+<code>scattMiss()</code> for the missing values in one variable. If
+there are imputed values, they will be highlighted.</p>
+<p>Information about missing values in one variable is included as
+vertical or horizontal lines, as determined by the <code>side</code>
+argument. The lines are thereby drawn at the observed x- or y-value. In
+case of imputed values, they will additionally be highlighted in the
+scatterplot. Supplementary, percentage coverage ellipses can be drawn to
+give a clue about the shape of the bivariate data distribution.</p>
+<p>In contrast to the other examples, <code>regressionImp()</code> is
+used for imputing missing values. This has been done deliberately to
+highlight the functionality of <code>scattMiss()</code>. The following
+plots makes it easy to indentify missing/imputed values.</p>
+<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>dataset <span class="ot">&lt;-</span> sleep[, <span class="fu">c</span>(<span class="st">&quot;Span&quot;</span>, <span class="st">&quot;NonD&quot;</span>,<span class="st">&quot;Sleep&quot;</span>)]</span>
+<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a><span class="co"># for missing values</span></span>
+<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a><span class="fu">scattMiss</span>(dataset[,<span class="sc">-</span><span class="dv">3</span>])</span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="co"># for imputed values</span></span>
+<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>imp_regression <span class="ot">&lt;-</span> <span class="fu">regressionImp</span>(NonD <span class="sc">~</span> Sleep, dataset)</span>
+<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; There still missing values in variable NonD . Probably due to missing values in the regressors.</span></span>
+<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a><span class="fu">scattMiss</span>(imp_regression[,<span class="sc">-</span><span class="dv">3</span>], <span class="at">delimiter =</span> <span class="st">&quot;_imp&quot;</span>)</span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+<p>The plot indicates that there are still some missings in
+<code>NonD</code>. This is because the regression model could not be
+applied to observations, where <code>Sleep</code> is unobserved.</p>
+</div>
+<div id="function-histmiss" class="section level2">
+<h2>Function histMiss()</h2>
+<p>The <code>histMiss()</code> function visualizes data in a histogram
+with highlighting the missing/imputed values in other variables by
+splitting each bin into two parts. Additionally, information about
+missing/imputed values in the variable of interest is shown on the right
+hand side.</p>
+<p>If <code>only.miss=TRUE</code>, the missing/imputed values in the
+variable of interest are visualized by one bar on the right hand side.
+If additional variables are supplied, this bar is again split into two
+parts according to missingness/number of imputed missings in the
+additional variables.</p>
+<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="do">## for missing values</span></span>
+<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>x <span class="ot">&lt;-</span> sleep[, <span class="fu">c</span>(<span class="st">&quot;Span&quot;</span>, <span class="st">&quot;NonD&quot;</span>,<span class="st">&quot;Sleep&quot;</span>)]</span>
+<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a><span class="fu">histMiss</span>(x, <span class="at">only.miss =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
+<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="co"># for imputed values</span></span>
+<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>x_IMPUTED  <span class="ot">&lt;-</span> <span class="fu">regressionImp</span>(NonD <span class="sc">~</span> Sleep, <span class="at">data =</span> x)</span>
+<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; There still missing values in variable NonD . Probably due to missing values in the regressors.</span></span>
+<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a><span class="fu">histMiss</span>(x_IMPUTED, <span class="at">delimiter =</span> <span class="st">&quot;_imp&quot;</span>, <span class="at">only.miss =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+</div>
+<div id="function-matrixplot" class="section level2">
+<h2>Function matrixplot()</h2>
+<p>The <code>matrixplot()</code> function creats a matrix plot, in which
+all cells of a data matrix are visualized by rectangles. Available data
+is coded according to a continuous color scheme, while missing/imputed
+data is visualized by a clearly distinguishable color.</p>
+<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>x <span class="ot">&lt;-</span> sleep[, <span class="fu">c</span>(<span class="st">&quot;Dream&quot;</span>, <span class="st">&quot;NonD&quot;</span>,<span class="st">&quot;Sleep&quot;</span>, <span class="st">&quot;BodyWgt&quot;</span>)]</span>
+<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>x<span class="sc">$</span>BodyWgt <span class="ot">&lt;-</span> <span class="fu">log</span>(x<span class="sc">$</span>BodyWgt)</span>
+<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a><span class="co"># for missing values</span></span>
+<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a><span class="fu">matrixplot</span>(x, <span class="at">sortby=</span><span class="st">&quot;BodyWgt&quot;</span>)</span></code></pre></div>
+<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
+<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="co"># for imputed values - multiple variable imputation with regrssionImp()</span></span>
+<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>x_IMPUTED  <span class="ot">&lt;-</span> <span class="fu">regressionImp</span>(NonD <span class="sc">+</span> Dream <span class="sc">~</span> Sleep, <span class="at">data =</span> x)</span>
+<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; There still missing values in variable NonD . Probably due to missing values in the regressors.</span></span>
+<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; There still missing values in variable Dream . Probably due to missing values in the regressors.</span></span>
+<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a><span class="fu">matrixplot</span>(x_IMPUTED, <span class="at">delimiter =</span> <span class="st">&quot;_imp&quot;</span>, <span class="at">sortby =</span> <span class="st">&quot;BodyWgt&quot;</span>)</span></code></pre></div>
+<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
+</div>
+<div id="function-marginplot" class="section level2">
+<h2>Function marginplot()</h2>
+<p>In addition to a standard scatterplot, information about
+missing/imputed values is shown in the plot margins. Furthermore,
+imputed values are highlighted in the scatterplot.</p>
+<p>Boxplots for available and missing/imputed data, as well as
+univariate scatterplots for missing/imputed values in one variable are
+shown in the plot margins.Imputed values in either of the variables are
+highlighted in the scatterplot.</p>
+<p>Furthermore, the frequencies of the missing/imputed values can be
+displayed by a number (lower left of the plot). The number in the lower
+left corner is the number of observations that are missing/imputed in
+both variables.</p>
+<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>dataset <span class="ot">&lt;-</span> sleep[, <span class="fu">c</span>(<span class="st">&quot;Dream&quot;</span>, <span class="st">&quot;NonD&quot;</span>, <span class="st">&quot;BodyWgt&quot;</span>, <span class="st">&quot;Span&quot;</span>)]</span>
+<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>dataset<span class="sc">$</span>BodyWgt <span class="ot">&lt;-</span> <span class="fu">log</span>(dataset<span class="sc">$</span>BodyWgt)</span>
+<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>dataset<span class="sc">$</span>Span <span class="ot">&lt;-</span> <span class="fu">log</span>(dataset<span class="sc">$</span>Span)</span>
+<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a>imp_knn <span class="ot">&lt;-</span> <span class="fu">kNN</span>(dataset, <span class="at">variable =</span> <span class="st">&quot;NonD&quot;</span>) </span>
+<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a>dataset[, <span class="fu">c</span>(<span class="st">&quot;NonD&quot;</span>, <span class="st">&quot;Span&quot;</span>)] <span class="sc">|&gt;</span> </span>
+<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">marginplot</span>()</span></code></pre></div>
+<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
+<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>imp_knn[, <span class="fu">c</span>(<span class="st">&quot;NonD&quot;</span>, <span class="st">&quot;Span&quot;</span>, <span class="st">&quot;NonD_imp&quot;</span>)] <span class="sc">|&gt;</span> </span>
+<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">marginplot</span>(<span class="at">delimiter =</span> <span class="st">&quot;_imp&quot;</span>)</span></code></pre></div>
+<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAkAAAAEgCAIAAADT5FumAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO3dd5wU5cEH8GfK9n6990bvVWnSRBAVbBgL9iTqa7Alxlhi1GhMLIlG0diiWIAIiFhAqihFkSIKHNxxvd/tbd+d+v5xcB53HNwde7s7d7/vH3682TIPu7Pzm+eZp1CyLBMAAAClocNdAAAAgJ5AgAEAgCIhwAAAQJEQYAAAoEgIMAAAUCQEGAAAKBICDAAAFAkBBgAAioQAAwAARUKAAQCAIiHAAABAkRBgAACgSAgwAABQJAQYAAAoEgIMAAAUCQEGAACKhAADAABFQoABAIAiIcAAAECREGAAAKBICDAAAFAkBBgAACgSAgwAABQJAQYAAIqEAAMAAEVCgAEAgCIhwAAAQJEQYAAAoEgIMAAAUCQEGAAAKBICDAAAFAkBBgAAioQAAwAARUKAAQCAIiHAAABAkRBgAACgSAgwAABQJAQYAAAoEgIMAAAUCQEGAACKhAADAABFQoABAIAiIcAAAECREGAAAKBICDAAAFAkBBgAACgSAgwAABQJAQYAAIqEAAMAAEVCgAEAgCIhwAAAQJEQYAAAoEgIMAAAUCQEGAAAKBICDAAAFAkBBgAAioQAAwAARUKAAQCAIiHAAABAkRBgAACgSAgwAABQJAQYAAAoEgIMAAAUCQEGAACKhAADAABFQoABAIAiIcAAAECREGAAAKBICDAAAFAkBBgAACgSAgwAABQJAQYAAIqEAAMAAEVCgAEAgCIhwAAAQJEQYAAAoEgIMAAAUCQ23AUInXvvvXfr1q2h3y/HcWq1ugcvlGVZEASVShX0IvUTXkHiRFkiREVTBpamqXAXCKAv0ul0K1eujI+PD/2u+1GAffvtt3feeeeQIUNCuVNJkkpKSrKysnrwWo7jamtrU1NTg16qPo+X5C/L3VqGzrWoVAxd4xEKHdyUJH2ivh8d8AChsWjRotraWgRYrysoKBg1alQo9yhJksFgGDhwYA9e6/f7KyoqcnJygl6qPm9rlWdMvHxRmrF1S6mL/6zMPXeQDdUwgODS6XTh2nX/CrAgOnbsmMPhqK+vj42NNRgMBQUF4S4R/OKYg5uXYWq7Jd2kUtOkzifE63DMA/QR+DH30P33319eXr5v375hw4ZZrdaNGzeGu0Twi4Ak65j2HZT0LO0X5bCUBwB6AwKsh1atWkUI0ev127dvD2MNGk7LpmFqfYK5Td8ZUZbr/WKUhgljqQAguNCNHvqgUbG6zZWe5oDY8qcgyRsqPKlGlUmFAx6g70ANDPqgPIvaw0v/LXTE61ktQ1V6+GSDqm2fDgDoAxBg52TevHk9G+MVXIIkN/hFmZBoLaPGcCdCCCEjYrQDbJoqDx8Q5UmJejQeAvQ9CLBzsnz58nAXgext8G+v9prUNEVIMyeNi9ONj8c9OUII0TJUljn8lxcA0EsQYMq2v9G/t8H/qzxLSw3DwUmflDgJIcgwAOjzcE/7nFRVVYW3ADtrfXPSjK3tYxY1fXG6aXedT0Z3cQDo6xBg52TAgAGBQCBcexdl2clJ7aZHsmoYhiJuQQpXqQAAQgMBdk54npeksEUFTVEURQTplNqWTAgnySp05QCAvi6CA0yq+v7TL/fXoy2sUxQhaUbVwaZTqoBHmgNRGkbLIMAAoI+L4ADjv/3bVYtf2ceHuxwR7YJkw9c13m3V3lqf0OAXd9R615d7ZqViwBMA9H2R0AtRKl/18MMfl4ntN5ft5hzqZ2687r80UY+769U7x0bewlhhHwcWo2Vuyrd+U+tdV+qWZTnJoFqcbzWrI/i6pN+TZbKv0X/IHnDykk3DjIzR5lrUskz2NvoP2wMuXrJpmFGx2mwMAAA4m0gIMEqjcuxZvewnryVzREFca0rJTW5ZZKuKjnkookl3d6Ep0W63f/XVV509Wl1d7fP5glPkkyJhHJhBRc9KQZVLGWRCVhQ7JVmemKC3quk6v7itylPu4uv8AkXIeQl6s5qu84mbKz3lbn5qkiHc5QWIaBERYHHzXtq5e+IDN9y9khn71Nt/vSpfTwghgZVXRt0V9eK2V2d29VK0tLR0xYoVnT1aWVm5b9++adOmBaXQkaMpIG6t8lZ4eEkmyQZ2UqIeK4ZErMP2ACfJv8qxUBQhhFg1TLpR9epPTVYNc32+teW+pU3DpJtU/zlkHxKljdZiAhGATkXKmc4w4JqXv5544WM33j5h3Lqn3nrxttG27r/J8OHDz1AlMplMJpOps0d7pqqqKikpKbjv2S3VXmFFkfO8BP2sFANNkUIHt/yY8+IMU4Yp8tpbgZDjLn6wTUO16WGjYSi9iraeOgOYlqFyLeoSF48AAziDSLpZosm4+K8bfvj4Gvsz00fOf2pTlQK6b4R3HBghZFOlZ0aKYVSs1qCidSw9LFo7L8O0scIdxiJ1Xb1PKHRwNd52AwH6MkGS1R07iMoU6bBNw1B8//lcAHokUmpgJ7EJUx9cs2fGK3fesGBsPc2xV4a7QGcW3nFgskyqPMLVOZq2GzNNKhcv+QRZx0ZuT3p7QPyszO3hpRgt4+AkQZYvSjMmG0JXa9z93frvP3su0HRcE5U5du69o0fPDM1+Y3VMuZsfaDvlKxNkiZfaX0qWu/nzEvShKRWAQkVagBFCCB015o5lu6cuf/GtXdy49D7bhPL3X50SMPct6/bldktydhyyzNCUKMuk41V9ZBAkeXmRc0ysbmSstmXLMQf3v2LXjQXW0KzX9dqzi5z7VxCKJkT2Nh3f8uJXe4dddet9y0Kw62HR2jcPNycZAkOiNIQQUSbf1nhVNF3tFX5qCgw6sVHeXu0TZZJpQkdEgDOJxAAjhBDKOOiqh/5+VbiL0WvapVfLlu5mGEORKA1d7ubTjL/UXer9Ik2IMYJXbjzm5KwapjW9CCE5FvWgKM2BRn8I6hy7dn3m3P8REztwxk2vpaUXlJUe+urNWxx7P/juu+vHjJnd23vXs/SV2eYvy91fV3ssaqbRL6Ya2atzzF5B+rzMva3aY1YzjX4xzai6IstMRegVCECkiNQAC7mysrL6+vqGhoaYmBi1Wj1kyJCuvCrs48DOT9R/Vua+ON2UbGAJIbU+YW2J67zEiG56avKL7eZvlGSSoGeLHFwI9v7t8ocIo7377/tphiGEDBly3oBnf3zhZtM3yx8MQYARQuJ07HV51mZOdHGSTcO0XGoYVfQN+dbmgOjipSgNY4jg6w+AyIEAO+GJJ5744Ycffvrpp9zcXIPBsGHDBqPx7IOrwj4OLN+qIYT6pMQlyTJNUaIsT00yDI7SnP2V4aNmKLf/xLD1405ua7W30S9KMtGzVJ1PiOvlMQCis5y2ZbakVwuWZWlbpugo79X9tmNVM1Z1++Zxq4axYuFNgC5DgJ3w2muvEUJyc3M//vjjnJyccBenG/Kt6nyr2sVLkixbOpwTI1CWWb2z1jEpUSpy8ttrvLNSDKlG1buFjjQj+9Ex5+XZ5nb1s+CiWL3st7fbKPuaKTaiq60A0BFaKs5J2NcDa2VS0cFKL1EmjX4xIPZWH+4oDTMyRvtOoeOrCvfkRD0nysuOOmK1zIwU47Rkw9YqT+szHZx4oNG/u85X4gramIq44ZfJrpqvNvy3dcv6L9+SPTVxI64I1i4AIDRQAzsnAwYMqKur02i63WR33zL53HshBh0nyV9Xe/c3+I0q2iNIiXp2ZoqxN8bSTkzQ2zTM52Xu3XU+i5oZH68rsGoIIXkW9ZflJwaxfVvj3VPvz7aotQx1yO7ZSsilmWbLOU/zeMXip1/a9da+d27av+pP2ti8QF2h5KoiatPl1//lXP9V3VfjFer9op6lkg0qLCAA0F0IsFNMmTIlKiqq688/l3FgbTMsEtKLELL6uEvFOzOLVjprjtgsieaBcz84lnljvrU3+hQk6Fm9il6cb227kaJIywdx2B441MzdPMCqZ0/semetb02J8/o8a4d36h6tRnftMz8tf34RV7bL564lsqTOmHDV3R9oNbpzfOducfPSp6UuJyclG1ivIH9W6p6SpB8arT37K8NEJuRIc6DGK1CEpBhVmGsYIgEC7BT/+c9/Qravjj3pw6vKI9Qe2sx9+mtzzgVxmaNc9sofX51rHH3Tnug/Te6Fbo1WNcOJcqNfbFvDK3JwyXqWELK/0T8pUd+aXoSQ8fG6/Y3+er8Ye841woTY1Mv+uPmHqobG2uOxCVkjEqPjjaGeeWvVcVemSXVetr6lr3xTQPzomMOiYdI7LwkvyXvq/ZUeXpJJkoEdHavThKrS5hWkFUVOlqayzWqZkK+rvd/V+RZkmk8zqwhACCHAwqNdevVgEFjQlTU2BtbedsFvPhw5/MR8x/bLH3zz4XGFKWMnJ14a9N1RFJmcqP/4uHNOqjHFqJJlcsQR2FDpuSzDRAhxcFJMh6CK1jKOQBACbGuV51AzNy7ONiIpptEvflrmHmjT9EZId6bWJ/gE6fw2e4zSMOcn6vfW+zsLMBcvLTvqSDGwQ6O1NEWOOrj/HLJfnWMJzWSJ68s96SZV6+z44+N1n5e5t1Z7Z6ZgvnwIJ3TiOEV1dXW3nh/EcWBhr5CVHfyKSRjeml6EEJs1JmnK79z7O53g/xwNj9FOStR/VuZ+/kDj8wcav6vzLcg0pRhVhBC9inZy7dtmXZzUtk7WM9Ve4Wc7d2O+dUSMNs2oGhGjXZxvPdjkr/EK5/jOXWcPiB1HC8Tr2KZA+0XxWm2s9AyN0sxLN+Va1Nlm9YWpxvMS9OvPOOmlJJOgdMThJbnYybUdY04RMjlR/1OTPwjvDhFJlIkiZuJEDYxMnDhxx44dHbczNKmprY+JiWm3vbi42G4/0Q/797///b59+1r+32azZWVl9WpRexXtrRXNaeU1Zd98udRRW6izJA4dt8BvSpM9H/feTgusmgKrJiDKDEXYNpNiFVjVO2u9aUZL67ZjDi4gyefew/6ogxscpWnb+KZlqMFR2qMOLqE3u++3pWVoj9A+nj2CrOu8Ra7Iwc1JPeXu7NBo7eYqT0CUOzYklrv5LVWeOp9ICInVMVOTDGnn0EbqE2QtS6lOnbLMqKIFmQiSzHacygyUrMTFb6nyNPpFQki8np2WZGiZJCEyRW7JQsbpdN43l8Rb2m9/cjU57QKY48aNU0Un0HT7ppvqwp8EnqMUO/9PXFxa6Y53Pnp4lG3UrzKHX2xvqvzq9cXElGyNy+7tXXc8BY+M0VW4hbeONA+L1mgZutzNFzm5BZlBmF0pIEpRmvaHvYGlmgKhm5Q5xcA2BcRqr9CaxzIh39f5cq2nr82LsizJ7T8lmiIahuoYYMVO7vMy98wUY45FTQgpcnJrS1yzU0/82QN6lvILMieeMo++i5fUNNXj9PL7vbt2ftpQc8wakzp6zEUWS3TP3geC60hzYFOlZ1aqMdOklgk52hxYddx5cYbpDLdmwwsB1m2CINzx2mqtqX3i/XF0vCzLPQ6wsN8Dy8sfu7v2YOL8fwjDFh/0i4YUOn/AvCP/GGUeMiv0haEpcmmmqdjJFTt5vyjE6thpybagdDS3aZhaX/vWwhqvGLLqFyGEpak5qcYVRc4RMdoUA+sV5R/q/RqGGhFz+p6QDEUZVFSDX2x7X9DDS5wod5z0ckuV96J0U+bJBeFyLWoNY/qy3N3jAGNpKtei3lbtnZ5iaPkCJJlsrvT0eMKXffu2bHr9Bk1cgTlpYH3xd/tW3Dfy6hemTVvUs3eDINpc5Z2fYW6tchXYNAxNba3ynHvv316CAIsUYe/H8dPBbeq08bWb/6E/tjU1Y6THXlm473+mgRc3Vx8KV5GyzOqsYHfXHmTT7KhtPurgck+e0AsdXLGLuyA5pP0RcizqxfnW7+t9u+v9OoYaFattt8ZKOyNjdOvL3ZdlmnQsTQgJiPLn5e5h0dp2VSBeku0Bsd1ypmlGlYeX/KKsZShOlAkh3e09ODPV+HGx850jzTlmtURIYXMgSsP0rNuLw9n01b+vnHzLO2PHzmnZcuTo3k//Nis9Y2hW5qAevCEEi0eQOFFu12CYbVavPu6S5NOsehEJIjjApIDLxWvNRlVEfnB9j8dRb04aPOGeTzdv+rC89ohszFBd/6XgLOF2vOTmpUie3r5bdCy9MNO8rsy1o9YbpWGaAiInypdnmUO/fJpZTXc9NcfF6wKi9Pqh5lQjSxGq3MPnW9RTkk7/8tP+S0pc3Dc1PicnEkLMamZakr7rFwdahrom11Ls5Kq8AkvI7FRjak/blL79eqUpa3JrehFC8nNH7Jl4047N72Rl/q1n7wm9J8LPvp0GmNS4c+nj/1j1Q6VLOLVaoJn+5PonLgjyZbH36NpXXl21o7BBnXvRnQ/eOvDYczdc/5dPi1yyOm7Egt//819LzouO8A9S+aLiMou+/3hrHRk++YZddf48m8rul+qLvmCjMt463Hx1ruXc+69HiCQDe3OBrdLDOzhpWDSdbFBF5tVlWxQhU5IMI2N1VR5BJvK0ZP1pZw5T0ZRVw5S4+LaVsHI3r6KpzZXeOWnGlu0lLv7zMtf0ZGNeJ3fdTisoFWJHQ6klqaDdxviUQcf2rj3Hd1YiSSYHGv0VHl6USZKeHR6jDeOxaGBpFU21vTVLCCl2cXE6JmJ/IJ0EmFy/4rfz71pnnrpwznlx2lMKryqICfK1uFT69qLzb/5cyhs/MpVb9cCc3dtn1q0+kHPb3x4YpK3Z8f7LD86/1vj9utsy+0gVIFLlDZu58/3fpZZ/cpS9ZFaqYYBNU1J2eOWOlwbe9FFykn5jhfvqnA4dXRSLpkiqUZV6zu9T6uLr/YKGodOMqnOf5uqsTCo6/2yRMzVJv67UNTvVmG1WE4ocd3JflLsJIfPSf6k2ZZhU89JNX5S7uxVgQaE3x9aX7m+30d5YrjPFh7gkYecVpA+POc1qusCqYShyzMH951DzohxzGFckmJakX33cNSfNmG5SEZkcdXDrK9zz0k3hKs9ZdRJg/A9fbeHnvPT1J4sTez16xYNvv/Cl4aZVu5bOjaWJa+OdI2YvVd+3ffXT4zSEkJtvnG4bPf2V936++eHBZ/taa2tr16xZ09mjPM/zfNDmhA26sHfiqOWZxGs/OPTRjaLu9e8yR25rrnYf25Iy94lA3LDBUZoNFZ6IbQcPCxcvrT7uEmU5xaAKiMLmSs+YOO2E+PBPaZ9tVs/LMG2u9KwpcVGERGmZmSnGtSWudo1+qUaVm5dO2wu/V42ecOkHnz1ZWn4kMSGzuuZ4fGyqz+8t2/b69Nv/e/YX9y1bqrxZ5l+Ghw+wafbU+74oD+eVYoFNo2aoTZWe5oAoExKrYy7JMPW4uTgEOgkwrqnRE1OQHxOKQ1soLjxuueDpmbE0IYSYzps53vSePH7YyTva+tHTJ5peLywWyFkDrLGxcc+ePZ09KkkSx4ViycSu6DiZb9jxEolJHXLxM/v/8/kqI1+anDV6+K3/crBR39f7GYqiCRFkWa3YQQJBt/q4K8einhB/ot+gR5A+POqwqZmCM/bFCI10o2pxvlWQZEIIS1OcJMuEyKfez5AJkWQS+u8zKSEjd+6fVvxpOJFEojYSzk1oJn7CrcOGTQ51UcLtSHPg9oG2tltGxOi2VXtDf1XRVktDMS/JFCGRP8ivkwDTj591nv25tzbcP+6iuN5uF6HMFpO3uLxRJokUIUQ9/PonnxCHtma+7Kqp8RrSDF34JAcOHLh06dLOHn3//fcNBsx806lYLbOvwW9KNapyZ03NtURpGELIwSpvrJap9Ql6llZH/NEcMg1+0cNLrelFCDGw9OQkw556XyQEWIvWs4+apmwapsTJZba5g3XcyUVpmNB/p5IsFW9/i7Ba4ncQfzMhMmE1dXtXuj1PGA3mEBcmjCSZ8JLcbmYZmiI6lvYJkoYJ8/1mBdwWJoR03olDNfjSRTGPLBi8Z+4Vs0ekR+varF+bf/Gd8/KC+PGqx8yfY53/yHUP6R664vwRQzPSZ/32zpOP8fXfvXX3kxv0Uz8Y1Wu1WJ/Pt+FHoutwL4DrzamFOla/wt6NPtWoUtPU5krP2Fjt2hLXJZmmWq9woNE/P9O0rtQ9Pj6kk7VHOAcnRnXo0hKjZRwdpr+KENOSDetKXdOTDS2DBwod3KZKT1jubXy7fY1Qe5CyZMx/cEtuzrDqmrLlL1zBl3//2ap/XHntn0NfnnChKWJU0e0ms/aLsk/oOz1+Q6Cze2D7lz23ulwbq6nb/cm7u095SDuv4NdBDTBimv235U81LX78hun/mPry8a9+k9Rybpfr37ks5+ZPXPrBt7799CXWXrsg4Hl+/+lWk1dH8BCDXrIwy7ShwvNjE69lqKU/N7MUMavoNcdd5yfoh8dE7kofoadnaTffYapGXtKHvC9+F2WaVAsyzVuqPJ+VuSlCEvXsgkxzUjimCNq7/kVC6CUvHqEZhhCSmJB299O7/n5zVMW3b5H+FGCEkBEx2g0V7ssyzS0NhoIkbyh3D7RpIr/hLnJ0cgRr5r5ytPyVUBWCsk2473+H76g9erjJ/MttN0o/7FdPvXH7pItmDI2N3JuIfYqOpednmDyC1OgXdQzFy4QmJEbL4BfVToKO5SS5yMm1Losly2Rnra9lWc7IlGRgr8m1yDIhJAy3vlpx9jLKEFNSdnjbZy85qw/qY7LHXHCLKr6Ar/4xbGUKk/Fxeg/vef2QPdOkoimqxMUlG1TTQzugXum6dwkmN2z+1yuNc/54eW7wW2gpXXzeiFN60hqGX3HH8KDvpwOtVjtzCDF3aCH78kDv7zsiGVjaYEQjxplQFLk43bTquDPHok4zqvyivL/Bb1HTo2IjvaE17L1wWH0UX73/48dGEyLTxoTmih827P2IUBSljty+2r2EosiMFMOoWG2FR5BkeXScrs8MtQyZzgNMavh+xbvr9la7f1mSQfYcXvfu9jGZD/RGgIWNWq0elnaayXw3HgxHaUAhkg3sLQNs+xr8xxyclqEmJep7PNlg/+HmJTZrOl+5h7A6wqgkRzmlNcsUQ7yNmrTx4S5deNg0jC18A7+UrrMA4/c+OXvK40VxgzKk4h/rzYOGpmrdZT8dcqQvfPrOKX3ud9rsJSxDCCGc8MutL0UshwNhpGUo9G3pOjcvvXOkWXbXEIoinIswGkIomfcRkSOESNyZ1jZTIjcvCbJsPd2EKRAsnQSYsPejZYcGPrr7mz8Nan5z3qCPr1i7dnGsd/8z867YE5ViDHcrRHANHjx403cnlk2pqKhISEhgWZYQkpSuNpn6XbMGQC/ZWesbFKXZW7WHEMIkjRSr9xNZIiLPxA4Q6w9z9UfDXcCgKXJyX1V4eElmKYqT5MmJ6APVWzoJMLGmstY6anyemlBRw4Yn/HX/EYHEGobd9cCkvD+98fNVZ58UQ0E+/PDD1v/Pzc39/PPPc3JywlgegD6pwsPPTjXu8doJpVry7B5CiN1et75RMyZOt+reJJnzhLuAwVHk5NaXu+eln5jAosEvflrq4iR5bBwq68HXyb162mI1uysrnTIhTEZuWt2BA3USIUQdG28+fqQodGuvh86iRYuys7PLysqmT59eUFDgcrnCXSKAPqVlKjLGEENkftPGZYQQmy2OpqjiQ9/KPjul6iPn929qvLPazNYfo2UuzTTtqPXilkRv6KQGpho5d5blkj9edY/mn09dPmZCzp2vPL12+u/zf3pj9fH4S5L6UPWr1SuvvNLU1MRxnFqt1ul0aDwEBZFk8n2970hzwMPLUVpmdKw26OuonbtEPXvcyZszxjXVH/7hv7fs//QpY9poR9Wh0rofCaNWxQ8OdwGDo9Yrtlu/2KpmdAzdzIlR6KwRbJ31ljbNfvr9v0xsWr1it4sZeOsjV/tfv6wgdcDlb/kXPnrHqL44wtdqtWZlZRUUFGRlZSUmJoa+AGGfzBe6ziNI+xr826q9Bxr9fjHMX5wok/ePOsrd/LQkw9W55mHRmq8qPNtrvOEtVUfj43Xf1fmyZt5DZFkVN1ASA44D/yPeenXySCIJEy59KNwFDA6GIh2PCEGWMZayN3SaRVT0pAdW7H2g5Y9LXt9XePu3PzltA8eOSjf18y9CkqSfNn+u1nWYd7w757EInMwXuugne2BThSfboraq6RIXv63aOyfNmB2+Gs+BRr+GoRZmnZhI0Kpm0oyq1w81D4nSnHbNsHCxaZircswbKxgy/Ul+y6OyJFBqE3FVcu7auPN/O2bM7HAXMDhSjapD9kDbXhuVHp4mFLoj9oazVKYER8n+PQdLGv2sJWXguEm5URHXLhF6PM+vfOyujtupbo4RRZVLiRr94sYKz69yLa1T2FV7hRVFzpsLrIYwTWFX4uIHR50yA4iOpbPMqlIXPzS6qyfNer9Y5OA8ghStZQbaNL00yW+cjl2Ua+Gz7zs+9ZLvN77RXH3QHJc7asq1A/JH98buwmJqkv6DY05OklsmhTrm4LZUeS5MNYa7XH1T5wHGl3zyp1vu+temMt+J8yylTZr0m3+99cyCLEzsBP3Vz/bAsGhN2wlYE/VsjkV9xMGNDFNXaUGS1R1W31AzVIfJGjvV0hY6OEpjVtGlLv6bau8lmaYUQ2/9zlU0lZeRl3fzM730/uEVq2Ovz7Nsq/burvMJkpxkUC3MMrdd4xiCqLOPNfDd45dc+aJj+v3v/feqiQOTTYGaw98uf+YPf73mMv22XU+M7c+DGmiaHjhtDsO2/3kf/Ko/rone37h4KaXDHLhRGsbJiWEpDyEkVseUu/l2bZgVbmFA1yZmPOrgCpsDtwywaU+mYLGTW3PcdftAG+bA7BmrhpmfgV5godDZbPS73nv3yIA/7Fz12PATPwvbeVc+unIAPXHsG8t2Pzp2cj+uhKlUqise+5fW1H7uqT+O7ndrovdDBpbuuGaKgztlUYwQGxmje+dIc4KObVmKTJDkLVVeDUN1cSHdn+yB8fF6bZs6XJZZHaVlytx8BHZlBGirsxWZqyvrzaPGDzz1+FXnT+wH58QAACAASURBVBhte97uiNA1j0DBfIJc6eF9ohyrZRIiuL1lgE29vMiZZGCPO3kHJ9o0TJpRdaSZW5xvDVeRzGr6imzzF+XuzVVek5pu8AnZFvWCzK7WADy8ZFG3v3tnVTOurjdBAoRJZ+PAktMSnJ+v3+W6cFKb34Hn+/XbfaPvGt5L1S/RXV3RrIlPitKe8nOS/Y56J7HEWSJ3pQo4N/sa/F9XexMNrI6hdtQIFjU9L90Urj4RZxanY+O17MoiZ6pRFaNlKj3Cd3W+QVEac4cMCKUEPbs439ocEN28FK1ldGw3CmNU0c2clHrqxuaAmG9F9QsiXScBph71m8cXfXb7pVPq7r5jwbjceJ2v9uju1f9+8WNy9atD6vbuqSOEUMaUQfnxQUkVrvh/D95879KvyzwiMWTOvPP51/5ySfrJmGz+6Nq0X+vea15+ORKsLyp2cjvrfNflWawahhAiy+TrGu+aEtc1uR0WCOjAyUk767w1XkFNU2lG1Zg4XW8vhV7pERoD4uJ8a7GLd3BinlU9M9Ww4pizKRD+YapWDWPtfhkGR2k2VXpyzGrdydU4jzk4Oyemna4FUpLJ/kb/UQfnE6QYLTMmThen60l1udTN723wNwdEs4oeGKWJ5HXUIJJ1cvAF1j10y9tH/IQse/SWZac88up1573a8n+qCc/+vP2+nHO/8OR+eGrhtS83TrrjxWcmmMq/Wf76S1de0LBy5+sXx+IWcj+wt8E/OVHfeualKDIpUb/0Z3uDX4w5442lMje/+rhrVKx2RrKRk+SfmvxvHm6+Ls+i7079o7sKmwNDozXxeja+TTvnAJvmmINT6GR32WZ1tVf4z2H7IJvGoKKrPUKVl780w9yxB4cgyR8WOdU0NSpWq2fpKg+/vMg5JckwJKp78fN1tfdne2Bigj5WyzRz4s5aX2EzF4G9HjhJFiS5Vw8nOEed1cDm/OvnY389cxs4pY1JDcZXy3+/7L1Defd988kTo7SEkMuvvW7qzeOv+M298ya8c2lMtyKstLS07cy87ffD836//5yLC0FmD4jtruIpQuK0TFPgLAH2eZl7foYpw3SiopBhUm2p8myr9vbqmBufKHfsr2FU0V5BwXeMzk/QD7Bqjjk5Ly9lW1Rz042nrcj+0ODXM9SCkyOmE/Vspln9XqEjz6LWdOjH35kGv7i/0d/a6TFBz+ZaNP890nzMwUXOgmqVHv6rCk+jX2RpiqHIpET90Oj+3PM6cnUSYJQhPjM7RJ3qpPrahqgJU4ecPEDo+PnPPr9oxFW/f/LmGc9P6c65SBRFu93e2aOyLMsyxg5HHC1DewTJJjMNfsEvyDE6xsDSHkHSnfGc2BQQZUJa06vF8Bjt+0cd3dq7T5Dq/aKapmK0TFd6jVvVdL2/fY/5Op+QblJ2x9xoLROtPUsNstjZvpYZpWES9Gy5m+969hQ7uQKrpm2nR4YiQ6M1Rc5ICbBKj7DquGtmirHlLmCtT/is1O0T5XHKrGH3bacLMF/J5g9W7Eu48u6L0mlCfIc/eujep1f/5E8aMfOa+x/+9cTYINeomcSUBPv67T8HZgw/0RJBxcx/+h8Lxtzw6z/O/fbFGV1/p6ysrKeffrqzR19++WWdDodgxMm1qrdWejyCrGUoLUvV+cQUA+vgpKQOw63a4kRZ2yHhdAwd6PLMhIIkb67y/mz3x2pZTpI9vHRBsmGA7SytYYOitO8cac63qlvH+RY5uVI3P6sfTLXASbKWaf/r1zJUoDsTrXPSab44LUtzYqQscrG9xjstydDahyVexy7MMr952D4qRouBcZGm/eEo2zf+ftLwGbc8/Pr2GokQ4tny4PzrXvxWGDx1tKXog9/NuvCxXb4gF4EdsejawYefvmT+fa/8b9O+Kj8hhNCJV/zr1ev4166cfcdrm4tdqDf1YUl6ttYnaBhqbLxuXJxuSJT2uJO3qmnmjFNzWTWMPSC2i6sqD3/mVse21ld4vIJ0+8Coa3Iti/OtC7PMW6o8x138mV9lUdMXpxs/KXEtL3JuqPB8cNTxVYVnQaa540m574nWMFXeUz4fmZBqr9D1z/zkm7TPqiqPEKOLlKkCqz18lvmU+rRZTZvVTGMgbGPVoTPtAozb8eRvXqiZ+s89tQefGscS2f7py+8cT71t+fY1b7+77vuvnxpx+F9/X2MPcp6ohv3+fx8tyS989c4rLrztvyUtRwkVM+/fW1bealj7u8sf38oFd4cQSfbU+2emGMfF64oc3Pf1fpqSbxloa+Yk+xnPF1qGGmDTfF7mbs2wpoC4ocIzpmvtPF5BKmwOXJRmbA2eBD07Lcmwu/bsk7hnmtW3DrCNjNFGaehx8bpbBliTz1hZ7DNGxep21voqPSfiR5TlTZUes5qO705HxFyL2h4Q99T7Ws8iR5q5w/bAkKhIuclEU1THKqUky+jLEYFOPfKEA5+uqxjxf5/+eoSFJoQQ/7dfbvXk3XbrNAtFCFHnXXXV2Ief/faAcPWU4Db4q9Iu/uv6eY81lZfYtRmtF2JsyrxnNs++7+DWzbuPGYf2i1NEf9ToF85L1MdqmUFtmu8S9WyDX7SdsVP4jGTDpirP0p/tCXqWl+QmvzgpUd/FDtmNfjFWx7brqpBiVG2s7NK6wCqaipAbNqGUoGfnpBk/KXHqWdqgomu8QopBdWk3ew+yNHVltvnzMvd39f5YLdPMSYSQy7PNxtAO+5MJ8fLSaccaphjZdjNbNvpFryBHa3EOijinfiViVXmNfsDgkxnC79u8zZF40czBJ55FxyTE0vZGe+/0t6I0UWn5Ue23qmIHz7hycDduhIHCqBgqIEqEnJJVAVE+64gulqZmpRgnxkt1PkFFU/E6tuOctp3ulKY63i3zd2Gn/Vy2WX3rAFu9X/TwUkwK07MlQmwa5ppcS4NftAdEs5qO07LdXMjhnPhFeWuV56emgIqheFEeEq2dlHjKTFqTEg0fHHXIMhkUpVHRpMTFbyj3TEvW49CIQKcGGKXRqgN2F0eImhAiFm7cVG6cNG3UyUtN2dHYLBmz+/uCYBBUmSb1gcZA27nPG/xig1/sYrucUUUbVd2uDMXpWK8gVXuFttOE72/0Y/Y/Qogoy/sbAlVeniJUspEdGqVte+5maSooc6vHaJlu3TwLCkkmHxx1pBhVvx0cpWUovyhvqfJ8dMxxXZ619d8Yq2WuzbNsqfRsq/ZIMonVMbNTDZk4MCLSqQeiaujYEeQP7y0vu/ymNMa/570PD6om/N95hhOPimVrVu1iRlw9RNkdhiGyjI3TLTvqWFPiGhmj1bF0uZv/psY7PdkQ3MpQQJT3NvhrfYKGptJNqgE2zexU48oi54QEXbpRFZDkA42Bcjd/fd7Zp//o25yc9OExR5yOzbGoZSIfaeb21PsX5Zhbx/MWO7mjDs4nyDFaZkSMNjJn/OrMoeaAnqVmppw4p2kZ6sJU4/tHHYWOQNvG5ygN0zLcTZZJKGuH0F3tamAJV957w7Pzfjth4tqpyZWbPj0UdcWfL46liGQ/+s2m1f9+5PFNUdeuXYgJMr5a+iyr7nCvBYPMekTDUNfnWb6v92+r9voEKU7HXpFt7la/gLOq8Qori505ZnWeRR2Q5D31/n0N/suzzdfkWnbUevc3BjQ0lWFW3VRgRRPi+gr30Gjt+PgTfWGGRGm3Vnk2VnouTjfJMllb6qr3iyNitHqWqvIIbxxunpduVFC1tdojdCxtllld7RFOe/cU6RXh2p0mKMvM59Z/nPiXV9btOhQY8Kvnnvj7gmiKEO+Xf5h1zVrTqBteW/H8bEs//06ff/752tralv/ftWvX2LFjW9Zivvrf/6ZpJV2NRg6WpsbH61pPmkH3aalrRoqh9Qw1PFq7psS1s9Y3KVE/Lz3iZjAKI16Sy1x8u34Z4+L1/z7YRNLJgSa/i5duzD/R2lZg1eRbNauOO28faFNK8FMU6XiZKSOoFKvjda46Y+5Db8x96JRtupnP7Dz8Sl5unA7fM1m8eHHr/+v1+mXLlmF8dCRr9IuCTNpdX4+P160tcU1K1IerVJEpIMpqhmo3XLelgwMnyYXN3Jg4HSfJ5W7eJ0gxWjbZwEZrmAo3r5RbRMkGdk+9v+18IjIhRx2BifE4EhSpaw01dHTOsOheLglAr/AJkrHDfKwmFe0V0OTbnp6leUn2ClLbGWwdnKiiKTVNeQWpzid8WeZONLB6lv6uzqdlaR1LKeiTzLdo9tT7PylxTU7SW9VMc0DcUuVV98tBEX0DRjZAH2fRME0BUZJJ23pFvU+0atDe2x5NkcFRmg0VnrlpxpZ6GC/JGyo8w2K0hBA1Q+2p912XZ21ZOEYmZFetb3uNd2ycYqovFEWuzDbvqPUtK3R4BcmgoodGacfH9/1pwPoqBBj0cSYVnahnPytz+QS5ZcRYipGt8QpdnLMjKEpd/I5ab71f1DFUtlk9MUHf9enbQ2xakuHzMvfrh5ozzSoikyInl21Wn5+gJy1d8gjVWnCKEJYmRCYR+i/phIqmJifqJyfqRZlE6pcAXYUAOydvvfUWboBFvlyL+qsKt46l000qTpIP2TmKkHRjiFqN9tT7vqv3T0vSpxhUPlH+od739pHmG/KtkTl9IktTF2eY6nxClUegKDImTte6fAwvyUOjte8ccaSZVEaWrvDwFCGZZlVzoKuD9iJKRH780D3KO+wiylVXXRXuIsBZSDLZVu29Pt/KS6TWJ6hpanqy8ccm/zc13jlpvd525Bflr6u9NxXYzGqaEGJQkVmpxg0V7p213qlJhrO+PFzidGzHpZZ1LJ1sYMfEWsvcvEeQcq2GNKPqo2OO1qWcz4qX5N11viIn5xfkWB0zPl4flDHR0G/h6IE+rs4nGFV0y+m4taIw2Kb58JgzBHuv8vAJerYlvVoNsmk3VLhDsPfgyrOov6/3XZ1taV10psYr1PvFVGOXpjbwi/J7hc2JenZGslHLUhVu/uNiZ3fXivQKUqVHCIhynI7pGLHQ3+AIOCdLly697bbbKIwiiWCiTDouCs/SlBCSgeeiTDqOkWJp0uVlyyLI0GhtsYt7p7B5ZIzWoKIr3ML+Rv9Faadfvrmj3XW+FIPqwpO13igNk2JUvVvYXGDTqLv2Dnvqfd/U+JINrIahttcIMVpmbppR1/HbhX4D3/05WbJkid/vD3cp4ExitEyjX/Sfmhilbj401+9xOqbKIwinrs9R6uLjImb5q66jKbIg0zwhXl/hEX6o98tEXpxv7XoH9BIXNzj6lNF4URomTstWebq0lGVhM/dDg/+GfOvCLPO8dNNtA2xRGmZtqfIqshBECDDo4zQMNSxau/q408WfWEah1MVvrvRM7IWJPySZuPlTwsqiZjLNqrWlLq9wYu9HHdzOWt84xY6czbeqL0ozXpFtnppkaNc0ema8RDQdalpqhuK7tqDzDw2+KUkGy8k90hSZmmSo8wnNHNaZ7L/QhAh939Qkw85a7xuH7CYVzUmEocjcdFNK1+7cdJFHkLZUeg43cxqG4kR5WIz2/JN95S9MNW6v8S792W5RMz5B0rH0ZVmm2JBPxB52MVqmwiPEtqn4irJc7RWmJ3fpo7AHpHbVVpoisTrWHpB6tqoL9AGREGDi8U/+9urXTWdaZUw17LrHr8WaltAzNEUmJujHxeubAqKaJpZgn+94SV5W6Mi3qv9vSJSKpnyCvLnKs7LYeU2uhSKEpampSYbzE/RNAVHH0iZFTd8eROPidCuLndFaJs2oIoQERHl9hTvVqLKecdnSVlqG8vKy9dQGSw8v6dAdvh+LhEygtAa2bscH739byWtj05MtpymTJjD9sWuHnu2NCgsL33zzzc4e5TjO4+nSertdh3FgCsJQpJfqPQebArE6dsrJbvE6lroozfjOkebjTq517nOWpvp5r7kEPTs33fR5mZumiI6lG3zCQJtmWnJXxxLkWdXf1fsuMfwy0XCJiw+IctuFCxr8ooMTLeowrDQGYREJvyg6cfr9b01dOHLW0PvFh7duuiutp1eoRqMxOjpakk5fl7PZbHl5eT0v5ulgHBgQQqq9Qpa5fYNkllld5T3N4h39WaZJdesAW1NA9AtSjI7t1lDusXG65UXOD445hkVrtQxV6uJ/bApcmmlq6QLc6Bc/K3N7BSlKw9gDooahLkozxvbvK4b+IGK+YCZjwcJxf1x+Tu+RlJR0//33d/bo6tWrbTbbOe0A4HTo0y0GJ8lEhcatDmiKxGgZQrpdQ1LR1DW5loNNgaMOLiDK8Trm5gJry3KanCh/VOQ4L0E/7OSQsh+bAsuLnDcPsEXmdCcQLJHTHE8nzv/zfx6cGR05JeqCpUuXyljHst9LMaqONAfabpFkctQRCG4/kfDyCFLXegv2IoqQIVGaSzJMV2abpyQZWheDPtQcSNKrhrUZED0kSpNmUv3cFOjknaCPiJgaGCF0yvlXpYS7EN20ZMmS66+/HrfB+rmBNs2eet+6Utd5iXqrmmnwi5srPVGaE70VFE2Q5B21vj31PoaiOEnOtainJRsirR9Ko19M6jAZY7JB1eDr0ggzUK4ICjAAhaIpsijX8m2N9/2jDjcvmdXMiBjt6Ni+cFmzpsTFUNTNA2wmFd0yk+F7hY4bCyJrJmIVTfk7TG3iFyR1JBUSegMCDCAI1DQ1NckwNckgy31nffoqj9DoF28ZYGsZf6yiqfMS9A5O2tfgH98Lw8B7LNus/qTUNT5e1zolFS/JB5sCIZisGcILAdZDzz33XF1dHc/zDz/8sM1me+ihh8JdIogIfSa9CCHVXj7D3H6mw2yz+ie7n5AICrAkA5ttVr17pHligj5GyzQGxG9rfGkmVRdnGQblQoD1kNVq5Xn+yiuvjI2NNZlMZ38BgNJQhOrYRUkmMhV5a1jOTDEec3AHGv12TrKq6fMT9XldnqQRlAsB1kM33XRTuIsA0LuSjeyuOp8oy0ybemVhM5cSkctX5ljUXZ9ZGPqGyOpNBACRI17HphrZFUXOep9ACPHw0lcVnlqfMDymGyt4AfSeSLyS6iWiKJaUlFgslhDvV5KkQ4cO9eCFsizLstyz1wIERTYhoixXFpEKQihC4iiSRFPHjtSEu1wQQXieD9eu+1GAMQyTlpYW9NmkzkySpKNHj3bcaVRUVMcnNzU1tf0zEAhUVlZmZWX1YvkAukaUCTqlw2mpVGHrLNOPAowQQtM0w4R0ls+WxZo77tTpPM169u2eRtM0RVEhLjDAaeEohAiEe2AAAKBI/asGxvM8x3Gh3GPL1Pgdd5qScppZs9o9jed5WZZDXGAAgG4J43yw/SjAJEmqrq4uKSkJ/X477nTjxo0dn9nuabIsC4IQ+gIDAHSdIIRtzsl+FGA0TYelE8fhw4d7tlO/319RUZGTkxP0UvVbdru9uLg43KWAE0wmU4h/j9Ab0IkDztUXX3xx6NChyZMnt/yZk5PTMmDA4/EcPny49Wn9fPsjjzyyY99BvSVSloVz1tcIXIAQYo6NZ9X9bnBVdeHBTRs3jh492mAwEEIcDsexY8daHy0oKMD2cG3ftm1bQUHBnDlzSGRDgPURK1as+Oyzz5YtW9by55IlS371q18RQrZs2fLoo4+2Pq2fbz9+/HjBzMtGzV/Upc+09338l3uqDh8ghMy5+5GkgqHhLk6ovXLDnLvvvvvJJ5+cO3cuIWTdunXPPfdc66N//vOfsT1c2ysrK+fMmRP5AUb1n/UYJ0yY8Pzzz48fPz6UO21pQhw4cGAPXtutJsTGxkae5xMSEnqwo/7j+eefb834SOD1elvuHxgMhn44XsJkMn3xxRcajSbcBYH2ampqVCpVdHR0V548bNiwd999d+jQMFyBoQbWR3TxUOvnlixZsmTJknCXAiDSKeVSGOPAAABAkRBgAACgSAgwAABQpP51D2zNmjUHDhwI5R5lWa6pqUlMTOzBa3met9vtcXFxQS9VhFuzZo3NZqP60trGEcPlchFCsARrb5Bl2W63X3LJJeEuSKg1NjaGa9f9KMBuvvnm7777rt2M7yHg9/urqqp68MKWeaTKy8uDXqQIt379+qysrH7YKy8E6uvrCSGxsbHhLkgfJIpicXHxaWeJ69sWLlyYkZERll33o270oBTR0dFHjx497YozcI4ee+yx1v9CcNnt9uzs7NBfIvdnuAcGAACKhAADAABFQoABAIAiIcAAAECREGAAAKBICDAAAFAkBBgAAChSPxrIDEqhUqlYFkdmrwjj4rl9Hsuy+HhDDAOZIeI0NjZidZhe4vP5CCE6nS7cBembcOiGGAIMAAAUCffAAABAkRBgAACgSAgwAABQJAQYAAAoEgIMAAAUCQEGAACKhAADAABFQoABAIAiIcAAAECREGAAAKBICDAAAFAkBBhECt+qayw01QYTf+uXXLhLpXxy3cYXn/6kRDplm+vAO0vmj8uOiUoZNv3Gf2yrkzp7NZzJaT5bHMahhEUrIEJI1cdLucSL/vin+WknLqsoXcFAHKDnij/y9iMPvTFx3L3zM05ersq1K26dfcv6jFsef+k+3Y9vPf7gvMupXZvuGYAPu5tO89niMA4pfLAQIcTS4jJq0PV3/fr2BCrcZekThMqdH3+6aevK19/c4U+Z2OYB8fAbz67yznrlfy/flESTqy/MaB4+57kXN//m1ZlYZKWLOv1scRiHFpoQITLIzpLjjebMrCj87IPE9+Oqpe99fsBnjdWd8plKNZs3/siet3BeYsuv33T+wgvj6zd/dUAISzEVqbPPFodxiCHAIDKIZcWlkrbkvatHp0eZojNHzLzl+a/rxHCXSslMFz6z8euvv97y+rXpTNvtYnFhsRRfkGc7eY5V5RZkk/KjRd4wFFKhOvtscRiHGAIMIgNfWlzOVewtSb/txWXv//OO0Y5V91940RPf+8Ndrr5HdjqclNVmbf3t05YoKy05HW6sbXvOcBiHFu6BQWSght72xorbRl40JUtHCJkz76J8YeiCfz7/+T3LLjOFu2x9S0tMUe02yaIkye02Q7fhMA4t1MAgMmizply+sOVnTwghlG3m5TMsroP7i3FnJshoi9VMHHZHa99vydHskGizxYSzwTnDYRxaOGQhIvD1hXv3Fje3acSiWZah1Bo16gRBxmblZVO1R4ucJz9s4fjR4yQlL0cf1mL1CTiMQwwBBhFB2vfc3HEzHtroPrnBuW3NxubECROzmDO9DLqPip82Y6jwzdoNjS3nWf+eT9dXx1wwc5gqzAXrA3AYhxjugUFE0Ey5/Y4Ry/584wLzY3dNS/IXfvHSk294Zr+yZJIm3CXre5j8m35/+UuL7rn+L6o/XKDdv/S+f1WPf+p3F2AQ2LnDYRxiqIFBZFCPePDTL5+bK3/yp2suvfp3r+yJveG/Wz68KRMHaC+g4ha8su7VS7zv3nbhjEXPHh7+2OoVd2MajqDAYRxalCyj7ywAACgPrgwAAECREGAAAKBICDAAAFAkBBgAACgSAgwAABQJAQYAAIqEAAMAAEVCgAEAgCIhwAAAQJEQYAAAoEgIMAAAUCQEGAAAKBICDAAAFAkBBgAAioQAAwAARUKAAQCAIiHAAABAkRBgAACgSAgwAABQJAQYAAAoEgIMAAAUCQEGAACKhAADAABFQoABAIAiIcAAAECREGAAvUM8+ux4NW2Y8PRBru1mufrVGVrD1f8LnMNb81vuSmOpk2hWZ0sdftGd//62TjzHMgMoChvuAgD0ZbJ311/vfu3y9XfmMEF+ZyrmosdfvmkgQ4jEOSsPfPHOa/93wYZ9y7cunZ9ABXlXABEKAQbQeyh93oDo7Y/d899LVt+Y2tPmDlkSZZpp/2pKnz35soVTVC1/Lbrx19c+P/f8B+58dOEFS2cbz6XMAIqBJkSAXqSd8OBzN5i+eOiBlbXy6Z/hP7bqj5eNTLXqdObkoXOXvHfQ3bKd/+aebOOClz/78+wMk1qltaWPvfpvW+ukzvc06I4nbsmsXvH2l85e+HcARCIEGEBvomwXPvHs5eR/Dzz0WVPHCJMb1t4x86p/Hsn/7cvLV7x27+jqNxfPWLys8mRMcZsfuXVl7N3vb97+6T8uEtf9cdFDG7yd70o9cvpkm/v7nT/zvfNPAYg0aEIE6F103IK//XXOiNvuefzayS9MNbV9SCx65+ll9ec9e+C9u7IYQsjcKdGNA2/92+s/XvXYMEIIkXxZd3z49j2DWEImD3/865ULDx+qk2ZndHbZySQmJ5C6mjNU0wD6FNTAAHobnXLtc49Nql1699O7fac84Nn9zT4yZuGlGSd6eFCJl14xmT2yY3djS2WNyZwxM//ERSYbHWOlZfnM4USh/wb0JwgwgN7HZN/24oMjil74vxd+bNN9XnLam0UmLiHml5+hLjHJRuyNzS05RdtiorrxExVrKmtIXEIcftXQT+BQBwgF1aC7/3lP3v6n715aKJzcRpttFkasr238pVrF2e1uYraaTvwwu1Wh4vdv2W43jh43UBWkMgNEOAQYQGhoRz/wz98kffPne98tPzne2DBm4lCy++M1ZScSTK5d/f4GX86EsXHdbwrkCl975LVjCZcvnm0OXpkBIho6cQCEivH8R55fvGrea4WSfgQhhBAmZ/Efrn7pij/MX+z70zVDmMKPn350rerit24fzhJytq6Esq/4m7VrmhlCZN5d9eOXb7/ywYGYxcsfn2U6ywsB+gwEGEDIUNbZf/n7VZ9e+UHzyQ2xl7y64f2EB5584ZZL62Vb3qTFb731l2tTutIwIteve2jhOkIIoWiNOTF/whUvbHzk1+fFoxsH9B+ULHcyvhIAACCC4R4YAAAoEgIMAAAUCQEGAACKhAADAABFQoABAIAiIcAAAECREGAAAKBICDAAAFAkBBgAACgSAgwAABQJAQYAAIqEAAMAAEVCgAEAgCIhwAAAQJEQYAAAoEgIMAAAUCQEGAAAKBICDAAAFAkB6HEdFgAAAChJREFUBgAAioQAAwAARUKAAQCAIiHAAABAkRBgAACgSAgwAABQpP8Hgq+m0VSnmiEAAAAASUVORK5CYII=" style="display: block; margin: auto;" /></p>
+</div>
+<div id="function-marginmatrix" class="section level2">
+<h2>Function marginmatrix()</h2>
+<p>The <code>marginmatrix()</code> function creates a scatterplot matrix
+with information about missing/imputed values in the plot margins of
+each panel.</p>
+<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="do">## for missing values</span></span>
+<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>x <span class="ot">&lt;-</span> sleep[, <span class="dv">2</span><span class="sc">:</span><span class="dv">4</span>]</span>
+<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a>x[, <span class="dv">1</span>] <span class="ot">&lt;-</span> <span class="fu">log10</span>(x[, <span class="dv">1</span>])</span>
+<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a><span class="fu">marginmatrix</span>(x)</span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a><span class="do">## for imputed values</span></span>
+<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>x_imp <span class="ot">&lt;-</span> <span class="fu">irmi</span>(sleep[, <span class="dv">2</span><span class="sc">:</span><span class="dv">4</span>])</span>
+<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a>x_imp[,<span class="dv">1</span>] <span class="ot">&lt;-</span> <span class="fu">log10</span>(x_imp[, <span class="dv">1</span>])</span>
+<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a><span class="fu">marginmatrix</span>(x_imp, <span class="at">delimiter =</span> <span class="st">&quot;_imp&quot;</span>)</span></code></pre></div>
+<p><img 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" 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+</div>
+
+
+
+<!-- code folding -->
+
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+  (function () {
+    var script = document.createElement("script");
+    script.type = "text/javascript";
+    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+    document.getElementsByTagName("head")[0].appendChild(script);
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diff --git a/inst/doc/donorImp.R b/inst/doc/donorImp.R
index 37973b2..314534a 100644
--- a/inst/doc/donorImp.R
+++ b/inst/doc/donorImp.R
@@ -8,7 +8,6 @@ knitr::opts_chunk$set(
 
 ## ----setup, message=F---------------------------------------------------------
 library(VIM)
-library(magrittr)
 dataset <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")]
 dataset$BodyWgt <- log(dataset$BodyWgt)
 dataset$Span <- log(dataset$Span)
@@ -25,13 +24,13 @@ aggr(imp_knn, delimiter = "_imp")
 aggr(imp_match, delimiter = "_imp")
 
 ## ---- fig.height=5------------------------------------------------------------
-imp_hotdeck[, c("NonD", "Span", "NonD_imp")] %>% 
+imp_hotdeck[, c("NonD", "Span", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
-imp_knn[, c("NonD", "Span", "NonD_imp")] %>% 
+imp_knn[, c("NonD", "Span", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
 
 ## ---- fig.height=5------------------------------------------------------------
-imp_match[, c("NonD", "Span", "NonD_imp")] %>% 
+imp_match[, c("NonD", "Span", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
 
 ## -----------------------------------------------------------------------------
diff --git a/inst/doc/donorImp.Rmd b/inst/doc/donorImp.Rmd
index 2b80854..5d74498 100644
--- a/inst/doc/donorImp.Rmd
+++ b/inst/doc/donorImp.Rmd
@@ -30,7 +30,6 @@ The following example demonstrates the functionality of `hodeck()` and `kNN()` u
 
 ```{r setup, message=F}
 library(VIM)
-library(magrittr)
 dataset <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")]
 dataset$BodyWgt <- log(dataset$BodyWgt)
 dataset$Span <- log(dataset$Span)
@@ -65,15 +64,15 @@ The specified variables in `matchImpute()` serve as a donor and enable imputatio
 As we can see in the next two plots, the origninal data structure of `NonD` and `Span` is preserved by `hotdeck()`. `kNN()` reveals the typically procedure of methods, which are based on similar data points weighted by the distance. 
 
 ```{r, fig.height=5}
-imp_hotdeck[, c("NonD", "Span", "NonD_imp")] %>% 
+imp_hotdeck[, c("NonD", "Span", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
-imp_knn[, c("NonD", "Span", "NonD_imp")] %>% 
+imp_knn[, c("NonD", "Span", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
 ```
 
 `matchImpute()` works by sampling values from the suitable donors and also provides reasonable results.
 ```{r, fig.height=5}
-imp_match[, c("NonD", "Span", "NonD_imp")] %>% 
+imp_match[, c("NonD", "Span", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
 ```
 
diff --git a/inst/doc/donorImp.html b/inst/doc/donorImp.html
new file mode 100644
index 0000000..ccac73d
--- /dev/null
+++ b/inst/doc/donorImp.html
@@ -0,0 +1,2206 @@
+<!DOCTYPE html>
+
+<html>
+
+<head>
+
+<meta charset="utf-8" />
+<meta name="generator" content="pandoc" />
+<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
+
+<meta name="viewport" content="width=device-width, initial-scale=1" />
+
+<meta name="author" content="Wolfgang Rannetbauer" />
+
+
+<title>Donor based Imputation Methods</title>
+
+<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
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+    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
+    a = h.attributes;
+    while (a.length > 0) h.removeAttribute(a[0].name);
+  }
+});
+</script>
+<script>/**
+ * core-js 2.6.11
+ * https://github.com/zloirock/core-js
+ * License: http://rock.mit-license.org
+ * © 2019 Denis Pushkarev
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+/***/ "./srcjs/input.js":
+/*!************************!*\
+  !*** ./srcjs/input.js ***!
+  \************************/
+/*! exports provided: reactShinyInput */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+__webpack_require__.r(__webpack_exports__);
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "reactShinyInput", function() { return reactShinyInput; });
+/* harmony import */ var react__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! react */ "react");
+/* harmony import */ var react__WEBPACK_IMPORTED_MODULE_0___default = /*#__PURE__*/__webpack_require__.n(react__WEBPACK_IMPORTED_MODULE_0__);
+/* harmony import */ var react_dom__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! react-dom */ "react-dom");
+/* harmony import */ var react_dom__WEBPACK_IMPORTED_MODULE_1___default = /*#__PURE__*/__webpack_require__.n(react_dom__WEBPACK_IMPORTED_MODULE_1__);
+/* harmony import */ var shiny__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! shiny */ "shiny");
+/* harmony import */ var shiny__WEBPACK_IMPORTED_MODULE_2___default = /*#__PURE__*/__webpack_require__.n(shiny__WEBPACK_IMPORTED_MODULE_2__);
+/* harmony import */ var jquery__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! jquery */ "jquery");
+/* harmony import */ var jquery__WEBPACK_IMPORTED_MODULE_3___default = /*#__PURE__*/__webpack_require__.n(jquery__WEBPACK_IMPORTED_MODULE_3__);
+function _typeof(obj) { "@babel/helpers - typeof"; if (typeof Symbol === "function" && typeof Symbol.iterator === "symbol") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }; } return _typeof(obj); }
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+function _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }
+
+function _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }
+
+function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }
+
+function _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }
+
+function _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }
+
+function _possibleConstructorReturn(self, call) { if (call && (_typeof(call) === "object" || typeof call === "function")) { return call; } return _assertThisInitialized(self); }
+
+function _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return self; }
+
+function _isNativeReflectConstruct() { if (typeof Reflect === "undefined" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === "function") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }
+
+function _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }
+
+
+
+
+
+/*
+ * This default receiveMessage implementation expects data to contain whole
+ * configuration and value properties. If either is present, it will be set and
+ * the component will be re-rendered. Because receiveMessage is typically used
+ * by input authors to perform incremental updates, this default implementation
+ * can be overriden by the user with the receiveMessage arguments to
+ * reactShinyInput.
+ */
+
+function defaultReceiveMessage(el, _ref) {
+  var configuration = _ref.configuration,
+      value = _ref.value;
+  var dirty = false;
+
+  if (configuration !== undefined) {
+    this.setInputConfiguration(el, configuration);
+    dirty = true;
+  }
+
+  if (value !== undefined) {
+    this.setInputValue(el, value);
+    dirty = true;
+  }
+
+  if (dirty) {
+    this.getCallback(el)();
+    this.render(el);
+  }
+}
+
+var defaultOptions = {
+  receiveMessage: defaultReceiveMessage,
+  type: false,
+  ratePolicy: null
+};
+/**
+ * Installs a new Shiny input binding based on a React component.
+ *
+ * @param {string} selector - jQuery selector that should identify the set of
+ * container elements within the scope argument of Shiny.InputBinding.find.
+ * @param {string} name - A name such as 'acme.FooInput' that should uniquely
+ * identify the component.
+ * @param {Object} component - React Component, either class or function.
+ * @param {Object} options - Additional configuration options. Supported
+ * options are:
+ * - receiveMessage: Implementation of Shiny.InputBinding to use in place of
+ *   the default. Typically overridden as an optimization to perform
+ *   incremental value updates.
+ * - type: `false`, a string, or a function.
+ *     - `false` (the default): denotes that the value produced by this input
+ *       should not be intercepted by any handlers registered in R on the
+ *       server using shiny::registerInputHandler().
+ *     - string: denotes the input's *type* and should correspond to the
+ *       type parameter of shiny::registerInputHandler().
+ *     - function: A function called with `this` bound to the InputBinding
+ *       instance and passed a single argument, the input's containing DOM
+ *       element. The function should return either `false` or a string
+ *       corresponding to the type parameter of shiny::registerInputHandler().
+ * - ratePolicy: A rate policy object as defined in the documentation for
+ *     getRatePolicy(): https://shiny.rstudio.com/articles/building-inputs.html
+ *     A rate policy object has two members:
+ *     - `policy`: Valid values are the strings "direct", "debounce", and
+ *       "throttle". "direct" means that all events are sent immediately.
+ *     - `delay`: Number indicating the number of milliseconds that should be
+ *       used when debouncing or throttling. Has no effect if the policy is
+ *       direct.
+ *     The specified rate policy is only applied when `true` is passed as the
+ *     second argument to the `setValue` function passed as a prop to the
+ *     input component.
+ *
+ */
+
+function reactShinyInput(selector, name, component, options) {
+  options = Object.assign({}, defaultOptions, options);
+  shiny__WEBPACK_IMPORTED_MODULE_2___default.a.inputBindings.register(new ( /*#__PURE__*/function (_Shiny$InputBinding) {
+    _inherits(_class, _Shiny$InputBinding);
+
+    var _super = _createSuper(_class);
+
+    function _class() {
+      _classCallCheck(this, _class);
+
+      return _super.apply(this, arguments);
+    }
+
+    _createClass(_class, [{
+      key: "find",
+
+      /*
+       * Methods override those in Shiny.InputBinding
+       */
+      value: function find(scope) {
+        return jquery__WEBPACK_IMPORTED_MODULE_3___default()(scope).find(selector);
+      }
+    }, {
+      key: "getValue",
+      value: function getValue(el) {
+        return this.getInputValue(el);
+      }
+    }, {
+      key: "setValue",
+      value: function setValue(el, value) {
+        var rateLimited = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false;
+
+        /*
+         * We have to check whether $(el).data('callback') is undefined here
+         * in case shiny::renderUI() is involved. If an input is contained in a
+         * shiny::uiOutput(), the following strange thing happens occasionally:
+         *
+         *   1. setValue() is bound to an el in this.render(), below
+         *   2. An event that will call setValue() is enqueued
+         *   3. While the event is still enqueued, el is unbound and removed
+         *      from the DOM by the JS code associated with shiny::uiOutput()
+         *      - That code uses jQuery .html() in output_binding_html.js
+         *      - .html() removes el from the DOM and clears ist data and events
+         *   4. By the time the setValue() bound to the original el is invoked,
+         *      el has been unbound and its data cleared.
+         *
+         *  Since the original input is gone along with its callback, it
+         *  seems to make the most sense to do nothing.
+         */
+        if (jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('callback') !== undefined) {
+          this.setInputValue(el, value);
+          this.getCallback(el)(rateLimited);
+          this.render(el);
+        }
+      }
+    }, {
+      key: "initialize",
+      value: function initialize(el) {
+        jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('value', JSON.parse(jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).next().text()));
+        jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('configuration', JSON.parse(jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).next().next().text()));
+      }
+    }, {
+      key: "subscribe",
+      value: function subscribe(el, callback) {
+        jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('callback', callback);
+        this.render(el);
+      }
+    }, {
+      key: "unsubscribe",
+      value: function unsubscribe(el) {
+        react_dom__WEBPACK_IMPORTED_MODULE_1___default.a.render(null, el);
+      }
+    }, {
+      key: "receiveMessage",
+      value: function receiveMessage(el, data) {
+        options.receiveMessage.call(this, el, data);
+      }
+    }, {
+      key: "getType",
+      value: function getType(el) {
+        if (typeof options.type === 'function') {
+          return options.type.call(this, el);
+        } else if (options.type === false || typeof options.type === 'string') {
+          return options.type;
+        } else {
+          throw new Error('options.type must be false, a string, or a function');
+        }
+      }
+    }, {
+      key: "getRatePolicy",
+      value: function getRatePolicy() {
+        return options.ratePolicy;
+      }
+      /*
+       * Methods not present in Shiny.InputBinding but accessible to users
+       * through `this` in receiveMessage
+       */
+
+    }, {
+      key: "getInputValue",
+      value: function getInputValue(el) {
+        return jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('value');
+      }
+    }, {
+      key: "setInputValue",
+      value: function setInputValue(el, value) {
+        jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('value', value);
+      }
+    }, {
+      key: "getInputConfiguration",
+      value: function getInputConfiguration(el) {
+        return jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('configuration');
+      }
+    }, {
+      key: "setInputConfiguration",
+      value: function setInputConfiguration(el, configuration) {
+        jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('configuration', configuration);
+      }
+    }, {
+      key: "getCallback",
+      value: function getCallback(el) {
+        return jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('callback');
+      }
+    }, {
+      key: "render",
+      value: function render(el) {
+        var element = /*#__PURE__*/react__WEBPACK_IMPORTED_MODULE_0___default.a.createElement(component, {
+          configuration: this.getInputConfiguration(el),
+          value: this.getValue(el),
+          setValue: this.setValue.bind(this, el),
+          el: el
+        });
+        react_dom__WEBPACK_IMPORTED_MODULE_1___default.a.render(element, el);
+      }
+    }]);
+
+    return _class;
+  }(shiny__WEBPACK_IMPORTED_MODULE_2___default.a.InputBinding))(), name);
+}
+
+/***/ }),
+
+/***/ "./srcjs/react-tools.js":
+/*!******************************!*\
+  !*** ./srcjs/react-tools.js ***!
+  \******************************/
+/*! no exports provided */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+__webpack_require__.r(__webpack_exports__);
+/* harmony import */ var _widget__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./widget */ "./srcjs/widget.js");
+/* harmony import */ var _input__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./input */ "./srcjs/input.js");
+
+
+window.reactR = {
+  reactShinyInput: _input__WEBPACK_IMPORTED_MODULE_1__["reactShinyInput"],
+  reactWidget: _widget__WEBPACK_IMPORTED_MODULE_0__["reactWidget"],
+  hydrate: _widget__WEBPACK_IMPORTED_MODULE_0__["hydrate"]
+};
+
+/***/ }),
+
+/***/ "./srcjs/widget.js":
+/*!*************************!*\
+  !*** ./srcjs/widget.js ***!
+  \*************************/
+/*! exports provided: hydrate, defaultOptions, mergeOptions, formatDimension, isTag, reactWidget */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+__webpack_require__.r(__webpack_exports__);
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "hydrate", function() { return hydrate; });
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "defaultOptions", function() { return defaultOptions; });
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "mergeOptions", function() { return mergeOptions; });
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "formatDimension", function() { return formatDimension; });
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "isTag", function() { return isTag; });
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "reactWidget", function() { return reactWidget; });
+function _typeof(obj) { "@babel/helpers - typeof"; if (typeof Symbol === "function" && typeof Symbol.iterator === "symbol") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }; } return _typeof(obj); }
+
+/**
+ * Recursively transforms tag, a JSON representation of an instance of a
+ * React component and its children, into a React element suitable for
+ * passing to ReactDOM.render.
+ * @param {Object} components
+ * @param {Object} tag
+ */
+function hydrate(components, tag) {
+  if (typeof tag === 'string') return tag;
+
+  if (tag.name[0] === tag.name[0].toUpperCase() && !components.hasOwnProperty(tag.name)) {
+    throw new Error("Unknown component: " + tag.name);
+  }
+
+  var elem = components.hasOwnProperty(tag.name) ? components[tag.name] : tag.name,
+      args = [elem, tag.attribs];
+
+  for (var i = 0; i < tag.children.length; i++) {
+    args.push(hydrate(components, tag.children[i]));
+  }
+
+  return React.createElement.apply(React, args);
+}
+var defaultOptions = {
+  // The name of the property on the root tag to use for the width, if
+  // it's updated.
+  widthProperty: "width",
+  // The name of the property on the root tag to use for the height, if
+  // it's updated.
+  heightProperty: "height",
+  // Whether or not to append the string 'px' to the width and height
+  // properties when they change.
+  appendPx: false,
+  // Whether or not to dynamically update the width and height properties
+  // of the last known tag when the computed width and height change in
+  // the browser.
+  renderOnResize: false
+};
+function mergeOptions(options) {
+  var merged = {};
+
+  for (var k in defaultOptions) {
+    merged[k] = defaultOptions[k];
+  }
+
+  for (var k in options) {
+    if (!defaultOptions.hasOwnProperty(k)) {
+      throw new Error("Unrecognized option: " + k);
+    }
+
+    merged[k] = options[k];
+  }
+
+  return merged;
+}
+function formatDimension(dim, options) {
+  if (options.appendPx) {
+    return dim + 'px';
+  } else {
+    return dim;
+  }
+}
+function isTag(value) {
+  return _typeof(value) === 'object' && value.hasOwnProperty('name') && value.hasOwnProperty('attribs') && value.hasOwnProperty('children');
+}
+/**
+ * Creates an HTMLWidget that is updated by rendering a React component.
+ * React component constructors are made available by specifying them by
+ * name in the components object.
+ * @param {string} name
+ * @param {string} type
+ * @param {Object} components
+ * @param {Object} options
+ */
+
+function reactWidget(name, type, components, options) {
+  var actualOptions = mergeOptions(options);
+  window.HTMLWidgets.widget({
+    name: name,
+    type: type,
+    factory: function factory(el, width, height) {
+      var lastValue,
+          instance = {},
+          renderValue = function renderValue(value) {
+        if (actualOptions.renderOnResize) {
+          // value.tag might be a primitive string, in which
+          // case there is no attribs property.
+          if (_typeof(value.tag) === 'object') {
+            value.tag.attribs[actualOptions["widthProperty"]] = formatDimension(width);
+            value.tag.attribs[actualOptions["heightProperty"]] = formatDimension(height);
+          }
+
+          lastValue = value;
+        } // with functional stateless components this will be null
+        // see https://reactjs.org/docs/react-dom.html#render for more details
+
+
+        this.instance.component = ReactDOM.render(hydrate(components, value.tag), el);
+      };
+
+      return {
+        instance: instance,
+        renderValue: renderValue,
+        resize: function resize(newWidth, newHeight) {
+          if (actualOptions.renderOnResize) {
+            width = newWidth;
+            height = newHeight;
+            renderValue(lastValue);
+          }
+        }
+      };
+    }
+  });
+}
+
+/***/ }),
+
+/***/ "jquery":
+/*!********************************!*\
+  !*** external "window.jQuery" ***!
+  \********************************/
+/*! no static exports found */
+/***/ (function(module, exports) {
+
+module.exports = window.jQuery;
+
+/***/ }),
+
+/***/ "react":
+/*!*******************************!*\
+  !*** external "window.React" ***!
+  \*******************************/
+/*! no static exports found */
+/***/ (function(module, exports) {
+
+module.exports = window.React;
+
+/***/ }),
+
+/***/ "react-dom":
+/*!**********************************!*\
+  !*** external "window.ReactDOM" ***!
+  \**********************************/
+/*! no static exports found */
+/***/ (function(module, exports) {
+
+module.exports = window.ReactDOM;
+
+/***/ }),
+
+/***/ "shiny":
+/*!*******************************!*\
+  !*** external "window.Shiny" ***!
+  \*******************************/
+/*! no static exports found */
+/***/ (function(module, exports) {
+
+module.exports = window.Shiny;
+
+/***/ })
+
+/******/ });
+//# sourceMappingURL=react-tools.js.map</script>
+<script>(function() {
+  // If window.HTMLWidgets is already defined, then use it; otherwise create a
+  // new object. This allows preceding code to set options that affect the
+  // initialization process (though none currently exist).
+  window.HTMLWidgets = window.HTMLWidgets || {};
+
+  // See if we're running in a viewer pane. If not, we're in a web browser.
+  var viewerMode = window.HTMLWidgets.viewerMode =
+      /\bviewer_pane=1\b/.test(window.location);
+
+  // See if we're running in Shiny mode. If not, it's a static document.
+  // Note that static widgets can appear in both Shiny and static modes, but
+  // obviously, Shiny widgets can only appear in Shiny apps/documents.
+  var shinyMode = window.HTMLWidgets.shinyMode =
+      typeof(window.Shiny) !== "undefined" && !!window.Shiny.outputBindings;
+
+  // We can't count on jQuery being available, so we implement our own
+  // version if necessary.
+  function querySelectorAll(scope, selector) {
+    if (typeof(jQuery) !== "undefined" && scope instanceof jQuery) {
+      return scope.find(selector);
+    }
+    if (scope.querySelectorAll) {
+      return scope.querySelectorAll(selector);
+    }
+  }
+
+  function asArray(value) {
+    if (value === null)
+      return [];
+    if ($.isArray(value))
+      return value;
+    return [value];
+  }
+
+  // Implement jQuery's extend
+  function extend(target /*, ... */) {
+    if (arguments.length == 1) {
+      return target;
+    }
+    for (var i = 1; i < arguments.length; i++) {
+      var source = arguments[i];
+      for (var prop in source) {
+        if (source.hasOwnProperty(prop)) {
+          target[prop] = source[prop];
+        }
+      }
+    }
+    return target;
+  }
+
+  // IE8 doesn't support Array.forEach.
+  function forEach(values, callback, thisArg) {
+    if (values.forEach) {
+      values.forEach(callback, thisArg);
+    } else {
+      for (var i = 0; i < values.length; i++) {
+        callback.call(thisArg, values[i], i, values);
+      }
+    }
+  }
+
+  // Replaces the specified method with the return value of funcSource.
+  //
+  // Note that funcSource should not BE the new method, it should be a function
+  // that RETURNS the new method. funcSource receives a single argument that is
+  // the overridden method, it can be called from the new method. The overridden
+  // method can be called like a regular function, it has the target permanently
+  // bound to it so "this" will work correctly.
+  function overrideMethod(target, methodName, funcSource) {
+    var superFunc = target[methodName] || function() {};
+    var superFuncBound = function() {
+      return superFunc.apply(target, arguments);
+    };
+    target[methodName] = funcSource(superFuncBound);
+  }
+
+  // Add a method to delegator that, when invoked, calls
+  // delegatee.methodName. If there is no such method on
+  // the delegatee, but there was one on delegator before
+  // delegateMethod was called, then the original version
+  // is invoked instead.
+  // For example:
+  //
+  // var a = {
+  //   method1: function() { console.log('a1'); }
+  //   method2: function() { console.log('a2'); }
+  // };
+  // var b = {
+  //   method1: function() { console.log('b1'); }
+  // };
+  // delegateMethod(a, b, "method1");
+  // delegateMethod(a, b, "method2");
+  // a.method1();
+  // a.method2();
+  //
+  // The output would be "b1", "a2".
+  function delegateMethod(delegator, delegatee, methodName) {
+    var inherited = delegator[methodName];
+    delegator[methodName] = function() {
+      var target = delegatee;
+      var method = delegatee[methodName];
+
+      // The method doesn't exist on the delegatee. Instead,
+      // call the method on the delegator, if it exists.
+      if (!method) {
+        target = delegator;
+        method = inherited;
+      }
+
+      if (method) {
+        return method.apply(target, arguments);
+      }
+    };
+  }
+
+  // Implement a vague facsimilie of jQuery's data method
+  function elementData(el, name, value) {
+    if (arguments.length == 2) {
+      return el["htmlwidget_data_" + name];
+    } else if (arguments.length == 3) {
+      el["htmlwidget_data_" + name] = value;
+      return el;
+    } else {
+      throw new Error("Wrong number of arguments for elementData: " +
+        arguments.length);
+    }
+  }
+
+  // http://stackoverflow.com/questions/3446170/escape-string-for-use-in-javascript-regex
+  function escapeRegExp(str) {
+    return str.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g, "\\$&");
+  }
+
+  function hasClass(el, className) {
+    var re = new RegExp("\\b" + escapeRegExp(className) + "\\b");
+    return re.test(el.className);
+  }
+
+  // elements - array (or array-like object) of HTML elements
+  // className - class name to test for
+  // include - if true, only return elements with given className;
+  //   if false, only return elements *without* given className
+  function filterByClass(elements, className, include) {
+    var results = [];
+    for (var i = 0; i < elements.length; i++) {
+      if (hasClass(elements[i], className) == include)
+        results.push(elements[i]);
+    }
+    return results;
+  }
+
+  function on(obj, eventName, func) {
+    if (obj.addEventListener) {
+      obj.addEventListener(eventName, func, false);
+    } else if (obj.attachEvent) {
+      obj.attachEvent(eventName, func);
+    }
+  }
+
+  function off(obj, eventName, func) {
+    if (obj.removeEventListener)
+      obj.removeEventListener(eventName, func, false);
+    else if (obj.detachEvent) {
+      obj.detachEvent(eventName, func);
+    }
+  }
+
+  // Translate array of values to top/right/bottom/left, as usual with
+  // the "padding" CSS property
+  // https://developer.mozilla.org/en-US/docs/Web/CSS/padding
+  function unpackPadding(value) {
+    if (typeof(value) === "number")
+      value = [value];
+    if (value.length === 1) {
+      return {top: value[0], right: value[0], bottom: value[0], left: value[0]};
+    }
+    if (value.length === 2) {
+      return {top: value[0], right: value[1], bottom: value[0], left: value[1]};
+    }
+    if (value.length === 3) {
+      return {top: value[0], right: value[1], bottom: value[2], left: value[1]};
+    }
+    if (value.length === 4) {
+      return {top: value[0], right: value[1], bottom: value[2], left: value[3]};
+    }
+  }
+
+  // Convert an unpacked padding object to a CSS value
+  function paddingToCss(paddingObj) {
+    return paddingObj.top + "px " + paddingObj.right + "px " + paddingObj.bottom + "px " + paddingObj.left + "px";
+  }
+
+  // Makes a number suitable for CSS
+  function px(x) {
+    if (typeof(x) === "number")
+      return x + "px";
+    else
+      return x;
+  }
+
+  // Retrieves runtime widget sizing information for an element.
+  // The return value is either null, or an object with fill, padding,
+  // defaultWidth, defaultHeight fields.
+  function sizingPolicy(el) {
+    var sizingEl = document.querySelector("script[data-for='" + el.id + "'][type='application/htmlwidget-sizing']");
+    if (!sizingEl)
+      return null;
+    var sp = JSON.parse(sizingEl.textContent || sizingEl.text || "{}");
+    if (viewerMode) {
+      return sp.viewer;
+    } else {
+      return sp.browser;
+    }
+  }
+
+  // @param tasks Array of strings (or falsy value, in which case no-op).
+  //   Each element must be a valid JavaScript expression that yields a
+  //   function. Or, can be an array of objects with "code" and "data"
+  //   properties; in this case, the "code" property should be a string
+  //   of JS that's an expr that yields a function, and "data" should be
+  //   an object that will be added as an additional argument when that
+  //   function is called.
+  // @param target The object that will be "this" for each function
+  //   execution.
+  // @param args Array of arguments to be passed to the functions. (The
+  //   same arguments will be passed to all functions.)
+  function evalAndRun(tasks, target, args) {
+    if (tasks) {
+      forEach(tasks, function(task) {
+        var theseArgs = args;
+        if (typeof(task) === "object") {
+          theseArgs = theseArgs.concat([task.data]);
+          task = task.code;
+        }
+        var taskFunc = tryEval(task);
+        if (typeof(taskFunc) !== "function") {
+          throw new Error("Task must be a function! Source:\n" + task);
+        }
+        taskFunc.apply(target, theseArgs);
+      });
+    }
+  }
+
+  // Attempt eval() both with and without enclosing in parentheses.
+  // Note that enclosing coerces a function declaration into
+  // an expression that eval() can parse
+  // (otherwise, a SyntaxError is thrown)
+  function tryEval(code) {
+    var result = null;
+    try {
+      result = eval("(" + code + ")");
+    } catch(error) {
+      if (!(error instanceof SyntaxError)) {
+        throw error;
+      }
+      try {
+        result = eval(code);
+      } catch(e) {
+        if (e instanceof SyntaxError) {
+          throw error;
+        } else {
+          throw e;
+        }
+      }
+    }
+    return result;
+  }
+
+  function initSizing(el) {
+    var sizing = sizingPolicy(el);
+    if (!sizing)
+      return;
+
+    var cel = document.getElementById("htmlwidget_container");
+    if (!cel)
+      return;
+
+    if (typeof(sizing.padding) !== "undefined") {
+      document.body.style.margin = "0";
+      document.body.style.padding = paddingToCss(unpackPadding(sizing.padding));
+    }
+
+    if (sizing.fill) {
+      document.body.style.overflow = "hidden";
+      document.body.style.width = "100%";
+      document.body.style.height = "100%";
+      document.documentElement.style.width = "100%";
+      document.documentElement.style.height = "100%";
+      cel.style.position = "absolute";
+      var pad = unpackPadding(sizing.padding);
+      cel.style.top = pad.top + "px";
+      cel.style.right = pad.right + "px";
+      cel.style.bottom = pad.bottom + "px";
+      cel.style.left = pad.left + "px";
+      el.style.width = "100%";
+      el.style.height = "100%";
+
+      return {
+        getWidth: function() { return cel.getBoundingClientRect().width; },
+        getHeight: function() { return cel.getBoundingClientRect().height; }
+      };
+
+    } else {
+      el.style.width = px(sizing.width);
+      el.style.height = px(sizing.height);
+
+      return {
+        getWidth: function() { return cel.getBoundingClientRect().width; },
+        getHeight: function() { return cel.getBoundingClientRect().height; }
+      };
+    }
+  }
+
+  // Default implementations for methods
+  var defaults = {
+    find: function(scope) {
+      return querySelectorAll(scope, "." + this.name);
+    },
+    renderError: function(el, err) {
+      var $el = $(el);
+
+      this.clearError(el);
+
+      // Add all these error classes, as Shiny does
+      var errClass = "shiny-output-error";
+      if (err.type !== null) {
+        // use the classes of the error condition as CSS class names
+        errClass = errClass + " " + $.map(asArray(err.type), function(type) {
+          return errClass + "-" + type;
+        }).join(" ");
+      }
+      errClass = errClass + " htmlwidgets-error";
+
+      // Is el inline or block? If inline or inline-block, just display:none it
+      // and add an inline error.
+      var display = $el.css("display");
+      $el.data("restore-display-mode", display);
+
+      if (display === "inline" || display === "inline-block") {
+        $el.hide();
+        if (err.message !== "") {
+          var errorSpan = $("<span>").addClass(errClass);
+          errorSpan.text(err.message);
+          $el.after(errorSpan);
+        }
+      } else if (display === "block") {
+        // If block, add an error just after the el, set visibility:none on the
+        // el, and position the error to be on top of the el.
+        // Mark it with a unique ID and CSS class so we can remove it later.
+        $el.css("visibility", "hidden");
+        if (err.message !== "") {
+          var errorDiv = $("<div>").addClass(errClass).css("position", "absolute")
+            .css("top", el.offsetTop)
+            .css("left", el.offsetLeft)
+            // setting width can push out the page size, forcing otherwise
+            // unnecessary scrollbars to appear and making it impossible for
+            // the element to shrink; so use max-width instead
+            .css("maxWidth", el.offsetWidth)
+            .css("height", el.offsetHeight);
+          errorDiv.text(err.message);
+          $el.after(errorDiv);
+
+          // Really dumb way to keep the size/position of the error in sync with
+          // the parent element as the window is resized or whatever.
+          var intId = setInterval(function() {
+            if (!errorDiv[0].parentElement) {
+              clearInterval(intId);
+              return;
+            }
+            errorDiv
+              .css("top", el.offsetTop)
+              .css("left", el.offsetLeft)
+              .css("maxWidth", el.offsetWidth)
+              .css("height", el.offsetHeight);
+          }, 500);
+        }
+      }
+    },
+    clearError: function(el) {
+      var $el = $(el);
+      var display = $el.data("restore-display-mode");
+      $el.data("restore-display-mode", null);
+
+      if (display === "inline" || display === "inline-block") {
+        if (display)
+          $el.css("display", display);
+        $(el.nextSibling).filter(".htmlwidgets-error").remove();
+      } else if (display === "block"){
+        $el.css("visibility", "inherit");
+        $(el.nextSibling).filter(".htmlwidgets-error").remove();
+      }
+    },
+    sizing: {}
+  };
+
+  // Called by widget bindings to register a new type of widget. The definition
+  // object can contain the following properties:
+  // - name (required) - A string indicating the binding name, which will be
+  //   used by default as the CSS classname to look for.
+  // - initialize (optional) - A function(el) that will be called once per
+  //   widget element; if a value is returned, it will be passed as the third
+  //   value to renderValue.
+  // - renderValue (required) - A function(el, data, initValue) that will be
+  //   called with data. Static contexts will cause this to be called once per
+  //   element; Shiny apps will cause this to be called multiple times per
+  //   element, as the data changes.
+  window.HTMLWidgets.widget = function(definition) {
+    if (!definition.name) {
+      throw new Error("Widget must have a name");
+    }
+    if (!definition.type) {
+      throw new Error("Widget must have a type");
+    }
+    // Currently we only support output widgets
+    if (definition.type !== "output") {
+      throw new Error("Unrecognized widget type '" + definition.type + "'");
+    }
+    // TODO: Verify that .name is a valid CSS classname
+
+    // Support new-style instance-bound definitions. Old-style class-bound
+    // definitions have one widget "object" per widget per type/class of
+    // widget; the renderValue and resize methods on such widget objects
+    // take el and instance arguments, because the widget object can't
+    // store them. New-style instance-bound definitions have one widget
+    // object per widget instance; the definition that's passed in doesn't
+    // provide renderValue or resize methods at all, just the single method
+    //   factory(el, width, height)
+    // which returns an object that has renderValue(x) and resize(w, h).
+    // This enables a far more natural programming style for the widget
+    // author, who can store per-instance state using either OO-style
+    // instance fields or functional-style closure variables (I guess this
+    // is in contrast to what can only be called C-style pseudo-OO which is
+    // what we required before).
+    if (definition.factory) {
+      definition = createLegacyDefinitionAdapter(definition);
+    }
+
+    if (!definition.renderValue) {
+      throw new Error("Widget must have a renderValue function");
+    }
+
+    // For static rendering (non-Shiny), use a simple widget registration
+    // scheme. We also use this scheme for Shiny apps/documents that also
+    // contain static widgets.
+    window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];
+    // Merge defaults into the definition; don't mutate the original definition.
+    var staticBinding = extend({}, defaults, definition);
+    overrideMethod(staticBinding, "find", function(superfunc) {
+      return function(scope) {
+        var results = superfunc(scope);
+        // Filter out Shiny outputs, we only want the static kind
+        return filterByClass(results, "html-widget-output", false);
+      };
+    });
+    window.HTMLWidgets.widgets.push(staticBinding);
+
+    if (shinyMode) {
+      // Shiny is running. Register the definition with an output binding.
+      // The definition itself will not be the output binding, instead
+      // we will make an output binding object that delegates to the
+      // definition. This is because we foolishly used the same method
+      // name (renderValue) for htmlwidgets definition and Shiny bindings
+      // but they actually have quite different semantics (the Shiny
+      // bindings receive data that includes lots of metadata that it
+      // strips off before calling htmlwidgets renderValue). We can't
+      // just ignore the difference because in some widgets it's helpful
+      // to call this.renderValue() from inside of resize(), and if
+      // we're not delegating, then that call will go to the Shiny
+      // version instead of the htmlwidgets version.
+
+      // Merge defaults with definition, without mutating either.
+      var bindingDef = extend({}, defaults, definition);
+
+      // This object will be our actual Shiny binding.
+      var shinyBinding = new Shiny.OutputBinding();
+
+      // With a few exceptions, we'll want to simply use the bindingDef's
+      // version of methods if they are available, otherwise fall back to
+      // Shiny's defaults. NOTE: If Shiny's output bindings gain additional
+      // methods in the future, and we want them to be overrideable by
+      // HTMLWidget binding definitions, then we'll need to add them to this
+      // list.
+      delegateMethod(shinyBinding, bindingDef, "getId");
+      delegateMethod(shinyBinding, bindingDef, "onValueChange");
+      delegateMethod(shinyBinding, bindingDef, "onValueError");
+      delegateMethod(shinyBinding, bindingDef, "renderError");
+      delegateMethod(shinyBinding, bindingDef, "clearError");
+      delegateMethod(shinyBinding, bindingDef, "showProgress");
+
+      // The find, renderValue, and resize are handled differently, because we
+      // want to actually decorate the behavior of the bindingDef methods.
+
+      shinyBinding.find = function(scope) {
+        var results = bindingDef.find(scope);
+
+        // Only return elements that are Shiny outputs, not static ones
+        var dynamicResults = results.filter(".html-widget-output");
+
+        // It's possible that whatever caused Shiny to think there might be
+        // new dynamic outputs, also caused there to be new static outputs.
+        // Since there might be lots of different htmlwidgets bindings, we
+        // schedule execution for later--no need to staticRender multiple
+        // times.
+        if (results.length !== dynamicResults.length)
+          scheduleStaticRender();
+
+        return dynamicResults;
+      };
+
+      // Wrap renderValue to handle initialization, which unfortunately isn't
+      // supported natively by Shiny at the time of this writing.
+
+      shinyBinding.renderValue = function(el, data) {
+        Shiny.renderDependencies(data.deps);
+        // Resolve strings marked as javascript literals to objects
+        if (!(data.evals instanceof Array)) data.evals = [data.evals];
+        for (var i = 0; data.evals && i < data.evals.length; i++) {
+          window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);
+        }
+        if (!bindingDef.renderOnNullValue) {
+          if (data.x === null) {
+            el.style.visibility = "hidden";
+            return;
+          } else {
+            el.style.visibility = "inherit";
+          }
+        }
+        if (!elementData(el, "initialized")) {
+          initSizing(el);
+
+          elementData(el, "initialized", true);
+          if (bindingDef.initialize) {
+            var rect = el.getBoundingClientRect();
+            var result = bindingDef.initialize(el, rect.width, rect.height);
+            elementData(el, "init_result", result);
+          }
+        }
+        bindingDef.renderValue(el, data.x, elementData(el, "init_result"));
+        evalAndRun(data.jsHooks.render, elementData(el, "init_result"), [el, data.x]);
+      };
+
+      // Only override resize if bindingDef implements it
+      if (bindingDef.resize) {
+        shinyBinding.resize = function(el, width, height) {
+          // Shiny can call resize before initialize/renderValue have been
+          // called, which doesn't make sense for widgets.
+          if (elementData(el, "initialized")) {
+            bindingDef.resize(el, width, height, elementData(el, "init_result"));
+          }
+        };
+      }
+
+      Shiny.outputBindings.register(shinyBinding, bindingDef.name);
+    }
+  };
+
+  var scheduleStaticRenderTimerId = null;
+  function scheduleStaticRender() {
+    if (!scheduleStaticRenderTimerId) {
+      scheduleStaticRenderTimerId = setTimeout(function() {
+        scheduleStaticRenderTimerId = null;
+        window.HTMLWidgets.staticRender();
+      }, 1);
+    }
+  }
+
+  // Render static widgets after the document finishes loading
+  // Statically render all elements that are of this widget's class
+  window.HTMLWidgets.staticRender = function() {
+    var bindings = window.HTMLWidgets.widgets || [];
+    forEach(bindings, function(binding) {
+      var matches = binding.find(document.documentElement);
+      forEach(matches, function(el) {
+        var sizeObj = initSizing(el, binding);
+
+        var getSize = function(el) {
+          if (sizeObj) {
+            return {w: sizeObj.getWidth(), h: sizeObj.getHeight()}
+          } else {
+            var rect = el.getBoundingClientRect();
+            return {w: rect.width, h: rect.height}
+          }
+        };
+
+        if (hasClass(el, "html-widget-static-bound"))
+          return;
+        el.className = el.className + " html-widget-static-bound";
+
+        var initResult;
+        if (binding.initialize) {
+          var size = getSize(el);
+          initResult = binding.initialize(el, size.w, size.h);
+          elementData(el, "init_result", initResult);
+        }
+
+        if (binding.resize) {
+          var lastSize = getSize(el);
+          var resizeHandler = function(e) {
+            var size = getSize(el);
+            if (size.w === 0 && size.h === 0)
+              return;
+            if (size.w === lastSize.w && size.h === lastSize.h)
+              return;
+            lastSize = size;
+            binding.resize(el, size.w, size.h, initResult);
+          };
+
+          on(window, "resize", resizeHandler);
+
+          // This is needed for cases where we're running in a Shiny
+          // app, but the widget itself is not a Shiny output, but
+          // rather a simple static widget. One example of this is
+          // an rmarkdown document that has runtime:shiny and widget
+          // that isn't in a render function. Shiny only knows to
+          // call resize handlers for Shiny outputs, not for static
+          // widgets, so we do it ourselves.
+          if (window.jQuery) {
+            window.jQuery(document).on(
+              "shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets",
+              resizeHandler
+            );
+            window.jQuery(document).on(
+              "hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets",
+              resizeHandler
+            );
+          }
+
+          // This is needed for the specific case of ioslides, which
+          // flips slides between display:none and display:block.
+          // Ideally we would not have to have ioslide-specific code
+          // here, but rather have ioslides raise a generic event,
+          // but the rmarkdown package just went to CRAN so the
+          // window to getting that fixed may be long.
+          if (window.addEventListener) {
+            // It's OK to limit this to window.addEventListener
+            // browsers because ioslides itself only supports
+            // such browsers.
+            on(document, "slideenter", resizeHandler);
+            on(document, "slideleave", resizeHandler);
+          }
+        }
+
+        var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']");
+        if (scriptData) {
+          var data = JSON.parse(scriptData.textContent || scriptData.text);
+          // Resolve strings marked as javascript literals to objects
+          if (!(data.evals instanceof Array)) data.evals = [data.evals];
+          for (var k = 0; data.evals && k < data.evals.length; k++) {
+            window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);
+          }
+          binding.renderValue(el, data.x, initResult);
+          evalAndRun(data.jsHooks.render, initResult, [el, data.x]);
+        }
+      });
+    });
+
+    invokePostRenderHandlers();
+  }
+
+
+  function has_jQuery3() {
+    if (!window.jQuery) {
+      return false;
+    }
+    var $version = window.jQuery.fn.jquery;
+    var $major_version = parseInt($version.split(".")[0]);
+    return $major_version >= 3;
+  }
+
+  /*
+  / Shiny 1.4 bumped jQuery from 1.x to 3.x which means jQuery's
+  / on-ready handler (i.e., $(fn)) is now asyncronous (i.e., it now
+  / really means $(setTimeout(fn)).
+  / https://jquery.com/upgrade-guide/3.0/#breaking-change-document-ready-handlers-are-now-asynchronous
+  /
+  / Since Shiny uses $() to schedule initShiny, shiny>=1.4 calls initShiny
+  / one tick later than it did before, which means staticRender() is
+  / called renderValue() earlier than (advanced) widget authors might be expecting.
+  / https://github.com/rstudio/shiny/issues/2630
+  /
+  / For a concrete example, leaflet has some methods (e.g., updateBounds)
+  / which reference Shiny methods registered in initShiny (e.g., setInputValue).
+  / Since leaflet is privy to this life-cycle, it knows to use setTimeout() to
+  / delay execution of those methods (until Shiny methods are ready)
+  / https://github.com/rstudio/leaflet/blob/18ec981/javascript/src/index.js#L266-L268
+  /
+  / Ideally widget authors wouldn't need to use this setTimeout() hack that
+  / leaflet uses to call Shiny methods on a staticRender(). In the long run,
+  / the logic initShiny should be broken up so that method registration happens
+  / right away, but binding happens later.
+  */
+  function maybeStaticRenderLater() {
+    if (shinyMode && has_jQuery3()) {
+      window.jQuery(window.HTMLWidgets.staticRender);
+    } else {
+      window.HTMLWidgets.staticRender();
+    }
+  }
+
+  if (document.addEventListener) {
+    document.addEventListener("DOMContentLoaded", function() {
+      document.removeEventListener("DOMContentLoaded", arguments.callee, false);
+      maybeStaticRenderLater();
+    }, false);
+  } else if (document.attachEvent) {
+    document.attachEvent("onreadystatechange", function() {
+      if (document.readyState === "complete") {
+        document.detachEvent("onreadystatechange", arguments.callee);
+        maybeStaticRenderLater();
+      }
+    });
+  }
+
+
+  window.HTMLWidgets.getAttachmentUrl = function(depname, key) {
+    // If no key, default to the first item
+    if (typeof(key) === "undefined")
+      key = 1;
+
+    var link = document.getElementById(depname + "-" + key + "-attachment");
+    if (!link) {
+      throw new Error("Attachment " + depname + "/" + key + " not found in document");
+    }
+    return link.getAttribute("href");
+  };
+
+  window.HTMLWidgets.dataframeToD3 = function(df) {
+    var names = [];
+    var length;
+    for (var name in df) {
+        if (df.hasOwnProperty(name))
+            names.push(name);
+        if (typeof(df[name]) !== "object" || typeof(df[name].length) === "undefined") {
+            throw new Error("All fields must be arrays");
+        } else if (typeof(length) !== "undefined" && length !== df[name].length) {
+            throw new Error("All fields must be arrays of the same length");
+        }
+        length = df[name].length;
+    }
+    var results = [];
+    var item;
+    for (var row = 0; row < length; row++) {
+        item = {};
+        for (var col = 0; col < names.length; col++) {
+            item[names[col]] = df[names[col]][row];
+        }
+        results.push(item);
+    }
+    return results;
+  };
+
+  window.HTMLWidgets.transposeArray2D = function(array) {
+      if (array.length === 0) return array;
+      var newArray = array[0].map(function(col, i) {
+          return array.map(function(row) {
+              return row[i]
+          })
+      });
+      return newArray;
+  };
+  // Split value at splitChar, but allow splitChar to be escaped
+  // using escapeChar. Any other characters escaped by escapeChar
+  // will be included as usual (including escapeChar itself).
+  function splitWithEscape(value, splitChar, escapeChar) {
+    var results = [];
+    var escapeMode = false;
+    var currentResult = "";
+    for (var pos = 0; pos < value.length; pos++) {
+      if (!escapeMode) {
+        if (value[pos] === splitChar) {
+          results.push(currentResult);
+          currentResult = "";
+        } else if (value[pos] === escapeChar) {
+          escapeMode = true;
+        } else {
+          currentResult += value[pos];
+        }
+      } else {
+        currentResult += value[pos];
+        escapeMode = false;
+      }
+    }
+    if (currentResult !== "") {
+      results.push(currentResult);
+    }
+    return results;
+  }
+  // Function authored by Yihui/JJ Allaire
+  window.HTMLWidgets.evaluateStringMember = function(o, member) {
+    var parts = splitWithEscape(member, '.', '\\');
+    for (var i = 0, l = parts.length; i < l; i++) {
+      var part = parts[i];
+      // part may be a character or 'numeric' member name
+      if (o !== null && typeof o === "object" && part in o) {
+        if (i == (l - 1)) { // if we are at the end of the line then evalulate
+          if (typeof o[part] === "string")
+            o[part] = tryEval(o[part]);
+        } else { // otherwise continue to next embedded object
+          o = o[part];
+        }
+      }
+    }
+  };
+
+  // Retrieve the HTMLWidget instance (i.e. the return value of an
+  // HTMLWidget binding's initialize() or factory() function)
+  // associated with an element, or null if none.
+  window.HTMLWidgets.getInstance = function(el) {
+    return elementData(el, "init_result");
+  };
+
+  // Finds the first element in the scope that matches the selector,
+  // and returns the HTMLWidget instance (i.e. the return value of
+  // an HTMLWidget binding's initialize() or factory() function)
+  // associated with that element, if any. If no element matches the
+  // selector, or the first matching element has no HTMLWidget
+  // instance associated with it, then null is returned.
+  //
+  // The scope argument is optional, and defaults to window.document.
+  window.HTMLWidgets.find = function(scope, selector) {
+    if (arguments.length == 1) {
+      selector = scope;
+      scope = document;
+    }
+
+    var el = scope.querySelector(selector);
+    if (el === null) {
+      return null;
+    } else {
+      return window.HTMLWidgets.getInstance(el);
+    }
+  };
+
+  // Finds all elements in the scope that match the selector, and
+  // returns the HTMLWidget instances (i.e. the return values of
+  // an HTMLWidget binding's initialize() or factory() function)
+  // associated with the elements, in an array. If elements that
+  // match the selector don't have an associated HTMLWidget
+  // instance, the returned array will contain nulls.
+  //
+  // The scope argument is optional, and defaults to window.document.
+  window.HTMLWidgets.findAll = function(scope, selector) {
+    if (arguments.length == 1) {
+      selector = scope;
+      scope = document;
+    }
+
+    var nodes = scope.querySelectorAll(selector);
+    var results = [];
+    for (var i = 0; i < nodes.length; i++) {
+      results.push(window.HTMLWidgets.getInstance(nodes[i]));
+    }
+    return results;
+  };
+
+  var postRenderHandlers = [];
+  function invokePostRenderHandlers() {
+    while (postRenderHandlers.length) {
+      var handler = postRenderHandlers.shift();
+      if (handler) {
+        handler();
+      }
+    }
+  }
+
+  // Register the given callback function to be invoked after the
+  // next time static widgets are rendered.
+  window.HTMLWidgets.addPostRenderHandler = function(callback) {
+    postRenderHandlers.push(callback);
+  };
+
+  // Takes a new-style instance-bound definition, and returns an
+  // old-style class-bound definition. This saves us from having
+  // to rewrite all the logic in this file to accomodate both
+  // types of definitions.
+  function createLegacyDefinitionAdapter(defn) {
+    var result = {
+      name: defn.name,
+      type: defn.type,
+      initialize: function(el, width, height) {
+        return defn.factory(el, width, height);
+      },
+      renderValue: function(el, x, instance) {
+        return instance.renderValue(x);
+      },
+      resize: function(el, width, height, instance) {
+        return instance.resize(width, height);
+      }
+    };
+
+    if (defn.find)
+      result.find = defn.find;
+    if (defn.renderError)
+      result.renderError = defn.renderError;
+    if (defn.clearError)
+      result.clearError = defn.clearError;
+
+    return result;
+  }
+})();
+</script>
+<link 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+
+<h1 class="title toc-ignore">Donor based Imputation Methods</h1>
+<h4 class="author">Wolfgang Rannetbauer</h4>
+
+
+
+<div id="overview" class="section level2">
+<h2>Overview</h2>
+<p>In addition to Model based Imputation Methods (see
+<code>vignette(&quot;modelImp&quot;)</code>) the <code>VIM</code> package also
+presents donor based imputation methods, namely Hot-Deck Imputation,
+k-Nearest Neighbour Imputation and fast matching/imputation based on
+categorical variable.</p>
+<p>This vignette showcases the functions <code>hotdeck()</code> and
+<code>kNN()</code>, which can both be used to generate imputations for
+several variables in a dataset. Moreover, the function
+<code>matchImpute()</code> is presented, which is in contrast a
+imputation method based on categorical variables.</p>
+</div>
+<div id="data" class="section level2">
+<h2>Data</h2>
+<p>The following example demonstrates the functionality of
+<code>hodeck()</code> and <code>kNN()</code> using a subset of
+<code>sleep</code>. The columns have been selected deliberately to
+include some interactions between the missing values.</p>
+<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(VIM)</span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>dataset <span class="ot">&lt;-</span> sleep[, <span class="fu">c</span>(<span class="st">&quot;Dream&quot;</span>, <span class="st">&quot;NonD&quot;</span>, <span class="st">&quot;BodyWgt&quot;</span>, <span class="st">&quot;Span&quot;</span>)]</span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>dataset<span class="sc">$</span>BodyWgt <span class="ot">&lt;-</span> <span class="fu">log</span>(dataset<span class="sc">$</span>BodyWgt)</span>
+<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>dataset<span class="sc">$</span>Span <span class="ot">&lt;-</span> <span class="fu">log</span>(dataset<span class="sc">$</span>Span)</span>
+<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="fu">aggr</span>(dataset)</span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+<p>The plot indicates several missing values in <code>Dream</code>,
+<code>NonD</code>, and <code>Span.</code></p>
+<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="fu">sapply</span>(dataset, <span class="cf">function</span>(x)<span class="fu">sum</span>(<span class="fu">is.na</span>(x)))</span>
+<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   Dream    NonD BodyWgt    Span </span></span>
+<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;      12      14       0       4</span></span></code></pre></div>
+</div>
+<div id="imputation" class="section level2">
+<h2>Imputation</h2>
+<p>The call of the functions is straightforward. We will start by just
+imputing <code>NonD</code> based on the other variables. Besides
+imputing missing variables for a single variable, these functions also
+support imputation of multiple variables. For <code>matchImpute()</code>
+suitable donors are searched based on matching of the categorical
+variables.</p>
+<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>imp_hotdeck <span class="ot">&lt;-</span> <span class="fu">hotdeck</span>(dataset, <span class="at">variable =</span> <span class="st">&quot;NonD&quot;</span>)  <span class="co"># hotdeck imputation</span></span>
+<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>imp_knn <span class="ot">&lt;-</span> <span class="fu">kNN</span>(dataset, <span class="at">variable =</span> <span class="st">&quot;NonD&quot;</span>) <span class="co"># kNN imputation</span></span>
+<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>imp_match <span class="ot">&lt;-</span> <span class="fu">matchImpute</span>(dataset, <span class="at">variable =</span> <span class="st">&quot;NonD&quot;</span>, <span class="at">match_var =</span> <span class="fu">c</span>(<span class="st">&quot;BodyWgt&quot;</span>,<span class="st">&quot;Span&quot;</span>)) <span class="co"># match imputation</span></span>
+<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a><span class="fu">aggr</span>(imp_knn, <span class="at">delimiter =</span> <span class="st">&quot;_imp&quot;</span>)</span>
+<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a><span class="fu">aggr</span>(imp_match, <span class="at">delimiter =</span> <span class="st">&quot;_imp&quot;</span>)</span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+<p>We can see that <code>kNN()</code> imputed all missing values for
+<code>NonD</code> in our dataset. The same is true for the values
+imputed via <code>hotdeck()</code>. The specified variables in
+<code>matchImpute()</code> serve as a donor and enable imputation for
+<code>NonD</code>.</p>
+<div id="diagnosing-the-results" class="section level3">
+<h3>Diagnosing the results</h3>
+<p>As we can see in the next two plots, the origninal data structure of
+<code>NonD</code> and <code>Span</code> is preserved by
+<code>hotdeck()</code>. <code>kNN()</code> reveals the typically
+procedure of methods, which are based on similar data points weighted by
+the distance.</p>
+<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>imp_hotdeck[, <span class="fu">c</span>(<span class="st">&quot;NonD&quot;</span>, <span class="st">&quot;Span&quot;</span>, <span class="st">&quot;NonD_imp&quot;</span>)] <span class="sc">|&gt;</span> </span>
+<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">marginplot</span>(<span class="at">delimiter =</span> <span class="st">&quot;_imp&quot;</span>)</span></code></pre></div>
+<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
+<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>imp_knn[, <span class="fu">c</span>(<span class="st">&quot;NonD&quot;</span>, <span class="st">&quot;Span&quot;</span>, <span class="st">&quot;NonD_imp&quot;</span>)] <span class="sc">|&gt;</span> </span>
+<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">marginplot</span>(<span class="at">delimiter =</span> <span class="st">&quot;_imp&quot;</span>)</span></code></pre></div>
+<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
+<p><code>matchImpute()</code> works by sampling values from the suitable
+donors and also provides reasonable results.</p>
+<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>imp_match[, <span class="fu">c</span>(<span class="st">&quot;NonD&quot;</span>, <span class="st">&quot;Span&quot;</span>, <span class="st">&quot;NonD_imp&quot;</span>)] <span class="sc">|&gt;</span> </span>
+<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">marginplot</span>(<span class="at">delimiter =</span> <span class="st">&quot;_imp&quot;</span>)</span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+</div>
+</div>
+<div id="performance-of-method" class="section level2">
+<h2>Performance of method</h2>
+<p>In order to validate the performance of <code>kNN()</code> and to
+highlight the ability to impute different datatypes the
+<code>iris</code> dataset is used. Firstly, some values are randomly set
+to <code>NA</code>.</p>
+<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(iris)</span>
+<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> iris</span>
+<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a><span class="fu">colnames</span>(df) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;S.Length&quot;</span>,<span class="st">&quot;S.Width&quot;</span>,<span class="st">&quot;P.Length&quot;</span>,<span class="st">&quot;P.Width&quot;</span>,<span class="st">&quot;Species&quot;</span>)</span>
+<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a><span class="co"># randomly produce some missing values in the data</span></span>
+<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">1</span>)</span>
+<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a>nbr_missing <span class="ot">&lt;-</span> <span class="dv">50</span></span>
+<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a>y <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">row =</span> <span class="fu">sample</span>(<span class="fu">nrow</span>(iris), <span class="at">size =</span> nbr_missing, <span class="at">replace =</span> <span class="cn">TRUE</span>),</span>
+<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a>                <span class="at">col =</span> <span class="fu">sample</span>(<span class="fu">ncol</span>(iris), <span class="at">size =</span> nbr_missing, <span class="at">replace =</span> <span class="cn">TRUE</span>))</span>
+<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a>y<span class="ot">&lt;-</span>y[<span class="sc">!</span><span class="fu">duplicated</span>(y), ]</span>
+<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a>df[<span class="fu">as.matrix</span>(y)] <span class="ot">&lt;-</span> <span class="cn">NA</span></span>
+<span id="cb7-11"><a href="#cb7-11" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a><span class="fu">aggr</span>(df)</span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="fu">sapply</span>(df, <span class="cf">function</span>(x) <span class="fu">sum</span>(<span class="fu">is.na</span>(x)))</span>
+<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; S.Length  S.Width P.Length  P.Width  Species </span></span>
+<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;       10        9        8       10       12</span></span></code></pre></div>
+<p>We can see that there are missings in all variables and some
+observations reveal missing values on several points.</p>
+<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>imp_knn <span class="ot">&lt;-</span> <span class="fu">kNN</span>(df)</span>
+<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a><span class="fu">aggr</span>(imp_knn, <span class="at">delimiter =</span> <span class="st">&quot;imp&quot;</span>)</span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+<p>The plot indicates that all missing values have been imputed by
+<code>kNN()</code>. The following table displays the rounded first five
+results of the imputation for all variables.</p>
+<div class="reactable html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-70c255e2466d62322b30" style="width:auto;height:auto;"></div>
+<script type="application/json" data-for="htmlwidget-70c255e2466d62322b30">{"x":{"tag":{"name":"Reactable","attribs":{"data":{"TRUE1":["6.4","7","5.4","6.5","7.6"],"IMPUTED1":["6.9","6.4","6.1","6.8","6.3"],"TRUE2":["4.1","2.7","2.8","3.8","3.5"],"IMPUTED2":["3.5","3.2","2.8","3.5","3.4"],"TRUE3":["4.1","4.1","3.9","5.1","4.7"],"IMPUTED3":["4","4.2","4.5","5.5","4.5"],"TRUE4":["0.2","0.2","0.3","1.5","0.2"],"IMPUTED4":["0.2","0.4","0.2","1.4","0.2"],"TRUE5":["setosa","virginica","versicolor","versicolor","virginica"],"IMPUTED5":["setosa","virginica","versicolor","virginica","virginica"]},"columns":[{"id":"TRUE1","name":"True","type":"character"},{"id":"IMPUTED1","name":"Imputed","type":"character"},{"id":"TRUE2","name":"True","type":"character"},{"id":"IMPUTED2","name":"Imputed","type":"character"},{"id":"TRUE3","name":"True","type":"character"},{"id":"IMPUTED3","name":"Imputed","type":"character"},{"id":"TRUE4","name":"True","type":"character"},{"id":"IMPUTED4","name":"Imputed","type":"character"},{"id":"TRUE5","name":"True","type":"character"},{"id":"IMPUTED5","name":"Imputed","type":"character"}],"columnGroups":[{"name":"S.Length","columns":["TRUE1","IMPUTED1"]},{"name":"S.Width","columns":["TRUE2","IMPUTED2"]},{"name":"P.Length","columns":["TRUE3","IMPUTED3"]},{"name":"P.Width","columns":["TRUE4","IMPUTED4"]},{"name":"Species","columns":["TRUE5","IMPUTED5"]}],"highlight":true,"bordered":true,"striped":true,"dataKey":"53ca64e03cfd3be75a1d3333f815da04"},"children":[]},"class":"reactR_markup"},"evals":[],"jsHooks":[]}</script>
+</div>
+
+
+
+<!-- code folding -->
+
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+  (function () {
+    var script = document.createElement("script");
+    script.type = "text/javascript";
+    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+    document.getElementsByTagName("head")[0].appendChild(script);
+  })();
+</script>
+
+</body>
+</html>
diff --git a/inst/doc/irmi.R b/inst/doc/irmi.R
index e9ac9b8..f443ccb 100644
--- a/inst/doc/irmi.R
+++ b/inst/doc/irmi.R
@@ -8,7 +8,6 @@ knitr::opts_chunk$set(
 
 ## ----setup, message=F---------------------------------------------------------
 library(VIM)
-library(magrittr)
 dataset <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")]
 dataset$BodyWgt <- log(dataset$BodyWgt)
 dataset$Span <- log(dataset$Span)
@@ -22,11 +21,11 @@ imp_irmi <- irmi(dataset)
 aggr(imp_irmi, delimiter = "_imp")
 
 ## ---- fig.height=5------------------------------------------------------------
-imp_irmi[, c("NonD", "BodyWgt", "NonD_imp")] %>% 
+imp_irmi[, c("NonD", "BodyWgt", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
 
 ## ---- fig.height=5------------------------------------------------------------
-imp_irmi[, c("Span", "BodyWgt", "Span_imp")] %>% 
+imp_irmi[, c("Span", "BodyWgt", "Span_imp")] |> 
   marginplot(delimiter = "_imp")
 
 ## -----------------------------------------------------------------------------
diff --git a/inst/doc/irmi.Rmd b/inst/doc/irmi.Rmd
index 6395143..f27bb94 100644
--- a/inst/doc/irmi.Rmd
+++ b/inst/doc/irmi.Rmd
@@ -31,7 +31,6 @@ The following example demonstrates the functionality of `irmi()` using a subset
 
 ```{r setup, message=F}
 library(VIM)
-library(magrittr)
 dataset <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")]
 dataset$BodyWgt <- log(dataset$BodyWgt)
 dataset$Span <- log(dataset$Span)
@@ -61,14 +60,14 @@ We can see that `irmi()` imputed all missing values for all variables in our dat
 As we can see in the next plot, for imputing missing values in `NonD` `Bodygt` plays an important role. The original data structure of `NonD` and `BodyWgt` is preserved by the `irmi()` imputation method.
 
 ```{r, fig.height=5}
-imp_irmi[, c("NonD", "BodyWgt", "NonD_imp")] %>% 
+imp_irmi[, c("NonD", "BodyWgt", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
 ```
 
 The same is true for the data structure of `Span` and `BodyWgt`.
 
 ```{r, fig.height=5}
-imp_irmi[, c("Span", "BodyWgt", "Span_imp")] %>% 
+imp_irmi[, c("Span", "BodyWgt", "Span_imp")] |> 
   marginplot(delimiter = "_imp")
 ```
 
diff --git a/inst/doc/irmi.html b/inst/doc/irmi.html
new file mode 100644
index 0000000..51d865c
--- /dev/null
+++ b/inst/doc/irmi.html
@@ -0,0 +1,2200 @@
+<!DOCTYPE html>
+
+<html>
+
+<head>
+
+<meta charset="utf-8" />
+<meta name="generator" content="pandoc" />
+<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
+
+<meta name="viewport" content="width=device-width, initial-scale=1" />
+
+<meta name="author" content="Wolfgang Rannetbauer" />
+
+
+<title>Imputation Method IRMI</title>
+
+<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+  var i, h, a;
+  for (i = 0; i < hs.length; i++) {
+    h = hs[i];
+    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
+    a = h.attributes;
+    while (a.length > 0) h.removeAttribute(a[0].name);
+  }
+});
+</script>
+<script>/**
+ * core-js 2.6.11
+ * https://github.com/zloirock/core-js
+ * License: http://rock.mit-license.org
+ * © 2019 Denis Pushkarev
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+/***/ "./srcjs/input.js":
+/*!************************!*\
+  !*** ./srcjs/input.js ***!
+  \************************/
+/*! exports provided: reactShinyInput */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+__webpack_require__.r(__webpack_exports__);
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "reactShinyInput", function() { return reactShinyInput; });
+/* harmony import */ var react__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! react */ "react");
+/* harmony import */ var react__WEBPACK_IMPORTED_MODULE_0___default = /*#__PURE__*/__webpack_require__.n(react__WEBPACK_IMPORTED_MODULE_0__);
+/* harmony import */ var react_dom__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! react-dom */ "react-dom");
+/* harmony import */ var react_dom__WEBPACK_IMPORTED_MODULE_1___default = /*#__PURE__*/__webpack_require__.n(react_dom__WEBPACK_IMPORTED_MODULE_1__);
+/* harmony import */ var shiny__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! shiny */ "shiny");
+/* harmony import */ var shiny__WEBPACK_IMPORTED_MODULE_2___default = /*#__PURE__*/__webpack_require__.n(shiny__WEBPACK_IMPORTED_MODULE_2__);
+/* harmony import */ var jquery__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! jquery */ "jquery");
+/* harmony import */ var jquery__WEBPACK_IMPORTED_MODULE_3___default = /*#__PURE__*/__webpack_require__.n(jquery__WEBPACK_IMPORTED_MODULE_3__);
+function _typeof(obj) { "@babel/helpers - typeof"; if (typeof Symbol === "function" && typeof Symbol.iterator === "symbol") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }; } return _typeof(obj); }
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+function _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }
+
+function _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }
+
+function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }
+
+function _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }
+
+function _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }
+
+function _possibleConstructorReturn(self, call) { if (call && (_typeof(call) === "object" || typeof call === "function")) { return call; } return _assertThisInitialized(self); }
+
+function _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return self; }
+
+function _isNativeReflectConstruct() { if (typeof Reflect === "undefined" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === "function") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }
+
+function _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }
+
+
+
+
+
+/*
+ * This default receiveMessage implementation expects data to contain whole
+ * configuration and value properties. If either is present, it will be set and
+ * the component will be re-rendered. Because receiveMessage is typically used
+ * by input authors to perform incremental updates, this default implementation
+ * can be overriden by the user with the receiveMessage arguments to
+ * reactShinyInput.
+ */
+
+function defaultReceiveMessage(el, _ref) {
+  var configuration = _ref.configuration,
+      value = _ref.value;
+  var dirty = false;
+
+  if (configuration !== undefined) {
+    this.setInputConfiguration(el, configuration);
+    dirty = true;
+  }
+
+  if (value !== undefined) {
+    this.setInputValue(el, value);
+    dirty = true;
+  }
+
+  if (dirty) {
+    this.getCallback(el)();
+    this.render(el);
+  }
+}
+
+var defaultOptions = {
+  receiveMessage: defaultReceiveMessage,
+  type: false,
+  ratePolicy: null
+};
+/**
+ * Installs a new Shiny input binding based on a React component.
+ *
+ * @param {string} selector - jQuery selector that should identify the set of
+ * container elements within the scope argument of Shiny.InputBinding.find.
+ * @param {string} name - A name such as 'acme.FooInput' that should uniquely
+ * identify the component.
+ * @param {Object} component - React Component, either class or function.
+ * @param {Object} options - Additional configuration options. Supported
+ * options are:
+ * - receiveMessage: Implementation of Shiny.InputBinding to use in place of
+ *   the default. Typically overridden as an optimization to perform
+ *   incremental value updates.
+ * - type: `false`, a string, or a function.
+ *     - `false` (the default): denotes that the value produced by this input
+ *       should not be intercepted by any handlers registered in R on the
+ *       server using shiny::registerInputHandler().
+ *     - string: denotes the input's *type* and should correspond to the
+ *       type parameter of shiny::registerInputHandler().
+ *     - function: A function called with `this` bound to the InputBinding
+ *       instance and passed a single argument, the input's containing DOM
+ *       element. The function should return either `false` or a string
+ *       corresponding to the type parameter of shiny::registerInputHandler().
+ * - ratePolicy: A rate policy object as defined in the documentation for
+ *     getRatePolicy(): https://shiny.rstudio.com/articles/building-inputs.html
+ *     A rate policy object has two members:
+ *     - `policy`: Valid values are the strings "direct", "debounce", and
+ *       "throttle". "direct" means that all events are sent immediately.
+ *     - `delay`: Number indicating the number of milliseconds that should be
+ *       used when debouncing or throttling. Has no effect if the policy is
+ *       direct.
+ *     The specified rate policy is only applied when `true` is passed as the
+ *     second argument to the `setValue` function passed as a prop to the
+ *     input component.
+ *
+ */
+
+function reactShinyInput(selector, name, component, options) {
+  options = Object.assign({}, defaultOptions, options);
+  shiny__WEBPACK_IMPORTED_MODULE_2___default.a.inputBindings.register(new ( /*#__PURE__*/function (_Shiny$InputBinding) {
+    _inherits(_class, _Shiny$InputBinding);
+
+    var _super = _createSuper(_class);
+
+    function _class() {
+      _classCallCheck(this, _class);
+
+      return _super.apply(this, arguments);
+    }
+
+    _createClass(_class, [{
+      key: "find",
+
+      /*
+       * Methods override those in Shiny.InputBinding
+       */
+      value: function find(scope) {
+        return jquery__WEBPACK_IMPORTED_MODULE_3___default()(scope).find(selector);
+      }
+    }, {
+      key: "getValue",
+      value: function getValue(el) {
+        return this.getInputValue(el);
+      }
+    }, {
+      key: "setValue",
+      value: function setValue(el, value) {
+        var rateLimited = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false;
+
+        /*
+         * We have to check whether $(el).data('callback') is undefined here
+         * in case shiny::renderUI() is involved. If an input is contained in a
+         * shiny::uiOutput(), the following strange thing happens occasionally:
+         *
+         *   1. setValue() is bound to an el in this.render(), below
+         *   2. An event that will call setValue() is enqueued
+         *   3. While the event is still enqueued, el is unbound and removed
+         *      from the DOM by the JS code associated with shiny::uiOutput()
+         *      - That code uses jQuery .html() in output_binding_html.js
+         *      - .html() removes el from the DOM and clears ist data and events
+         *   4. By the time the setValue() bound to the original el is invoked,
+         *      el has been unbound and its data cleared.
+         *
+         *  Since the original input is gone along with its callback, it
+         *  seems to make the most sense to do nothing.
+         */
+        if (jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('callback') !== undefined) {
+          this.setInputValue(el, value);
+          this.getCallback(el)(rateLimited);
+          this.render(el);
+        }
+      }
+    }, {
+      key: "initialize",
+      value: function initialize(el) {
+        jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('value', JSON.parse(jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).next().text()));
+        jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('configuration', JSON.parse(jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).next().next().text()));
+      }
+    }, {
+      key: "subscribe",
+      value: function subscribe(el, callback) {
+        jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('callback', callback);
+        this.render(el);
+      }
+    }, {
+      key: "unsubscribe",
+      value: function unsubscribe(el) {
+        react_dom__WEBPACK_IMPORTED_MODULE_1___default.a.render(null, el);
+      }
+    }, {
+      key: "receiveMessage",
+      value: function receiveMessage(el, data) {
+        options.receiveMessage.call(this, el, data);
+      }
+    }, {
+      key: "getType",
+      value: function getType(el) {
+        if (typeof options.type === 'function') {
+          return options.type.call(this, el);
+        } else if (options.type === false || typeof options.type === 'string') {
+          return options.type;
+        } else {
+          throw new Error('options.type must be false, a string, or a function');
+        }
+      }
+    }, {
+      key: "getRatePolicy",
+      value: function getRatePolicy() {
+        return options.ratePolicy;
+      }
+      /*
+       * Methods not present in Shiny.InputBinding but accessible to users
+       * through `this` in receiveMessage
+       */
+
+    }, {
+      key: "getInputValue",
+      value: function getInputValue(el) {
+        return jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('value');
+      }
+    }, {
+      key: "setInputValue",
+      value: function setInputValue(el, value) {
+        jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('value', value);
+      }
+    }, {
+      key: "getInputConfiguration",
+      value: function getInputConfiguration(el) {
+        return jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('configuration');
+      }
+    }, {
+      key: "setInputConfiguration",
+      value: function setInputConfiguration(el, configuration) {
+        jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('configuration', configuration);
+      }
+    }, {
+      key: "getCallback",
+      value: function getCallback(el) {
+        return jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('callback');
+      }
+    }, {
+      key: "render",
+      value: function render(el) {
+        var element = /*#__PURE__*/react__WEBPACK_IMPORTED_MODULE_0___default.a.createElement(component, {
+          configuration: this.getInputConfiguration(el),
+          value: this.getValue(el),
+          setValue: this.setValue.bind(this, el),
+          el: el
+        });
+        react_dom__WEBPACK_IMPORTED_MODULE_1___default.a.render(element, el);
+      }
+    }]);
+
+    return _class;
+  }(shiny__WEBPACK_IMPORTED_MODULE_2___default.a.InputBinding))(), name);
+}
+
+/***/ }),
+
+/***/ "./srcjs/react-tools.js":
+/*!******************************!*\
+  !*** ./srcjs/react-tools.js ***!
+  \******************************/
+/*! no exports provided */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+__webpack_require__.r(__webpack_exports__);
+/* harmony import */ var _widget__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./widget */ "./srcjs/widget.js");
+/* harmony import */ var _input__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./input */ "./srcjs/input.js");
+
+
+window.reactR = {
+  reactShinyInput: _input__WEBPACK_IMPORTED_MODULE_1__["reactShinyInput"],
+  reactWidget: _widget__WEBPACK_IMPORTED_MODULE_0__["reactWidget"],
+  hydrate: _widget__WEBPACK_IMPORTED_MODULE_0__["hydrate"]
+};
+
+/***/ }),
+
+/***/ "./srcjs/widget.js":
+/*!*************************!*\
+  !*** ./srcjs/widget.js ***!
+  \*************************/
+/*! exports provided: hydrate, defaultOptions, mergeOptions, formatDimension, isTag, reactWidget */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+__webpack_require__.r(__webpack_exports__);
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "hydrate", function() { return hydrate; });
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "defaultOptions", function() { return defaultOptions; });
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "mergeOptions", function() { return mergeOptions; });
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "formatDimension", function() { return formatDimension; });
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "isTag", function() { return isTag; });
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "reactWidget", function() { return reactWidget; });
+function _typeof(obj) { "@babel/helpers - typeof"; if (typeof Symbol === "function" && typeof Symbol.iterator === "symbol") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }; } return _typeof(obj); }
+
+/**
+ * Recursively transforms tag, a JSON representation of an instance of a
+ * React component and its children, into a React element suitable for
+ * passing to ReactDOM.render.
+ * @param {Object} components
+ * @param {Object} tag
+ */
+function hydrate(components, tag) {
+  if (typeof tag === 'string') return tag;
+
+  if (tag.name[0] === tag.name[0].toUpperCase() && !components.hasOwnProperty(tag.name)) {
+    throw new Error("Unknown component: " + tag.name);
+  }
+
+  var elem = components.hasOwnProperty(tag.name) ? components[tag.name] : tag.name,
+      args = [elem, tag.attribs];
+
+  for (var i = 0; i < tag.children.length; i++) {
+    args.push(hydrate(components, tag.children[i]));
+  }
+
+  return React.createElement.apply(React, args);
+}
+var defaultOptions = {
+  // The name of the property on the root tag to use for the width, if
+  // it's updated.
+  widthProperty: "width",
+  // The name of the property on the root tag to use for the height, if
+  // it's updated.
+  heightProperty: "height",
+  // Whether or not to append the string 'px' to the width and height
+  // properties when they change.
+  appendPx: false,
+  // Whether or not to dynamically update the width and height properties
+  // of the last known tag when the computed width and height change in
+  // the browser.
+  renderOnResize: false
+};
+function mergeOptions(options) {
+  var merged = {};
+
+  for (var k in defaultOptions) {
+    merged[k] = defaultOptions[k];
+  }
+
+  for (var k in options) {
+    if (!defaultOptions.hasOwnProperty(k)) {
+      throw new Error("Unrecognized option: " + k);
+    }
+
+    merged[k] = options[k];
+  }
+
+  return merged;
+}
+function formatDimension(dim, options) {
+  if (options.appendPx) {
+    return dim + 'px';
+  } else {
+    return dim;
+  }
+}
+function isTag(value) {
+  return _typeof(value) === 'object' && value.hasOwnProperty('name') && value.hasOwnProperty('attribs') && value.hasOwnProperty('children');
+}
+/**
+ * Creates an HTMLWidget that is updated by rendering a React component.
+ * React component constructors are made available by specifying them by
+ * name in the components object.
+ * @param {string} name
+ * @param {string} type
+ * @param {Object} components
+ * @param {Object} options
+ */
+
+function reactWidget(name, type, components, options) {
+  var actualOptions = mergeOptions(options);
+  window.HTMLWidgets.widget({
+    name: name,
+    type: type,
+    factory: function factory(el, width, height) {
+      var lastValue,
+          instance = {},
+          renderValue = function renderValue(value) {
+        if (actualOptions.renderOnResize) {
+          // value.tag might be a primitive string, in which
+          // case there is no attribs property.
+          if (_typeof(value.tag) === 'object') {
+            value.tag.attribs[actualOptions["widthProperty"]] = formatDimension(width);
+            value.tag.attribs[actualOptions["heightProperty"]] = formatDimension(height);
+          }
+
+          lastValue = value;
+        } // with functional stateless components this will be null
+        // see https://reactjs.org/docs/react-dom.html#render for more details
+
+
+        this.instance.component = ReactDOM.render(hydrate(components, value.tag), el);
+      };
+
+      return {
+        instance: instance,
+        renderValue: renderValue,
+        resize: function resize(newWidth, newHeight) {
+          if (actualOptions.renderOnResize) {
+            width = newWidth;
+            height = newHeight;
+            renderValue(lastValue);
+          }
+        }
+      };
+    }
+  });
+}
+
+/***/ }),
+
+/***/ "jquery":
+/*!********************************!*\
+  !*** external "window.jQuery" ***!
+  \********************************/
+/*! no static exports found */
+/***/ (function(module, exports) {
+
+module.exports = window.jQuery;
+
+/***/ }),
+
+/***/ "react":
+/*!*******************************!*\
+  !*** external "window.React" ***!
+  \*******************************/
+/*! no static exports found */
+/***/ (function(module, exports) {
+
+module.exports = window.React;
+
+/***/ }),
+
+/***/ "react-dom":
+/*!**********************************!*\
+  !*** external "window.ReactDOM" ***!
+  \**********************************/
+/*! no static exports found */
+/***/ (function(module, exports) {
+
+module.exports = window.ReactDOM;
+
+/***/ }),
+
+/***/ "shiny":
+/*!*******************************!*\
+  !*** external "window.Shiny" ***!
+  \*******************************/
+/*! no static exports found */
+/***/ (function(module, exports) {
+
+module.exports = window.Shiny;
+
+/***/ })
+
+/******/ });
+//# sourceMappingURL=react-tools.js.map</script>
+<script>(function() {
+  // If window.HTMLWidgets is already defined, then use it; otherwise create a
+  // new object. This allows preceding code to set options that affect the
+  // initialization process (though none currently exist).
+  window.HTMLWidgets = window.HTMLWidgets || {};
+
+  // See if we're running in a viewer pane. If not, we're in a web browser.
+  var viewerMode = window.HTMLWidgets.viewerMode =
+      /\bviewer_pane=1\b/.test(window.location);
+
+  // See if we're running in Shiny mode. If not, it's a static document.
+  // Note that static widgets can appear in both Shiny and static modes, but
+  // obviously, Shiny widgets can only appear in Shiny apps/documents.
+  var shinyMode = window.HTMLWidgets.shinyMode =
+      typeof(window.Shiny) !== "undefined" && !!window.Shiny.outputBindings;
+
+  // We can't count on jQuery being available, so we implement our own
+  // version if necessary.
+  function querySelectorAll(scope, selector) {
+    if (typeof(jQuery) !== "undefined" && scope instanceof jQuery) {
+      return scope.find(selector);
+    }
+    if (scope.querySelectorAll) {
+      return scope.querySelectorAll(selector);
+    }
+  }
+
+  function asArray(value) {
+    if (value === null)
+      return [];
+    if ($.isArray(value))
+      return value;
+    return [value];
+  }
+
+  // Implement jQuery's extend
+  function extend(target /*, ... */) {
+    if (arguments.length == 1) {
+      return target;
+    }
+    for (var i = 1; i < arguments.length; i++) {
+      var source = arguments[i];
+      for (var prop in source) {
+        if (source.hasOwnProperty(prop)) {
+          target[prop] = source[prop];
+        }
+      }
+    }
+    return target;
+  }
+
+  // IE8 doesn't support Array.forEach.
+  function forEach(values, callback, thisArg) {
+    if (values.forEach) {
+      values.forEach(callback, thisArg);
+    } else {
+      for (var i = 0; i < values.length; i++) {
+        callback.call(thisArg, values[i], i, values);
+      }
+    }
+  }
+
+  // Replaces the specified method with the return value of funcSource.
+  //
+  // Note that funcSource should not BE the new method, it should be a function
+  // that RETURNS the new method. funcSource receives a single argument that is
+  // the overridden method, it can be called from the new method. The overridden
+  // method can be called like a regular function, it has the target permanently
+  // bound to it so "this" will work correctly.
+  function overrideMethod(target, methodName, funcSource) {
+    var superFunc = target[methodName] || function() {};
+    var superFuncBound = function() {
+      return superFunc.apply(target, arguments);
+    };
+    target[methodName] = funcSource(superFuncBound);
+  }
+
+  // Add a method to delegator that, when invoked, calls
+  // delegatee.methodName. If there is no such method on
+  // the delegatee, but there was one on delegator before
+  // delegateMethod was called, then the original version
+  // is invoked instead.
+  // For example:
+  //
+  // var a = {
+  //   method1: function() { console.log('a1'); }
+  //   method2: function() { console.log('a2'); }
+  // };
+  // var b = {
+  //   method1: function() { console.log('b1'); }
+  // };
+  // delegateMethod(a, b, "method1");
+  // delegateMethod(a, b, "method2");
+  // a.method1();
+  // a.method2();
+  //
+  // The output would be "b1", "a2".
+  function delegateMethod(delegator, delegatee, methodName) {
+    var inherited = delegator[methodName];
+    delegator[methodName] = function() {
+      var target = delegatee;
+      var method = delegatee[methodName];
+
+      // The method doesn't exist on the delegatee. Instead,
+      // call the method on the delegator, if it exists.
+      if (!method) {
+        target = delegator;
+        method = inherited;
+      }
+
+      if (method) {
+        return method.apply(target, arguments);
+      }
+    };
+  }
+
+  // Implement a vague facsimilie of jQuery's data method
+  function elementData(el, name, value) {
+    if (arguments.length == 2) {
+      return el["htmlwidget_data_" + name];
+    } else if (arguments.length == 3) {
+      el["htmlwidget_data_" + name] = value;
+      return el;
+    } else {
+      throw new Error("Wrong number of arguments for elementData: " +
+        arguments.length);
+    }
+  }
+
+  // http://stackoverflow.com/questions/3446170/escape-string-for-use-in-javascript-regex
+  function escapeRegExp(str) {
+    return str.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g, "\\$&");
+  }
+
+  function hasClass(el, className) {
+    var re = new RegExp("\\b" + escapeRegExp(className) + "\\b");
+    return re.test(el.className);
+  }
+
+  // elements - array (or array-like object) of HTML elements
+  // className - class name to test for
+  // include - if true, only return elements with given className;
+  //   if false, only return elements *without* given className
+  function filterByClass(elements, className, include) {
+    var results = [];
+    for (var i = 0; i < elements.length; i++) {
+      if (hasClass(elements[i], className) == include)
+        results.push(elements[i]);
+    }
+    return results;
+  }
+
+  function on(obj, eventName, func) {
+    if (obj.addEventListener) {
+      obj.addEventListener(eventName, func, false);
+    } else if (obj.attachEvent) {
+      obj.attachEvent(eventName, func);
+    }
+  }
+
+  function off(obj, eventName, func) {
+    if (obj.removeEventListener)
+      obj.removeEventListener(eventName, func, false);
+    else if (obj.detachEvent) {
+      obj.detachEvent(eventName, func);
+    }
+  }
+
+  // Translate array of values to top/right/bottom/left, as usual with
+  // the "padding" CSS property
+  // https://developer.mozilla.org/en-US/docs/Web/CSS/padding
+  function unpackPadding(value) {
+    if (typeof(value) === "number")
+      value = [value];
+    if (value.length === 1) {
+      return {top: value[0], right: value[0], bottom: value[0], left: value[0]};
+    }
+    if (value.length === 2) {
+      return {top: value[0], right: value[1], bottom: value[0], left: value[1]};
+    }
+    if (value.length === 3) {
+      return {top: value[0], right: value[1], bottom: value[2], left: value[1]};
+    }
+    if (value.length === 4) {
+      return {top: value[0], right: value[1], bottom: value[2], left: value[3]};
+    }
+  }
+
+  // Convert an unpacked padding object to a CSS value
+  function paddingToCss(paddingObj) {
+    return paddingObj.top + "px " + paddingObj.right + "px " + paddingObj.bottom + "px " + paddingObj.left + "px";
+  }
+
+  // Makes a number suitable for CSS
+  function px(x) {
+    if (typeof(x) === "number")
+      return x + "px";
+    else
+      return x;
+  }
+
+  // Retrieves runtime widget sizing information for an element.
+  // The return value is either null, or an object with fill, padding,
+  // defaultWidth, defaultHeight fields.
+  function sizingPolicy(el) {
+    var sizingEl = document.querySelector("script[data-for='" + el.id + "'][type='application/htmlwidget-sizing']");
+    if (!sizingEl)
+      return null;
+    var sp = JSON.parse(sizingEl.textContent || sizingEl.text || "{}");
+    if (viewerMode) {
+      return sp.viewer;
+    } else {
+      return sp.browser;
+    }
+  }
+
+  // @param tasks Array of strings (or falsy value, in which case no-op).
+  //   Each element must be a valid JavaScript expression that yields a
+  //   function. Or, can be an array of objects with "code" and "data"
+  //   properties; in this case, the "code" property should be a string
+  //   of JS that's an expr that yields a function, and "data" should be
+  //   an object that will be added as an additional argument when that
+  //   function is called.
+  // @param target The object that will be "this" for each function
+  //   execution.
+  // @param args Array of arguments to be passed to the functions. (The
+  //   same arguments will be passed to all functions.)
+  function evalAndRun(tasks, target, args) {
+    if (tasks) {
+      forEach(tasks, function(task) {
+        var theseArgs = args;
+        if (typeof(task) === "object") {
+          theseArgs = theseArgs.concat([task.data]);
+          task = task.code;
+        }
+        var taskFunc = tryEval(task);
+        if (typeof(taskFunc) !== "function") {
+          throw new Error("Task must be a function! Source:\n" + task);
+        }
+        taskFunc.apply(target, theseArgs);
+      });
+    }
+  }
+
+  // Attempt eval() both with and without enclosing in parentheses.
+  // Note that enclosing coerces a function declaration into
+  // an expression that eval() can parse
+  // (otherwise, a SyntaxError is thrown)
+  function tryEval(code) {
+    var result = null;
+    try {
+      result = eval("(" + code + ")");
+    } catch(error) {
+      if (!(error instanceof SyntaxError)) {
+        throw error;
+      }
+      try {
+        result = eval(code);
+      } catch(e) {
+        if (e instanceof SyntaxError) {
+          throw error;
+        } else {
+          throw e;
+        }
+      }
+    }
+    return result;
+  }
+
+  function initSizing(el) {
+    var sizing = sizingPolicy(el);
+    if (!sizing)
+      return;
+
+    var cel = document.getElementById("htmlwidget_container");
+    if (!cel)
+      return;
+
+    if (typeof(sizing.padding) !== "undefined") {
+      document.body.style.margin = "0";
+      document.body.style.padding = paddingToCss(unpackPadding(sizing.padding));
+    }
+
+    if (sizing.fill) {
+      document.body.style.overflow = "hidden";
+      document.body.style.width = "100%";
+      document.body.style.height = "100%";
+      document.documentElement.style.width = "100%";
+      document.documentElement.style.height = "100%";
+      cel.style.position = "absolute";
+      var pad = unpackPadding(sizing.padding);
+      cel.style.top = pad.top + "px";
+      cel.style.right = pad.right + "px";
+      cel.style.bottom = pad.bottom + "px";
+      cel.style.left = pad.left + "px";
+      el.style.width = "100%";
+      el.style.height = "100%";
+
+      return {
+        getWidth: function() { return cel.getBoundingClientRect().width; },
+        getHeight: function() { return cel.getBoundingClientRect().height; }
+      };
+
+    } else {
+      el.style.width = px(sizing.width);
+      el.style.height = px(sizing.height);
+
+      return {
+        getWidth: function() { return cel.getBoundingClientRect().width; },
+        getHeight: function() { return cel.getBoundingClientRect().height; }
+      };
+    }
+  }
+
+  // Default implementations for methods
+  var defaults = {
+    find: function(scope) {
+      return querySelectorAll(scope, "." + this.name);
+    },
+    renderError: function(el, err) {
+      var $el = $(el);
+
+      this.clearError(el);
+
+      // Add all these error classes, as Shiny does
+      var errClass = "shiny-output-error";
+      if (err.type !== null) {
+        // use the classes of the error condition as CSS class names
+        errClass = errClass + " " + $.map(asArray(err.type), function(type) {
+          return errClass + "-" + type;
+        }).join(" ");
+      }
+      errClass = errClass + " htmlwidgets-error";
+
+      // Is el inline or block? If inline or inline-block, just display:none it
+      // and add an inline error.
+      var display = $el.css("display");
+      $el.data("restore-display-mode", display);
+
+      if (display === "inline" || display === "inline-block") {
+        $el.hide();
+        if (err.message !== "") {
+          var errorSpan = $("<span>").addClass(errClass);
+          errorSpan.text(err.message);
+          $el.after(errorSpan);
+        }
+      } else if (display === "block") {
+        // If block, add an error just after the el, set visibility:none on the
+        // el, and position the error to be on top of the el.
+        // Mark it with a unique ID and CSS class so we can remove it later.
+        $el.css("visibility", "hidden");
+        if (err.message !== "") {
+          var errorDiv = $("<div>").addClass(errClass).css("position", "absolute")
+            .css("top", el.offsetTop)
+            .css("left", el.offsetLeft)
+            // setting width can push out the page size, forcing otherwise
+            // unnecessary scrollbars to appear and making it impossible for
+            // the element to shrink; so use max-width instead
+            .css("maxWidth", el.offsetWidth)
+            .css("height", el.offsetHeight);
+          errorDiv.text(err.message);
+          $el.after(errorDiv);
+
+          // Really dumb way to keep the size/position of the error in sync with
+          // the parent element as the window is resized or whatever.
+          var intId = setInterval(function() {
+            if (!errorDiv[0].parentElement) {
+              clearInterval(intId);
+              return;
+            }
+            errorDiv
+              .css("top", el.offsetTop)
+              .css("left", el.offsetLeft)
+              .css("maxWidth", el.offsetWidth)
+              .css("height", el.offsetHeight);
+          }, 500);
+        }
+      }
+    },
+    clearError: function(el) {
+      var $el = $(el);
+      var display = $el.data("restore-display-mode");
+      $el.data("restore-display-mode", null);
+
+      if (display === "inline" || display === "inline-block") {
+        if (display)
+          $el.css("display", display);
+        $(el.nextSibling).filter(".htmlwidgets-error").remove();
+      } else if (display === "block"){
+        $el.css("visibility", "inherit");
+        $(el.nextSibling).filter(".htmlwidgets-error").remove();
+      }
+    },
+    sizing: {}
+  };
+
+  // Called by widget bindings to register a new type of widget. The definition
+  // object can contain the following properties:
+  // - name (required) - A string indicating the binding name, which will be
+  //   used by default as the CSS classname to look for.
+  // - initialize (optional) - A function(el) that will be called once per
+  //   widget element; if a value is returned, it will be passed as the third
+  //   value to renderValue.
+  // - renderValue (required) - A function(el, data, initValue) that will be
+  //   called with data. Static contexts will cause this to be called once per
+  //   element; Shiny apps will cause this to be called multiple times per
+  //   element, as the data changes.
+  window.HTMLWidgets.widget = function(definition) {
+    if (!definition.name) {
+      throw new Error("Widget must have a name");
+    }
+    if (!definition.type) {
+      throw new Error("Widget must have a type");
+    }
+    // Currently we only support output widgets
+    if (definition.type !== "output") {
+      throw new Error("Unrecognized widget type '" + definition.type + "'");
+    }
+    // TODO: Verify that .name is a valid CSS classname
+
+    // Support new-style instance-bound definitions. Old-style class-bound
+    // definitions have one widget "object" per widget per type/class of
+    // widget; the renderValue and resize methods on such widget objects
+    // take el and instance arguments, because the widget object can't
+    // store them. New-style instance-bound definitions have one widget
+    // object per widget instance; the definition that's passed in doesn't
+    // provide renderValue or resize methods at all, just the single method
+    //   factory(el, width, height)
+    // which returns an object that has renderValue(x) and resize(w, h).
+    // This enables a far more natural programming style for the widget
+    // author, who can store per-instance state using either OO-style
+    // instance fields or functional-style closure variables (I guess this
+    // is in contrast to what can only be called C-style pseudo-OO which is
+    // what we required before).
+    if (definition.factory) {
+      definition = createLegacyDefinitionAdapter(definition);
+    }
+
+    if (!definition.renderValue) {
+      throw new Error("Widget must have a renderValue function");
+    }
+
+    // For static rendering (non-Shiny), use a simple widget registration
+    // scheme. We also use this scheme for Shiny apps/documents that also
+    // contain static widgets.
+    window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];
+    // Merge defaults into the definition; don't mutate the original definition.
+    var staticBinding = extend({}, defaults, definition);
+    overrideMethod(staticBinding, "find", function(superfunc) {
+      return function(scope) {
+        var results = superfunc(scope);
+        // Filter out Shiny outputs, we only want the static kind
+        return filterByClass(results, "html-widget-output", false);
+      };
+    });
+    window.HTMLWidgets.widgets.push(staticBinding);
+
+    if (shinyMode) {
+      // Shiny is running. Register the definition with an output binding.
+      // The definition itself will not be the output binding, instead
+      // we will make an output binding object that delegates to the
+      // definition. This is because we foolishly used the same method
+      // name (renderValue) for htmlwidgets definition and Shiny bindings
+      // but they actually have quite different semantics (the Shiny
+      // bindings receive data that includes lots of metadata that it
+      // strips off before calling htmlwidgets renderValue). We can't
+      // just ignore the difference because in some widgets it's helpful
+      // to call this.renderValue() from inside of resize(), and if
+      // we're not delegating, then that call will go to the Shiny
+      // version instead of the htmlwidgets version.
+
+      // Merge defaults with definition, without mutating either.
+      var bindingDef = extend({}, defaults, definition);
+
+      // This object will be our actual Shiny binding.
+      var shinyBinding = new Shiny.OutputBinding();
+
+      // With a few exceptions, we'll want to simply use the bindingDef's
+      // version of methods if they are available, otherwise fall back to
+      // Shiny's defaults. NOTE: If Shiny's output bindings gain additional
+      // methods in the future, and we want them to be overrideable by
+      // HTMLWidget binding definitions, then we'll need to add them to this
+      // list.
+      delegateMethod(shinyBinding, bindingDef, "getId");
+      delegateMethod(shinyBinding, bindingDef, "onValueChange");
+      delegateMethod(shinyBinding, bindingDef, "onValueError");
+      delegateMethod(shinyBinding, bindingDef, "renderError");
+      delegateMethod(shinyBinding, bindingDef, "clearError");
+      delegateMethod(shinyBinding, bindingDef, "showProgress");
+
+      // The find, renderValue, and resize are handled differently, because we
+      // want to actually decorate the behavior of the bindingDef methods.
+
+      shinyBinding.find = function(scope) {
+        var results = bindingDef.find(scope);
+
+        // Only return elements that are Shiny outputs, not static ones
+        var dynamicResults = results.filter(".html-widget-output");
+
+        // It's possible that whatever caused Shiny to think there might be
+        // new dynamic outputs, also caused there to be new static outputs.
+        // Since there might be lots of different htmlwidgets bindings, we
+        // schedule execution for later--no need to staticRender multiple
+        // times.
+        if (results.length !== dynamicResults.length)
+          scheduleStaticRender();
+
+        return dynamicResults;
+      };
+
+      // Wrap renderValue to handle initialization, which unfortunately isn't
+      // supported natively by Shiny at the time of this writing.
+
+      shinyBinding.renderValue = function(el, data) {
+        Shiny.renderDependencies(data.deps);
+        // Resolve strings marked as javascript literals to objects
+        if (!(data.evals instanceof Array)) data.evals = [data.evals];
+        for (var i = 0; data.evals && i < data.evals.length; i++) {
+          window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);
+        }
+        if (!bindingDef.renderOnNullValue) {
+          if (data.x === null) {
+            el.style.visibility = "hidden";
+            return;
+          } else {
+            el.style.visibility = "inherit";
+          }
+        }
+        if (!elementData(el, "initialized")) {
+          initSizing(el);
+
+          elementData(el, "initialized", true);
+          if (bindingDef.initialize) {
+            var rect = el.getBoundingClientRect();
+            var result = bindingDef.initialize(el, rect.width, rect.height);
+            elementData(el, "init_result", result);
+          }
+        }
+        bindingDef.renderValue(el, data.x, elementData(el, "init_result"));
+        evalAndRun(data.jsHooks.render, elementData(el, "init_result"), [el, data.x]);
+      };
+
+      // Only override resize if bindingDef implements it
+      if (bindingDef.resize) {
+        shinyBinding.resize = function(el, width, height) {
+          // Shiny can call resize before initialize/renderValue have been
+          // called, which doesn't make sense for widgets.
+          if (elementData(el, "initialized")) {
+            bindingDef.resize(el, width, height, elementData(el, "init_result"));
+          }
+        };
+      }
+
+      Shiny.outputBindings.register(shinyBinding, bindingDef.name);
+    }
+  };
+
+  var scheduleStaticRenderTimerId = null;
+  function scheduleStaticRender() {
+    if (!scheduleStaticRenderTimerId) {
+      scheduleStaticRenderTimerId = setTimeout(function() {
+        scheduleStaticRenderTimerId = null;
+        window.HTMLWidgets.staticRender();
+      }, 1);
+    }
+  }
+
+  // Render static widgets after the document finishes loading
+  // Statically render all elements that are of this widget's class
+  window.HTMLWidgets.staticRender = function() {
+    var bindings = window.HTMLWidgets.widgets || [];
+    forEach(bindings, function(binding) {
+      var matches = binding.find(document.documentElement);
+      forEach(matches, function(el) {
+        var sizeObj = initSizing(el, binding);
+
+        var getSize = function(el) {
+          if (sizeObj) {
+            return {w: sizeObj.getWidth(), h: sizeObj.getHeight()}
+          } else {
+            var rect = el.getBoundingClientRect();
+            return {w: rect.width, h: rect.height}
+          }
+        };
+
+        if (hasClass(el, "html-widget-static-bound"))
+          return;
+        el.className = el.className + " html-widget-static-bound";
+
+        var initResult;
+        if (binding.initialize) {
+          var size = getSize(el);
+          initResult = binding.initialize(el, size.w, size.h);
+          elementData(el, "init_result", initResult);
+        }
+
+        if (binding.resize) {
+          var lastSize = getSize(el);
+          var resizeHandler = function(e) {
+            var size = getSize(el);
+            if (size.w === 0 && size.h === 0)
+              return;
+            if (size.w === lastSize.w && size.h === lastSize.h)
+              return;
+            lastSize = size;
+            binding.resize(el, size.w, size.h, initResult);
+          };
+
+          on(window, "resize", resizeHandler);
+
+          // This is needed for cases where we're running in a Shiny
+          // app, but the widget itself is not a Shiny output, but
+          // rather a simple static widget. One example of this is
+          // an rmarkdown document that has runtime:shiny and widget
+          // that isn't in a render function. Shiny only knows to
+          // call resize handlers for Shiny outputs, not for static
+          // widgets, so we do it ourselves.
+          if (window.jQuery) {
+            window.jQuery(document).on(
+              "shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets",
+              resizeHandler
+            );
+            window.jQuery(document).on(
+              "hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets",
+              resizeHandler
+            );
+          }
+
+          // This is needed for the specific case of ioslides, which
+          // flips slides between display:none and display:block.
+          // Ideally we would not have to have ioslide-specific code
+          // here, but rather have ioslides raise a generic event,
+          // but the rmarkdown package just went to CRAN so the
+          // window to getting that fixed may be long.
+          if (window.addEventListener) {
+            // It's OK to limit this to window.addEventListener
+            // browsers because ioslides itself only supports
+            // such browsers.
+            on(document, "slideenter", resizeHandler);
+            on(document, "slideleave", resizeHandler);
+          }
+        }
+
+        var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']");
+        if (scriptData) {
+          var data = JSON.parse(scriptData.textContent || scriptData.text);
+          // Resolve strings marked as javascript literals to objects
+          if (!(data.evals instanceof Array)) data.evals = [data.evals];
+          for (var k = 0; data.evals && k < data.evals.length; k++) {
+            window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);
+          }
+          binding.renderValue(el, data.x, initResult);
+          evalAndRun(data.jsHooks.render, initResult, [el, data.x]);
+        }
+      });
+    });
+
+    invokePostRenderHandlers();
+  }
+
+
+  function has_jQuery3() {
+    if (!window.jQuery) {
+      return false;
+    }
+    var $version = window.jQuery.fn.jquery;
+    var $major_version = parseInt($version.split(".")[0]);
+    return $major_version >= 3;
+  }
+
+  /*
+  / Shiny 1.4 bumped jQuery from 1.x to 3.x which means jQuery's
+  / on-ready handler (i.e., $(fn)) is now asyncronous (i.e., it now
+  / really means $(setTimeout(fn)).
+  / https://jquery.com/upgrade-guide/3.0/#breaking-change-document-ready-handlers-are-now-asynchronous
+  /
+  / Since Shiny uses $() to schedule initShiny, shiny>=1.4 calls initShiny
+  / one tick later than it did before, which means staticRender() is
+  / called renderValue() earlier than (advanced) widget authors might be expecting.
+  / https://github.com/rstudio/shiny/issues/2630
+  /
+  / For a concrete example, leaflet has some methods (e.g., updateBounds)
+  / which reference Shiny methods registered in initShiny (e.g., setInputValue).
+  / Since leaflet is privy to this life-cycle, it knows to use setTimeout() to
+  / delay execution of those methods (until Shiny methods are ready)
+  / https://github.com/rstudio/leaflet/blob/18ec981/javascript/src/index.js#L266-L268
+  /
+  / Ideally widget authors wouldn't need to use this setTimeout() hack that
+  / leaflet uses to call Shiny methods on a staticRender(). In the long run,
+  / the logic initShiny should be broken up so that method registration happens
+  / right away, but binding happens later.
+  */
+  function maybeStaticRenderLater() {
+    if (shinyMode && has_jQuery3()) {
+      window.jQuery(window.HTMLWidgets.staticRender);
+    } else {
+      window.HTMLWidgets.staticRender();
+    }
+  }
+
+  if (document.addEventListener) {
+    document.addEventListener("DOMContentLoaded", function() {
+      document.removeEventListener("DOMContentLoaded", arguments.callee, false);
+      maybeStaticRenderLater();
+    }, false);
+  } else if (document.attachEvent) {
+    document.attachEvent("onreadystatechange", function() {
+      if (document.readyState === "complete") {
+        document.detachEvent("onreadystatechange", arguments.callee);
+        maybeStaticRenderLater();
+      }
+    });
+  }
+
+
+  window.HTMLWidgets.getAttachmentUrl = function(depname, key) {
+    // If no key, default to the first item
+    if (typeof(key) === "undefined")
+      key = 1;
+
+    var link = document.getElementById(depname + "-" + key + "-attachment");
+    if (!link) {
+      throw new Error("Attachment " + depname + "/" + key + " not found in document");
+    }
+    return link.getAttribute("href");
+  };
+
+  window.HTMLWidgets.dataframeToD3 = function(df) {
+    var names = [];
+    var length;
+    for (var name in df) {
+        if (df.hasOwnProperty(name))
+            names.push(name);
+        if (typeof(df[name]) !== "object" || typeof(df[name].length) === "undefined") {
+            throw new Error("All fields must be arrays");
+        } else if (typeof(length) !== "undefined" && length !== df[name].length) {
+            throw new Error("All fields must be arrays of the same length");
+        }
+        length = df[name].length;
+    }
+    var results = [];
+    var item;
+    for (var row = 0; row < length; row++) {
+        item = {};
+        for (var col = 0; col < names.length; col++) {
+            item[names[col]] = df[names[col]][row];
+        }
+        results.push(item);
+    }
+    return results;
+  };
+
+  window.HTMLWidgets.transposeArray2D = function(array) {
+      if (array.length === 0) return array;
+      var newArray = array[0].map(function(col, i) {
+          return array.map(function(row) {
+              return row[i]
+          })
+      });
+      return newArray;
+  };
+  // Split value at splitChar, but allow splitChar to be escaped
+  // using escapeChar. Any other characters escaped by escapeChar
+  // will be included as usual (including escapeChar itself).
+  function splitWithEscape(value, splitChar, escapeChar) {
+    var results = [];
+    var escapeMode = false;
+    var currentResult = "";
+    for (var pos = 0; pos < value.length; pos++) {
+      if (!escapeMode) {
+        if (value[pos] === splitChar) {
+          results.push(currentResult);
+          currentResult = "";
+        } else if (value[pos] === escapeChar) {
+          escapeMode = true;
+        } else {
+          currentResult += value[pos];
+        }
+      } else {
+        currentResult += value[pos];
+        escapeMode = false;
+      }
+    }
+    if (currentResult !== "") {
+      results.push(currentResult);
+    }
+    return results;
+  }
+  // Function authored by Yihui/JJ Allaire
+  window.HTMLWidgets.evaluateStringMember = function(o, member) {
+    var parts = splitWithEscape(member, '.', '\\');
+    for (var i = 0, l = parts.length; i < l; i++) {
+      var part = parts[i];
+      // part may be a character or 'numeric' member name
+      if (o !== null && typeof o === "object" && part in o) {
+        if (i == (l - 1)) { // if we are at the end of the line then evalulate
+          if (typeof o[part] === "string")
+            o[part] = tryEval(o[part]);
+        } else { // otherwise continue to next embedded object
+          o = o[part];
+        }
+      }
+    }
+  };
+
+  // Retrieve the HTMLWidget instance (i.e. the return value of an
+  // HTMLWidget binding's initialize() or factory() function)
+  // associated with an element, or null if none.
+  window.HTMLWidgets.getInstance = function(el) {
+    return elementData(el, "init_result");
+  };
+
+  // Finds the first element in the scope that matches the selector,
+  // and returns the HTMLWidget instance (i.e. the return value of
+  // an HTMLWidget binding's initialize() or factory() function)
+  // associated with that element, if any. If no element matches the
+  // selector, or the first matching element has no HTMLWidget
+  // instance associated with it, then null is returned.
+  //
+  // The scope argument is optional, and defaults to window.document.
+  window.HTMLWidgets.find = function(scope, selector) {
+    if (arguments.length == 1) {
+      selector = scope;
+      scope = document;
+    }
+
+    var el = scope.querySelector(selector);
+    if (el === null) {
+      return null;
+    } else {
+      return window.HTMLWidgets.getInstance(el);
+    }
+  };
+
+  // Finds all elements in the scope that match the selector, and
+  // returns the HTMLWidget instances (i.e. the return values of
+  // an HTMLWidget binding's initialize() or factory() function)
+  // associated with the elements, in an array. If elements that
+  // match the selector don't have an associated HTMLWidget
+  // instance, the returned array will contain nulls.
+  //
+  // The scope argument is optional, and defaults to window.document.
+  window.HTMLWidgets.findAll = function(scope, selector) {
+    if (arguments.length == 1) {
+      selector = scope;
+      scope = document;
+    }
+
+    var nodes = scope.querySelectorAll(selector);
+    var results = [];
+    for (var i = 0; i < nodes.length; i++) {
+      results.push(window.HTMLWidgets.getInstance(nodes[i]));
+    }
+    return results;
+  };
+
+  var postRenderHandlers = [];
+  function invokePostRenderHandlers() {
+    while (postRenderHandlers.length) {
+      var handler = postRenderHandlers.shift();
+      if (handler) {
+        handler();
+      }
+    }
+  }
+
+  // Register the given callback function to be invoked after the
+  // next time static widgets are rendered.
+  window.HTMLWidgets.addPostRenderHandler = function(callback) {
+    postRenderHandlers.push(callback);
+  };
+
+  // Takes a new-style instance-bound definition, and returns an
+  // old-style class-bound definition. This saves us from having
+  // to rewrite all the logic in this file to accomodate both
+  // types of definitions.
+  function createLegacyDefinitionAdapter(defn) {
+    var result = {
+      name: defn.name,
+      type: defn.type,
+      initialize: function(el, width, height) {
+        return defn.factory(el, width, height);
+      },
+      renderValue: function(el, x, instance) {
+        return instance.renderValue(x);
+      },
+      resize: function(el, width, height, instance) {
+        return instance.resize(width, height);
+      }
+    };
+
+    if (defn.find)
+      result.find = defn.find;
+    if (defn.renderError)
+      result.renderError = defn.renderError;
+    if (defn.clearError)
+      result.clearError = defn.clearError;
+
+    return result;
+  }
+})();
+</script>
+<link 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+
+<h1 class="title toc-ignore">Imputation Method IRMI</h1>
+<h4 class="author">Wolfgang Rannetbauer</h4>
+
+
+
+<div id="overview" class="section level2">
+<h2>Overview</h2>
+<p>In addition to Model based Imputation Methods (see
+<code>vignette(&quot;modelImp&quot;)</code>) the <code>VIM</code> package also
+presents an iterative imputation method.</p>
+<p>This vignette showcases the function <code>irmi()</code>.
+<strong>IRMI</strong> is short for <strong>I</strong>terative
+<strong>R</strong>obust <strong>M</strong>odel-based
+<strong>I</strong>mputation. This method can be used to generate
+imputations for several variables in a dataset.</p>
+<p>Basically <code>irmi()</code> mimics the functionality of IVEWARE <a href="https://www.semanticscholar.org/paper/A-multivariate-technique-for-multiply-imputing-a-of-Raghunathan-Lepkowski/13b30e35b9a54dad07094cfe4f50d40ff15d8370?p2df">(Raghunathan
+et al., 2001)</a>, but there are several improvements with respect to
+the stability of the initialized values, or the robustness of the
+imputed values. In contrast to other imputation methods, the IRMI
+algorithm does not require at least one fully observed variable. In each
+step of the iteration, one variable is used as a response variable and
+the remaining variables serve as the regressors. Thus the “whole”
+multivariate information will be used for imputation in the response
+variable. For more details, please see <a href="http://file.statistik.tuwien.ac.at/filz/papers/CSDA11TKF.pdf">IRMI
+Imputation</a>.</p>
+</div>
+<div id="data" class="section level2">
+<h2>Data</h2>
+<p>The following example demonstrates the functionality of
+<code>irmi()</code> using a subset of <code>sleep</code>. The columns
+have been selected deliberately to include some interactions between the
+missing values.</p>
+<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(VIM)</span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>dataset <span class="ot">&lt;-</span> sleep[, <span class="fu">c</span>(<span class="st">&quot;Dream&quot;</span>, <span class="st">&quot;NonD&quot;</span>, <span class="st">&quot;BodyWgt&quot;</span>, <span class="st">&quot;Span&quot;</span>)]</span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>dataset<span class="sc">$</span>BodyWgt <span class="ot">&lt;-</span> <span class="fu">log</span>(dataset<span class="sc">$</span>BodyWgt)</span>
+<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>dataset<span class="sc">$</span>Span <span class="ot">&lt;-</span> <span class="fu">log</span>(dataset<span class="sc">$</span>Span)</span>
+<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="fu">aggr</span>(dataset)</span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+<p>The plot indicates several missing values in <code>Dream</code>,
+<code>NonD</code>, and <code>Span.</code></p>
+<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="fu">sapply</span>(dataset, <span class="cf">function</span>(x) <span class="fu">sum</span>(<span class="fu">is.na</span>(x)))</span>
+<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   Dream    NonD BodyWgt    Span </span></span>
+<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;      12      14       0       4</span></span></code></pre></div>
+</div>
+<div id="imputing-multiple-variables" class="section level2">
+<h2>Imputing multiple variables</h2>
+<p>The call of the function is straightforward and the algorithm usually
+converges in a few iterations.</p>
+<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>imp_irmi <span class="ot">&lt;-</span> <span class="fu">irmi</span>(dataset)</span>
+<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="fu">aggr</span>(imp_irmi, <span class="at">delimiter =</span> <span class="st">&quot;_imp&quot;</span>)</span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+<p>We can see that <code>irmi()</code> imputed all missing values for
+all variables in our dataset.</p>
+<div id="diagnosing-the-results" class="section level3">
+<h3>Diagnosing the results</h3>
+<p>As we can see in the next plot, for imputing missing values in
+<code>NonD</code> <code>Bodygt</code> plays an important role. The
+original data structure of <code>NonD</code> and <code>BodyWgt</code> is
+preserved by the <code>irmi()</code> imputation method.</p>
+<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>imp_irmi[, <span class="fu">c</span>(<span class="st">&quot;NonD&quot;</span>, <span class="st">&quot;BodyWgt&quot;</span>, <span class="st">&quot;NonD_imp&quot;</span>)] <span class="sc">|&gt;</span> </span>
+<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">marginplot</span>(<span class="at">delimiter =</span> <span class="st">&quot;_imp&quot;</span>)</span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+<p>The same is true for the data structure of <code>Span</code> and
+<code>BodyWgt</code>.</p>
+<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>imp_irmi[, <span class="fu">c</span>(<span class="st">&quot;Span&quot;</span>, <span class="st">&quot;BodyWgt&quot;</span>, <span class="st">&quot;Span_imp&quot;</span>)] <span class="sc">|&gt;</span> </span>
+<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">marginplot</span>(<span class="at">delimiter =</span> <span class="st">&quot;_imp&quot;</span>)</span></code></pre></div>
+<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
+</div>
+</div>
+<div id="performance-of-method" class="section level2">
+<h2>Performance of method</h2>
+<p>In order to validate the performance of <code>irmi()</code> and to
+highlight the ability to impute different datatypes the
+<code>iris</code> dataset is used. Firstly, some values are randomly set
+to <code>NA</code>.</p>
+<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(iris)</span>
+<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> iris</span>
+<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a><span class="fu">colnames</span>(df) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;S.Length&quot;</span>, <span class="st">&quot;S.Width&quot;</span>, <span class="st">&quot;P.Length&quot;</span>, <span class="st">&quot;P.Width&quot;</span>, <span class="st">&quot;Species&quot;</span>)</span>
+<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a><span class="co"># randomly produce some missing values in the data</span></span>
+<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">1</span>)</span>
+<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a>nbr_missing <span class="ot">&lt;-</span> <span class="dv">50</span></span>
+<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a>y <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">row =</span> <span class="fu">sample</span>(<span class="fu">nrow</span>(iris), <span class="at">size =</span> nbr_missing, <span class="at">replace =</span> <span class="cn">TRUE</span>),</span>
+<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a>                <span class="at">col =</span> <span class="fu">sample</span>(<span class="fu">ncol</span>(iris), <span class="at">size =</span> nbr_missing, <span class="at">replace =</span> <span class="cn">TRUE</span>))</span>
+<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a>y <span class="ot">&lt;-</span> y[<span class="sc">!</span><span class="fu">duplicated</span>(y), ]</span>
+<span id="cb6-10"><a href="#cb6-10" aria-hidden="true" tabindex="-1"></a>df[<span class="fu">as.matrix</span>(y)] <span class="ot">&lt;-</span> <span class="cn">NA</span></span>
+<span id="cb6-11"><a href="#cb6-11" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb6-12"><a href="#cb6-12" aria-hidden="true" tabindex="-1"></a><span class="fu">aggr</span>(df)</span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="fu">sapply</span>(df, <span class="cf">function</span>(x) <span class="fu">sum</span>(<span class="fu">is.na</span>(x)))</span>
+<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; S.Length  S.Width P.Length  P.Width  Species </span></span>
+<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;       10        9        8       10       12</span></span></code></pre></div>
+<p>We can see that there are missings in all variables and some
+observations reveal missing values on several points.</p>
+<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>imp_irmi <span class="ot">&lt;-</span> <span class="fu">irmi</span>(df)</span>
+<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="fu">aggr</span>(imp_irmi, <span class="at">delimiter =</span> <span class="st">&quot;imp&quot;</span>)</span></code></pre></div>
+<p><img 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style="display: block; margin: auto;" /></p>
+<p>The plot indicates that all missing values have been imputed by the
+IRMI algorithm. The following table displays the rounded first five
+results of the imputation for all variables.</p>
+<div class="reactable html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-683bf1b7f53031f6eeda" style="width:auto;height:auto;"></div>
+<script type="application/json" data-for="htmlwidget-683bf1b7f53031f6eeda">{"x":{"tag":{"name":"Reactable","attribs":{"data":{"TRUE1":["6.4","7","5.4","6.5","7.6"],"IMPUTED1":["6.95","6.41","6.27","6.55","7.77"],"TRUE2":["4.1","2.7","2.8","3.8","3.5"],"IMPUTED2":["3.04","2.7","2.53","3.62","3.35"],"TRUE3":["4.1","4.1","3.9","5.1","4.7"],"IMPUTED3":["4.17","4.42","4.52","5.27","4.87"],"TRUE4":["0.2","0.2","0.3","1.5","0.2"],"IMPUTED4":["0.16","0.15","0.5","1.38","0.15"],"TRUE5":["setosa","virginica","versicolor","versicolor","virginica"],"IMPUTED5":["setosa","virginica","versicolor","versicolor","virginica"]},"columns":[{"id":"TRUE1","name":"True","type":"character"},{"id":"IMPUTED1","name":"Imputed","type":"character"},{"id":"TRUE2","name":"True","type":"character"},{"id":"IMPUTED2","name":"Imputed","type":"character"},{"id":"TRUE3","name":"True","type":"character"},{"id":"IMPUTED3","name":"Imputed","type":"character"},{"id":"TRUE4","name":"True","type":"character"},{"id":"IMPUTED4","name":"Imputed","type":"character"},{"id":"TRUE5","name":"True","type":"character"},{"id":"IMPUTED5","name":"Imputed","type":"character"}],"columnGroups":[{"name":"S.Length","columns":["TRUE1","IMPUTED1"]},{"name":"S.Width","columns":["TRUE2","IMPUTED2"]},{"name":"P.Length","columns":["TRUE3","IMPUTED3"]},{"name":"P.Width","columns":["TRUE4","IMPUTED4"]},{"name":"Species","columns":["TRUE5","IMPUTED5"]}],"highlight":true,"bordered":true,"striped":true,"dataKey":"b025179563bc6770dffd4dfe4ebae857"},"children":[]},"class":"reactR_markup"},"evals":[],"jsHooks":[]}</script>
+</div>
+
+
+
+<!-- code folding -->
+
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+  (function () {
+    var script = document.createElement("script");
+    script.type = "text/javascript";
+    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+    document.getElementsByTagName("head")[0].appendChild(script);
+  })();
+</script>
+
+</body>
+</html>
diff --git a/inst/doc/modelImp.R b/inst/doc/modelImp.R
index 567817c..c495ab4 100644
--- a/inst/doc/modelImp.R
+++ b/inst/doc/modelImp.R
@@ -8,7 +8,6 @@ knitr::opts_chunk$set(
 
 ## ----setup, message = FALSE---------------------------------------------------
 library(VIM)
-library(magrittr)
 dataset <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")]
 dataset$BodyWgt <- log(dataset$BodyWgt)
 dataset$Span <- log(dataset$Span)
@@ -21,13 +20,13 @@ imp_ranger <- rangerImpute(NonD ~ BodyWgt + Span, dataset)
 aggr(imp_regression, delimiter = "_imp")
 
 ## ---- fig.height=5------------------------------------------------------------
-imp_regression[, c("NonD", "BodyWgt", "NonD_imp")] %>% 
+imp_regression[, c("NonD", "BodyWgt", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
 
 ## ---- fig.height=5------------------------------------------------------------
-imp_ranger[, c("NonD", "BodyWgt", "NonD_imp")] %>% 
+imp_ranger[, c("NonD", "BodyWgt", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
-imp_ranger[, c("NonD", "Span", "NonD_imp")] %>% 
+imp_ranger[, c("NonD", "Span", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
 
 ## -----------------------------------------------------------------------------
diff --git a/inst/doc/modelImp.Rmd b/inst/doc/modelImp.Rmd
index 5fb3eb1..6dc7dda 100644
--- a/inst/doc/modelImp.Rmd
+++ b/inst/doc/modelImp.Rmd
@@ -28,7 +28,6 @@ deliberately to include some interactions between the missing values.
 
 ```{r setup, message = FALSE}
 library(VIM)
-library(magrittr)
 dataset <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")]
 dataset$BodyWgt <- log(dataset$BodyWgt)
 dataset$Span <- log(dataset$Span)
@@ -61,7 +60,7 @@ As we can see in the next two plots, the correlation structure of `NonD` and
 suggests that the variable `Span` had little to no effect on the model.
 
 ```{r, fig.height=5}
-imp_regression[, c("NonD", "BodyWgt", "NonD_imp")] %>% 
+imp_regression[, c("NonD", "BodyWgt", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
 ```
 
@@ -69,9 +68,9 @@ For `rangerImpute()` on the other hand, `Span` played an important role in the
 generation of the imputed values.
 
 ```{r, fig.height=5}
-imp_ranger[, c("NonD", "BodyWgt", "NonD_imp")] %>% 
+imp_ranger[, c("NonD", "BodyWgt", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
-imp_ranger[, c("NonD", "Span", "NonD_imp")] %>% 
+imp_ranger[, c("NonD", "Span", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
 ```
 
diff --git a/inst/doc/modelImp.html b/inst/doc/modelImp.html
new file mode 100644
index 0000000..b2acb73
--- /dev/null
+++ b/inst/doc/modelImp.html
@@ -0,0 +1,2211 @@
+<!DOCTYPE html>
+
+<html>
+
+<head>
+
+<meta charset="utf-8" />
+<meta name="generator" content="pandoc" />
+<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
+
+<meta name="viewport" content="width=device-width, initial-scale=1" />
+
+<meta name="author" content="Gregor de Cillia" />
+
+
+<title>Model based Imputation Methods</title>
+
+<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+  var i, h, a;
+  for (i = 0; i < hs.length; i++) {
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+    a = h.attributes;
+    while (a.length > 0) h.removeAttribute(a[0].name);
+  }
+});
+</script>
+<script>/**
+ * core-js 2.6.11
+ * https://github.com/zloirock/core-js
+ * License: http://rock.mit-license.org
+ * © 2019 Denis Pushkarev
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+/***/ "./srcjs/input.js":
+/*!************************!*\
+  !*** ./srcjs/input.js ***!
+  \************************/
+/*! exports provided: reactShinyInput */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+__webpack_require__.r(__webpack_exports__);
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "reactShinyInput", function() { return reactShinyInput; });
+/* harmony import */ var react__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! react */ "react");
+/* harmony import */ var react__WEBPACK_IMPORTED_MODULE_0___default = /*#__PURE__*/__webpack_require__.n(react__WEBPACK_IMPORTED_MODULE_0__);
+/* harmony import */ var react_dom__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! react-dom */ "react-dom");
+/* harmony import */ var react_dom__WEBPACK_IMPORTED_MODULE_1___default = /*#__PURE__*/__webpack_require__.n(react_dom__WEBPACK_IMPORTED_MODULE_1__);
+/* harmony import */ var shiny__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! shiny */ "shiny");
+/* harmony import */ var shiny__WEBPACK_IMPORTED_MODULE_2___default = /*#__PURE__*/__webpack_require__.n(shiny__WEBPACK_IMPORTED_MODULE_2__);
+/* harmony import */ var jquery__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! jquery */ "jquery");
+/* harmony import */ var jquery__WEBPACK_IMPORTED_MODULE_3___default = /*#__PURE__*/__webpack_require__.n(jquery__WEBPACK_IMPORTED_MODULE_3__);
+function _typeof(obj) { "@babel/helpers - typeof"; if (typeof Symbol === "function" && typeof Symbol.iterator === "symbol") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }; } return _typeof(obj); }
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+function _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }
+
+function _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }
+
+function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }
+
+function _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }
+
+function _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }
+
+function _possibleConstructorReturn(self, call) { if (call && (_typeof(call) === "object" || typeof call === "function")) { return call; } return _assertThisInitialized(self); }
+
+function _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return self; }
+
+function _isNativeReflectConstruct() { if (typeof Reflect === "undefined" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === "function") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }
+
+function _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }
+
+
+
+
+
+/*
+ * This default receiveMessage implementation expects data to contain whole
+ * configuration and value properties. If either is present, it will be set and
+ * the component will be re-rendered. Because receiveMessage is typically used
+ * by input authors to perform incremental updates, this default implementation
+ * can be overriden by the user with the receiveMessage arguments to
+ * reactShinyInput.
+ */
+
+function defaultReceiveMessage(el, _ref) {
+  var configuration = _ref.configuration,
+      value = _ref.value;
+  var dirty = false;
+
+  if (configuration !== undefined) {
+    this.setInputConfiguration(el, configuration);
+    dirty = true;
+  }
+
+  if (value !== undefined) {
+    this.setInputValue(el, value);
+    dirty = true;
+  }
+
+  if (dirty) {
+    this.getCallback(el)();
+    this.render(el);
+  }
+}
+
+var defaultOptions = {
+  receiveMessage: defaultReceiveMessage,
+  type: false,
+  ratePolicy: null
+};
+/**
+ * Installs a new Shiny input binding based on a React component.
+ *
+ * @param {string} selector - jQuery selector that should identify the set of
+ * container elements within the scope argument of Shiny.InputBinding.find.
+ * @param {string} name - A name such as 'acme.FooInput' that should uniquely
+ * identify the component.
+ * @param {Object} component - React Component, either class or function.
+ * @param {Object} options - Additional configuration options. Supported
+ * options are:
+ * - receiveMessage: Implementation of Shiny.InputBinding to use in place of
+ *   the default. Typically overridden as an optimization to perform
+ *   incremental value updates.
+ * - type: `false`, a string, or a function.
+ *     - `false` (the default): denotes that the value produced by this input
+ *       should not be intercepted by any handlers registered in R on the
+ *       server using shiny::registerInputHandler().
+ *     - string: denotes the input's *type* and should correspond to the
+ *       type parameter of shiny::registerInputHandler().
+ *     - function: A function called with `this` bound to the InputBinding
+ *       instance and passed a single argument, the input's containing DOM
+ *       element. The function should return either `false` or a string
+ *       corresponding to the type parameter of shiny::registerInputHandler().
+ * - ratePolicy: A rate policy object as defined in the documentation for
+ *     getRatePolicy(): https://shiny.rstudio.com/articles/building-inputs.html
+ *     A rate policy object has two members:
+ *     - `policy`: Valid values are the strings "direct", "debounce", and
+ *       "throttle". "direct" means that all events are sent immediately.
+ *     - `delay`: Number indicating the number of milliseconds that should be
+ *       used when debouncing or throttling. Has no effect if the policy is
+ *       direct.
+ *     The specified rate policy is only applied when `true` is passed as the
+ *     second argument to the `setValue` function passed as a prop to the
+ *     input component.
+ *
+ */
+
+function reactShinyInput(selector, name, component, options) {
+  options = Object.assign({}, defaultOptions, options);
+  shiny__WEBPACK_IMPORTED_MODULE_2___default.a.inputBindings.register(new ( /*#__PURE__*/function (_Shiny$InputBinding) {
+    _inherits(_class, _Shiny$InputBinding);
+
+    var _super = _createSuper(_class);
+
+    function _class() {
+      _classCallCheck(this, _class);
+
+      return _super.apply(this, arguments);
+    }
+
+    _createClass(_class, [{
+      key: "find",
+
+      /*
+       * Methods override those in Shiny.InputBinding
+       */
+      value: function find(scope) {
+        return jquery__WEBPACK_IMPORTED_MODULE_3___default()(scope).find(selector);
+      }
+    }, {
+      key: "getValue",
+      value: function getValue(el) {
+        return this.getInputValue(el);
+      }
+    }, {
+      key: "setValue",
+      value: function setValue(el, value) {
+        var rateLimited = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false;
+
+        /*
+         * We have to check whether $(el).data('callback') is undefined here
+         * in case shiny::renderUI() is involved. If an input is contained in a
+         * shiny::uiOutput(), the following strange thing happens occasionally:
+         *
+         *   1. setValue() is bound to an el in this.render(), below
+         *   2. An event that will call setValue() is enqueued
+         *   3. While the event is still enqueued, el is unbound and removed
+         *      from the DOM by the JS code associated with shiny::uiOutput()
+         *      - That code uses jQuery .html() in output_binding_html.js
+         *      - .html() removes el from the DOM and clears ist data and events
+         *   4. By the time the setValue() bound to the original el is invoked,
+         *      el has been unbound and its data cleared.
+         *
+         *  Since the original input is gone along with its callback, it
+         *  seems to make the most sense to do nothing.
+         */
+        if (jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('callback') !== undefined) {
+          this.setInputValue(el, value);
+          this.getCallback(el)(rateLimited);
+          this.render(el);
+        }
+      }
+    }, {
+      key: "initialize",
+      value: function initialize(el) {
+        jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('value', JSON.parse(jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).next().text()));
+        jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('configuration', JSON.parse(jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).next().next().text()));
+      }
+    }, {
+      key: "subscribe",
+      value: function subscribe(el, callback) {
+        jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('callback', callback);
+        this.render(el);
+      }
+    }, {
+      key: "unsubscribe",
+      value: function unsubscribe(el) {
+        react_dom__WEBPACK_IMPORTED_MODULE_1___default.a.render(null, el);
+      }
+    }, {
+      key: "receiveMessage",
+      value: function receiveMessage(el, data) {
+        options.receiveMessage.call(this, el, data);
+      }
+    }, {
+      key: "getType",
+      value: function getType(el) {
+        if (typeof options.type === 'function') {
+          return options.type.call(this, el);
+        } else if (options.type === false || typeof options.type === 'string') {
+          return options.type;
+        } else {
+          throw new Error('options.type must be false, a string, or a function');
+        }
+      }
+    }, {
+      key: "getRatePolicy",
+      value: function getRatePolicy() {
+        return options.ratePolicy;
+      }
+      /*
+       * Methods not present in Shiny.InputBinding but accessible to users
+       * through `this` in receiveMessage
+       */
+
+    }, {
+      key: "getInputValue",
+      value: function getInputValue(el) {
+        return jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('value');
+      }
+    }, {
+      key: "setInputValue",
+      value: function setInputValue(el, value) {
+        jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('value', value);
+      }
+    }, {
+      key: "getInputConfiguration",
+      value: function getInputConfiguration(el) {
+        return jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('configuration');
+      }
+    }, {
+      key: "setInputConfiguration",
+      value: function setInputConfiguration(el, configuration) {
+        jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('configuration', configuration);
+      }
+    }, {
+      key: "getCallback",
+      value: function getCallback(el) {
+        return jquery__WEBPACK_IMPORTED_MODULE_3___default()(el).data('callback');
+      }
+    }, {
+      key: "render",
+      value: function render(el) {
+        var element = /*#__PURE__*/react__WEBPACK_IMPORTED_MODULE_0___default.a.createElement(component, {
+          configuration: this.getInputConfiguration(el),
+          value: this.getValue(el),
+          setValue: this.setValue.bind(this, el),
+          el: el
+        });
+        react_dom__WEBPACK_IMPORTED_MODULE_1___default.a.render(element, el);
+      }
+    }]);
+
+    return _class;
+  }(shiny__WEBPACK_IMPORTED_MODULE_2___default.a.InputBinding))(), name);
+}
+
+/***/ }),
+
+/***/ "./srcjs/react-tools.js":
+/*!******************************!*\
+  !*** ./srcjs/react-tools.js ***!
+  \******************************/
+/*! no exports provided */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+__webpack_require__.r(__webpack_exports__);
+/* harmony import */ var _widget__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./widget */ "./srcjs/widget.js");
+/* harmony import */ var _input__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./input */ "./srcjs/input.js");
+
+
+window.reactR = {
+  reactShinyInput: _input__WEBPACK_IMPORTED_MODULE_1__["reactShinyInput"],
+  reactWidget: _widget__WEBPACK_IMPORTED_MODULE_0__["reactWidget"],
+  hydrate: _widget__WEBPACK_IMPORTED_MODULE_0__["hydrate"]
+};
+
+/***/ }),
+
+/***/ "./srcjs/widget.js":
+/*!*************************!*\
+  !*** ./srcjs/widget.js ***!
+  \*************************/
+/*! exports provided: hydrate, defaultOptions, mergeOptions, formatDimension, isTag, reactWidget */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+__webpack_require__.r(__webpack_exports__);
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "hydrate", function() { return hydrate; });
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "defaultOptions", function() { return defaultOptions; });
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "mergeOptions", function() { return mergeOptions; });
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "formatDimension", function() { return formatDimension; });
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "isTag", function() { return isTag; });
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "reactWidget", function() { return reactWidget; });
+function _typeof(obj) { "@babel/helpers - typeof"; if (typeof Symbol === "function" && typeof Symbol.iterator === "symbol") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }; } return _typeof(obj); }
+
+/**
+ * Recursively transforms tag, a JSON representation of an instance of a
+ * React component and its children, into a React element suitable for
+ * passing to ReactDOM.render.
+ * @param {Object} components
+ * @param {Object} tag
+ */
+function hydrate(components, tag) {
+  if (typeof tag === 'string') return tag;
+
+  if (tag.name[0] === tag.name[0].toUpperCase() && !components.hasOwnProperty(tag.name)) {
+    throw new Error("Unknown component: " + tag.name);
+  }
+
+  var elem = components.hasOwnProperty(tag.name) ? components[tag.name] : tag.name,
+      args = [elem, tag.attribs];
+
+  for (var i = 0; i < tag.children.length; i++) {
+    args.push(hydrate(components, tag.children[i]));
+  }
+
+  return React.createElement.apply(React, args);
+}
+var defaultOptions = {
+  // The name of the property on the root tag to use for the width, if
+  // it's updated.
+  widthProperty: "width",
+  // The name of the property on the root tag to use for the height, if
+  // it's updated.
+  heightProperty: "height",
+  // Whether or not to append the string 'px' to the width and height
+  // properties when they change.
+  appendPx: false,
+  // Whether or not to dynamically update the width and height properties
+  // of the last known tag when the computed width and height change in
+  // the browser.
+  renderOnResize: false
+};
+function mergeOptions(options) {
+  var merged = {};
+
+  for (var k in defaultOptions) {
+    merged[k] = defaultOptions[k];
+  }
+
+  for (var k in options) {
+    if (!defaultOptions.hasOwnProperty(k)) {
+      throw new Error("Unrecognized option: " + k);
+    }
+
+    merged[k] = options[k];
+  }
+
+  return merged;
+}
+function formatDimension(dim, options) {
+  if (options.appendPx) {
+    return dim + 'px';
+  } else {
+    return dim;
+  }
+}
+function isTag(value) {
+  return _typeof(value) === 'object' && value.hasOwnProperty('name') && value.hasOwnProperty('attribs') && value.hasOwnProperty('children');
+}
+/**
+ * Creates an HTMLWidget that is updated by rendering a React component.
+ * React component constructors are made available by specifying them by
+ * name in the components object.
+ * @param {string} name
+ * @param {string} type
+ * @param {Object} components
+ * @param {Object} options
+ */
+
+function reactWidget(name, type, components, options) {
+  var actualOptions = mergeOptions(options);
+  window.HTMLWidgets.widget({
+    name: name,
+    type: type,
+    factory: function factory(el, width, height) {
+      var lastValue,
+          instance = {},
+          renderValue = function renderValue(value) {
+        if (actualOptions.renderOnResize) {
+          // value.tag might be a primitive string, in which
+          // case there is no attribs property.
+          if (_typeof(value.tag) === 'object') {
+            value.tag.attribs[actualOptions["widthProperty"]] = formatDimension(width);
+            value.tag.attribs[actualOptions["heightProperty"]] = formatDimension(height);
+          }
+
+          lastValue = value;
+        } // with functional stateless components this will be null
+        // see https://reactjs.org/docs/react-dom.html#render for more details
+
+
+        this.instance.component = ReactDOM.render(hydrate(components, value.tag), el);
+      };
+
+      return {
+        instance: instance,
+        renderValue: renderValue,
+        resize: function resize(newWidth, newHeight) {
+          if (actualOptions.renderOnResize) {
+            width = newWidth;
+            height = newHeight;
+            renderValue(lastValue);
+          }
+        }
+      };
+    }
+  });
+}
+
+/***/ }),
+
+/***/ "jquery":
+/*!********************************!*\
+  !*** external "window.jQuery" ***!
+  \********************************/
+/*! no static exports found */
+/***/ (function(module, exports) {
+
+module.exports = window.jQuery;
+
+/***/ }),
+
+/***/ "react":
+/*!*******************************!*\
+  !*** external "window.React" ***!
+  \*******************************/
+/*! no static exports found */
+/***/ (function(module, exports) {
+
+module.exports = window.React;
+
+/***/ }),
+
+/***/ "react-dom":
+/*!**********************************!*\
+  !*** external "window.ReactDOM" ***!
+  \**********************************/
+/*! no static exports found */
+/***/ (function(module, exports) {
+
+module.exports = window.ReactDOM;
+
+/***/ }),
+
+/***/ "shiny":
+/*!*******************************!*\
+  !*** external "window.Shiny" ***!
+  \*******************************/
+/*! no static exports found */
+/***/ (function(module, exports) {
+
+module.exports = window.Shiny;
+
+/***/ })
+
+/******/ });
+//# sourceMappingURL=react-tools.js.map</script>
+<script>(function() {
+  // If window.HTMLWidgets is already defined, then use it; otherwise create a
+  // new object. This allows preceding code to set options that affect the
+  // initialization process (though none currently exist).
+  window.HTMLWidgets = window.HTMLWidgets || {};
+
+  // See if we're running in a viewer pane. If not, we're in a web browser.
+  var viewerMode = window.HTMLWidgets.viewerMode =
+      /\bviewer_pane=1\b/.test(window.location);
+
+  // See if we're running in Shiny mode. If not, it's a static document.
+  // Note that static widgets can appear in both Shiny and static modes, but
+  // obviously, Shiny widgets can only appear in Shiny apps/documents.
+  var shinyMode = window.HTMLWidgets.shinyMode =
+      typeof(window.Shiny) !== "undefined" && !!window.Shiny.outputBindings;
+
+  // We can't count on jQuery being available, so we implement our own
+  // version if necessary.
+  function querySelectorAll(scope, selector) {
+    if (typeof(jQuery) !== "undefined" && scope instanceof jQuery) {
+      return scope.find(selector);
+    }
+    if (scope.querySelectorAll) {
+      return scope.querySelectorAll(selector);
+    }
+  }
+
+  function asArray(value) {
+    if (value === null)
+      return [];
+    if ($.isArray(value))
+      return value;
+    return [value];
+  }
+
+  // Implement jQuery's extend
+  function extend(target /*, ... */) {
+    if (arguments.length == 1) {
+      return target;
+    }
+    for (var i = 1; i < arguments.length; i++) {
+      var source = arguments[i];
+      for (var prop in source) {
+        if (source.hasOwnProperty(prop)) {
+          target[prop] = source[prop];
+        }
+      }
+    }
+    return target;
+  }
+
+  // IE8 doesn't support Array.forEach.
+  function forEach(values, callback, thisArg) {
+    if (values.forEach) {
+      values.forEach(callback, thisArg);
+    } else {
+      for (var i = 0; i < values.length; i++) {
+        callback.call(thisArg, values[i], i, values);
+      }
+    }
+  }
+
+  // Replaces the specified method with the return value of funcSource.
+  //
+  // Note that funcSource should not BE the new method, it should be a function
+  // that RETURNS the new method. funcSource receives a single argument that is
+  // the overridden method, it can be called from the new method. The overridden
+  // method can be called like a regular function, it has the target permanently
+  // bound to it so "this" will work correctly.
+  function overrideMethod(target, methodName, funcSource) {
+    var superFunc = target[methodName] || function() {};
+    var superFuncBound = function() {
+      return superFunc.apply(target, arguments);
+    };
+    target[methodName] = funcSource(superFuncBound);
+  }
+
+  // Add a method to delegator that, when invoked, calls
+  // delegatee.methodName. If there is no such method on
+  // the delegatee, but there was one on delegator before
+  // delegateMethod was called, then the original version
+  // is invoked instead.
+  // For example:
+  //
+  // var a = {
+  //   method1: function() { console.log('a1'); }
+  //   method2: function() { console.log('a2'); }
+  // };
+  // var b = {
+  //   method1: function() { console.log('b1'); }
+  // };
+  // delegateMethod(a, b, "method1");
+  // delegateMethod(a, b, "method2");
+  // a.method1();
+  // a.method2();
+  //
+  // The output would be "b1", "a2".
+  function delegateMethod(delegator, delegatee, methodName) {
+    var inherited = delegator[methodName];
+    delegator[methodName] = function() {
+      var target = delegatee;
+      var method = delegatee[methodName];
+
+      // The method doesn't exist on the delegatee. Instead,
+      // call the method on the delegator, if it exists.
+      if (!method) {
+        target = delegator;
+        method = inherited;
+      }
+
+      if (method) {
+        return method.apply(target, arguments);
+      }
+    };
+  }
+
+  // Implement a vague facsimilie of jQuery's data method
+  function elementData(el, name, value) {
+    if (arguments.length == 2) {
+      return el["htmlwidget_data_" + name];
+    } else if (arguments.length == 3) {
+      el["htmlwidget_data_" + name] = value;
+      return el;
+    } else {
+      throw new Error("Wrong number of arguments for elementData: " +
+        arguments.length);
+    }
+  }
+
+  // http://stackoverflow.com/questions/3446170/escape-string-for-use-in-javascript-regex
+  function escapeRegExp(str) {
+    return str.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g, "\\$&");
+  }
+
+  function hasClass(el, className) {
+    var re = new RegExp("\\b" + escapeRegExp(className) + "\\b");
+    return re.test(el.className);
+  }
+
+  // elements - array (or array-like object) of HTML elements
+  // className - class name to test for
+  // include - if true, only return elements with given className;
+  //   if false, only return elements *without* given className
+  function filterByClass(elements, className, include) {
+    var results = [];
+    for (var i = 0; i < elements.length; i++) {
+      if (hasClass(elements[i], className) == include)
+        results.push(elements[i]);
+    }
+    return results;
+  }
+
+  function on(obj, eventName, func) {
+    if (obj.addEventListener) {
+      obj.addEventListener(eventName, func, false);
+    } else if (obj.attachEvent) {
+      obj.attachEvent(eventName, func);
+    }
+  }
+
+  function off(obj, eventName, func) {
+    if (obj.removeEventListener)
+      obj.removeEventListener(eventName, func, false);
+    else if (obj.detachEvent) {
+      obj.detachEvent(eventName, func);
+    }
+  }
+
+  // Translate array of values to top/right/bottom/left, as usual with
+  // the "padding" CSS property
+  // https://developer.mozilla.org/en-US/docs/Web/CSS/padding
+  function unpackPadding(value) {
+    if (typeof(value) === "number")
+      value = [value];
+    if (value.length === 1) {
+      return {top: value[0], right: value[0], bottom: value[0], left: value[0]};
+    }
+    if (value.length === 2) {
+      return {top: value[0], right: value[1], bottom: value[0], left: value[1]};
+    }
+    if (value.length === 3) {
+      return {top: value[0], right: value[1], bottom: value[2], left: value[1]};
+    }
+    if (value.length === 4) {
+      return {top: value[0], right: value[1], bottom: value[2], left: value[3]};
+    }
+  }
+
+  // Convert an unpacked padding object to a CSS value
+  function paddingToCss(paddingObj) {
+    return paddingObj.top + "px " + paddingObj.right + "px " + paddingObj.bottom + "px " + paddingObj.left + "px";
+  }
+
+  // Makes a number suitable for CSS
+  function px(x) {
+    if (typeof(x) === "number")
+      return x + "px";
+    else
+      return x;
+  }
+
+  // Retrieves runtime widget sizing information for an element.
+  // The return value is either null, or an object with fill, padding,
+  // defaultWidth, defaultHeight fields.
+  function sizingPolicy(el) {
+    var sizingEl = document.querySelector("script[data-for='" + el.id + "'][type='application/htmlwidget-sizing']");
+    if (!sizingEl)
+      return null;
+    var sp = JSON.parse(sizingEl.textContent || sizingEl.text || "{}");
+    if (viewerMode) {
+      return sp.viewer;
+    } else {
+      return sp.browser;
+    }
+  }
+
+  // @param tasks Array of strings (or falsy value, in which case no-op).
+  //   Each element must be a valid JavaScript expression that yields a
+  //   function. Or, can be an array of objects with "code" and "data"
+  //   properties; in this case, the "code" property should be a string
+  //   of JS that's an expr that yields a function, and "data" should be
+  //   an object that will be added as an additional argument when that
+  //   function is called.
+  // @param target The object that will be "this" for each function
+  //   execution.
+  // @param args Array of arguments to be passed to the functions. (The
+  //   same arguments will be passed to all functions.)
+  function evalAndRun(tasks, target, args) {
+    if (tasks) {
+      forEach(tasks, function(task) {
+        var theseArgs = args;
+        if (typeof(task) === "object") {
+          theseArgs = theseArgs.concat([task.data]);
+          task = task.code;
+        }
+        var taskFunc = tryEval(task);
+        if (typeof(taskFunc) !== "function") {
+          throw new Error("Task must be a function! Source:\n" + task);
+        }
+        taskFunc.apply(target, theseArgs);
+      });
+    }
+  }
+
+  // Attempt eval() both with and without enclosing in parentheses.
+  // Note that enclosing coerces a function declaration into
+  // an expression that eval() can parse
+  // (otherwise, a SyntaxError is thrown)
+  function tryEval(code) {
+    var result = null;
+    try {
+      result = eval("(" + code + ")");
+    } catch(error) {
+      if (!(error instanceof SyntaxError)) {
+        throw error;
+      }
+      try {
+        result = eval(code);
+      } catch(e) {
+        if (e instanceof SyntaxError) {
+          throw error;
+        } else {
+          throw e;
+        }
+      }
+    }
+    return result;
+  }
+
+  function initSizing(el) {
+    var sizing = sizingPolicy(el);
+    if (!sizing)
+      return;
+
+    var cel = document.getElementById("htmlwidget_container");
+    if (!cel)
+      return;
+
+    if (typeof(sizing.padding) !== "undefined") {
+      document.body.style.margin = "0";
+      document.body.style.padding = paddingToCss(unpackPadding(sizing.padding));
+    }
+
+    if (sizing.fill) {
+      document.body.style.overflow = "hidden";
+      document.body.style.width = "100%";
+      document.body.style.height = "100%";
+      document.documentElement.style.width = "100%";
+      document.documentElement.style.height = "100%";
+      cel.style.position = "absolute";
+      var pad = unpackPadding(sizing.padding);
+      cel.style.top = pad.top + "px";
+      cel.style.right = pad.right + "px";
+      cel.style.bottom = pad.bottom + "px";
+      cel.style.left = pad.left + "px";
+      el.style.width = "100%";
+      el.style.height = "100%";
+
+      return {
+        getWidth: function() { return cel.getBoundingClientRect().width; },
+        getHeight: function() { return cel.getBoundingClientRect().height; }
+      };
+
+    } else {
+      el.style.width = px(sizing.width);
+      el.style.height = px(sizing.height);
+
+      return {
+        getWidth: function() { return cel.getBoundingClientRect().width; },
+        getHeight: function() { return cel.getBoundingClientRect().height; }
+      };
+    }
+  }
+
+  // Default implementations for methods
+  var defaults = {
+    find: function(scope) {
+      return querySelectorAll(scope, "." + this.name);
+    },
+    renderError: function(el, err) {
+      var $el = $(el);
+
+      this.clearError(el);
+
+      // Add all these error classes, as Shiny does
+      var errClass = "shiny-output-error";
+      if (err.type !== null) {
+        // use the classes of the error condition as CSS class names
+        errClass = errClass + " " + $.map(asArray(err.type), function(type) {
+          return errClass + "-" + type;
+        }).join(" ");
+      }
+      errClass = errClass + " htmlwidgets-error";
+
+      // Is el inline or block? If inline or inline-block, just display:none it
+      // and add an inline error.
+      var display = $el.css("display");
+      $el.data("restore-display-mode", display);
+
+      if (display === "inline" || display === "inline-block") {
+        $el.hide();
+        if (err.message !== "") {
+          var errorSpan = $("<span>").addClass(errClass);
+          errorSpan.text(err.message);
+          $el.after(errorSpan);
+        }
+      } else if (display === "block") {
+        // If block, add an error just after the el, set visibility:none on the
+        // el, and position the error to be on top of the el.
+        // Mark it with a unique ID and CSS class so we can remove it later.
+        $el.css("visibility", "hidden");
+        if (err.message !== "") {
+          var errorDiv = $("<div>").addClass(errClass).css("position", "absolute")
+            .css("top", el.offsetTop)
+            .css("left", el.offsetLeft)
+            // setting width can push out the page size, forcing otherwise
+            // unnecessary scrollbars to appear and making it impossible for
+            // the element to shrink; so use max-width instead
+            .css("maxWidth", el.offsetWidth)
+            .css("height", el.offsetHeight);
+          errorDiv.text(err.message);
+          $el.after(errorDiv);
+
+          // Really dumb way to keep the size/position of the error in sync with
+          // the parent element as the window is resized or whatever.
+          var intId = setInterval(function() {
+            if (!errorDiv[0].parentElement) {
+              clearInterval(intId);
+              return;
+            }
+            errorDiv
+              .css("top", el.offsetTop)
+              .css("left", el.offsetLeft)
+              .css("maxWidth", el.offsetWidth)
+              .css("height", el.offsetHeight);
+          }, 500);
+        }
+      }
+    },
+    clearError: function(el) {
+      var $el = $(el);
+      var display = $el.data("restore-display-mode");
+      $el.data("restore-display-mode", null);
+
+      if (display === "inline" || display === "inline-block") {
+        if (display)
+          $el.css("display", display);
+        $(el.nextSibling).filter(".htmlwidgets-error").remove();
+      } else if (display === "block"){
+        $el.css("visibility", "inherit");
+        $(el.nextSibling).filter(".htmlwidgets-error").remove();
+      }
+    },
+    sizing: {}
+  };
+
+  // Called by widget bindings to register a new type of widget. The definition
+  // object can contain the following properties:
+  // - name (required) - A string indicating the binding name, which will be
+  //   used by default as the CSS classname to look for.
+  // - initialize (optional) - A function(el) that will be called once per
+  //   widget element; if a value is returned, it will be passed as the third
+  //   value to renderValue.
+  // - renderValue (required) - A function(el, data, initValue) that will be
+  //   called with data. Static contexts will cause this to be called once per
+  //   element; Shiny apps will cause this to be called multiple times per
+  //   element, as the data changes.
+  window.HTMLWidgets.widget = function(definition) {
+    if (!definition.name) {
+      throw new Error("Widget must have a name");
+    }
+    if (!definition.type) {
+      throw new Error("Widget must have a type");
+    }
+    // Currently we only support output widgets
+    if (definition.type !== "output") {
+      throw new Error("Unrecognized widget type '" + definition.type + "'");
+    }
+    // TODO: Verify that .name is a valid CSS classname
+
+    // Support new-style instance-bound definitions. Old-style class-bound
+    // definitions have one widget "object" per widget per type/class of
+    // widget; the renderValue and resize methods on such widget objects
+    // take el and instance arguments, because the widget object can't
+    // store them. New-style instance-bound definitions have one widget
+    // object per widget instance; the definition that's passed in doesn't
+    // provide renderValue or resize methods at all, just the single method
+    //   factory(el, width, height)
+    // which returns an object that has renderValue(x) and resize(w, h).
+    // This enables a far more natural programming style for the widget
+    // author, who can store per-instance state using either OO-style
+    // instance fields or functional-style closure variables (I guess this
+    // is in contrast to what can only be called C-style pseudo-OO which is
+    // what we required before).
+    if (definition.factory) {
+      definition = createLegacyDefinitionAdapter(definition);
+    }
+
+    if (!definition.renderValue) {
+      throw new Error("Widget must have a renderValue function");
+    }
+
+    // For static rendering (non-Shiny), use a simple widget registration
+    // scheme. We also use this scheme for Shiny apps/documents that also
+    // contain static widgets.
+    window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];
+    // Merge defaults into the definition; don't mutate the original definition.
+    var staticBinding = extend({}, defaults, definition);
+    overrideMethod(staticBinding, "find", function(superfunc) {
+      return function(scope) {
+        var results = superfunc(scope);
+        // Filter out Shiny outputs, we only want the static kind
+        return filterByClass(results, "html-widget-output", false);
+      };
+    });
+    window.HTMLWidgets.widgets.push(staticBinding);
+
+    if (shinyMode) {
+      // Shiny is running. Register the definition with an output binding.
+      // The definition itself will not be the output binding, instead
+      // we will make an output binding object that delegates to the
+      // definition. This is because we foolishly used the same method
+      // name (renderValue) for htmlwidgets definition and Shiny bindings
+      // but they actually have quite different semantics (the Shiny
+      // bindings receive data that includes lots of metadata that it
+      // strips off before calling htmlwidgets renderValue). We can't
+      // just ignore the difference because in some widgets it's helpful
+      // to call this.renderValue() from inside of resize(), and if
+      // we're not delegating, then that call will go to the Shiny
+      // version instead of the htmlwidgets version.
+
+      // Merge defaults with definition, without mutating either.
+      var bindingDef = extend({}, defaults, definition);
+
+      // This object will be our actual Shiny binding.
+      var shinyBinding = new Shiny.OutputBinding();
+
+      // With a few exceptions, we'll want to simply use the bindingDef's
+      // version of methods if they are available, otherwise fall back to
+      // Shiny's defaults. NOTE: If Shiny's output bindings gain additional
+      // methods in the future, and we want them to be overrideable by
+      // HTMLWidget binding definitions, then we'll need to add them to this
+      // list.
+      delegateMethod(shinyBinding, bindingDef, "getId");
+      delegateMethod(shinyBinding, bindingDef, "onValueChange");
+      delegateMethod(shinyBinding, bindingDef, "onValueError");
+      delegateMethod(shinyBinding, bindingDef, "renderError");
+      delegateMethod(shinyBinding, bindingDef, "clearError");
+      delegateMethod(shinyBinding, bindingDef, "showProgress");
+
+      // The find, renderValue, and resize are handled differently, because we
+      // want to actually decorate the behavior of the bindingDef methods.
+
+      shinyBinding.find = function(scope) {
+        var results = bindingDef.find(scope);
+
+        // Only return elements that are Shiny outputs, not static ones
+        var dynamicResults = results.filter(".html-widget-output");
+
+        // It's possible that whatever caused Shiny to think there might be
+        // new dynamic outputs, also caused there to be new static outputs.
+        // Since there might be lots of different htmlwidgets bindings, we
+        // schedule execution for later--no need to staticRender multiple
+        // times.
+        if (results.length !== dynamicResults.length)
+          scheduleStaticRender();
+
+        return dynamicResults;
+      };
+
+      // Wrap renderValue to handle initialization, which unfortunately isn't
+      // supported natively by Shiny at the time of this writing.
+
+      shinyBinding.renderValue = function(el, data) {
+        Shiny.renderDependencies(data.deps);
+        // Resolve strings marked as javascript literals to objects
+        if (!(data.evals instanceof Array)) data.evals = [data.evals];
+        for (var i = 0; data.evals && i < data.evals.length; i++) {
+          window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);
+        }
+        if (!bindingDef.renderOnNullValue) {
+          if (data.x === null) {
+            el.style.visibility = "hidden";
+            return;
+          } else {
+            el.style.visibility = "inherit";
+          }
+        }
+        if (!elementData(el, "initialized")) {
+          initSizing(el);
+
+          elementData(el, "initialized", true);
+          if (bindingDef.initialize) {
+            var rect = el.getBoundingClientRect();
+            var result = bindingDef.initialize(el, rect.width, rect.height);
+            elementData(el, "init_result", result);
+          }
+        }
+        bindingDef.renderValue(el, data.x, elementData(el, "init_result"));
+        evalAndRun(data.jsHooks.render, elementData(el, "init_result"), [el, data.x]);
+      };
+
+      // Only override resize if bindingDef implements it
+      if (bindingDef.resize) {
+        shinyBinding.resize = function(el, width, height) {
+          // Shiny can call resize before initialize/renderValue have been
+          // called, which doesn't make sense for widgets.
+          if (elementData(el, "initialized")) {
+            bindingDef.resize(el, width, height, elementData(el, "init_result"));
+          }
+        };
+      }
+
+      Shiny.outputBindings.register(shinyBinding, bindingDef.name);
+    }
+  };
+
+  var scheduleStaticRenderTimerId = null;
+  function scheduleStaticRender() {
+    if (!scheduleStaticRenderTimerId) {
+      scheduleStaticRenderTimerId = setTimeout(function() {
+        scheduleStaticRenderTimerId = null;
+        window.HTMLWidgets.staticRender();
+      }, 1);
+    }
+  }
+
+  // Render static widgets after the document finishes loading
+  // Statically render all elements that are of this widget's class
+  window.HTMLWidgets.staticRender = function() {
+    var bindings = window.HTMLWidgets.widgets || [];
+    forEach(bindings, function(binding) {
+      var matches = binding.find(document.documentElement);
+      forEach(matches, function(el) {
+        var sizeObj = initSizing(el, binding);
+
+        var getSize = function(el) {
+          if (sizeObj) {
+            return {w: sizeObj.getWidth(), h: sizeObj.getHeight()}
+          } else {
+            var rect = el.getBoundingClientRect();
+            return {w: rect.width, h: rect.height}
+          }
+        };
+
+        if (hasClass(el, "html-widget-static-bound"))
+          return;
+        el.className = el.className + " html-widget-static-bound";
+
+        var initResult;
+        if (binding.initialize) {
+          var size = getSize(el);
+          initResult = binding.initialize(el, size.w, size.h);
+          elementData(el, "init_result", initResult);
+        }
+
+        if (binding.resize) {
+          var lastSize = getSize(el);
+          var resizeHandler = function(e) {
+            var size = getSize(el);
+            if (size.w === 0 && size.h === 0)
+              return;
+            if (size.w === lastSize.w && size.h === lastSize.h)
+              return;
+            lastSize = size;
+            binding.resize(el, size.w, size.h, initResult);
+          };
+
+          on(window, "resize", resizeHandler);
+
+          // This is needed for cases where we're running in a Shiny
+          // app, but the widget itself is not a Shiny output, but
+          // rather a simple static widget. One example of this is
+          // an rmarkdown document that has runtime:shiny and widget
+          // that isn't in a render function. Shiny only knows to
+          // call resize handlers for Shiny outputs, not for static
+          // widgets, so we do it ourselves.
+          if (window.jQuery) {
+            window.jQuery(document).on(
+              "shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets",
+              resizeHandler
+            );
+            window.jQuery(document).on(
+              "hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets",
+              resizeHandler
+            );
+          }
+
+          // This is needed for the specific case of ioslides, which
+          // flips slides between display:none and display:block.
+          // Ideally we would not have to have ioslide-specific code
+          // here, but rather have ioslides raise a generic event,
+          // but the rmarkdown package just went to CRAN so the
+          // window to getting that fixed may be long.
+          if (window.addEventListener) {
+            // It's OK to limit this to window.addEventListener
+            // browsers because ioslides itself only supports
+            // such browsers.
+            on(document, "slideenter", resizeHandler);
+            on(document, "slideleave", resizeHandler);
+          }
+        }
+
+        var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']");
+        if (scriptData) {
+          var data = JSON.parse(scriptData.textContent || scriptData.text);
+          // Resolve strings marked as javascript literals to objects
+          if (!(data.evals instanceof Array)) data.evals = [data.evals];
+          for (var k = 0; data.evals && k < data.evals.length; k++) {
+            window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);
+          }
+          binding.renderValue(el, data.x, initResult);
+          evalAndRun(data.jsHooks.render, initResult, [el, data.x]);
+        }
+      });
+    });
+
+    invokePostRenderHandlers();
+  }
+
+
+  function has_jQuery3() {
+    if (!window.jQuery) {
+      return false;
+    }
+    var $version = window.jQuery.fn.jquery;
+    var $major_version = parseInt($version.split(".")[0]);
+    return $major_version >= 3;
+  }
+
+  /*
+  / Shiny 1.4 bumped jQuery from 1.x to 3.x which means jQuery's
+  / on-ready handler (i.e., $(fn)) is now asyncronous (i.e., it now
+  / really means $(setTimeout(fn)).
+  / https://jquery.com/upgrade-guide/3.0/#breaking-change-document-ready-handlers-are-now-asynchronous
+  /
+  / Since Shiny uses $() to schedule initShiny, shiny>=1.4 calls initShiny
+  / one tick later than it did before, which means staticRender() is
+  / called renderValue() earlier than (advanced) widget authors might be expecting.
+  / https://github.com/rstudio/shiny/issues/2630
+  /
+  / For a concrete example, leaflet has some methods (e.g., updateBounds)
+  / which reference Shiny methods registered in initShiny (e.g., setInputValue).
+  / Since leaflet is privy to this life-cycle, it knows to use setTimeout() to
+  / delay execution of those methods (until Shiny methods are ready)
+  / https://github.com/rstudio/leaflet/blob/18ec981/javascript/src/index.js#L266-L268
+  /
+  / Ideally widget authors wouldn't need to use this setTimeout() hack that
+  / leaflet uses to call Shiny methods on a staticRender(). In the long run,
+  / the logic initShiny should be broken up so that method registration happens
+  / right away, but binding happens later.
+  */
+  function maybeStaticRenderLater() {
+    if (shinyMode && has_jQuery3()) {
+      window.jQuery(window.HTMLWidgets.staticRender);
+    } else {
+      window.HTMLWidgets.staticRender();
+    }
+  }
+
+  if (document.addEventListener) {
+    document.addEventListener("DOMContentLoaded", function() {
+      document.removeEventListener("DOMContentLoaded", arguments.callee, false);
+      maybeStaticRenderLater();
+    }, false);
+  } else if (document.attachEvent) {
+    document.attachEvent("onreadystatechange", function() {
+      if (document.readyState === "complete") {
+        document.detachEvent("onreadystatechange", arguments.callee);
+        maybeStaticRenderLater();
+      }
+    });
+  }
+
+
+  window.HTMLWidgets.getAttachmentUrl = function(depname, key) {
+    // If no key, default to the first item
+    if (typeof(key) === "undefined")
+      key = 1;
+
+    var link = document.getElementById(depname + "-" + key + "-attachment");
+    if (!link) {
+      throw new Error("Attachment " + depname + "/" + key + " not found in document");
+    }
+    return link.getAttribute("href");
+  };
+
+  window.HTMLWidgets.dataframeToD3 = function(df) {
+    var names = [];
+    var length;
+    for (var name in df) {
+        if (df.hasOwnProperty(name))
+            names.push(name);
+        if (typeof(df[name]) !== "object" || typeof(df[name].length) === "undefined") {
+            throw new Error("All fields must be arrays");
+        } else if (typeof(length) !== "undefined" && length !== df[name].length) {
+            throw new Error("All fields must be arrays of the same length");
+        }
+        length = df[name].length;
+    }
+    var results = [];
+    var item;
+    for (var row = 0; row < length; row++) {
+        item = {};
+        for (var col = 0; col < names.length; col++) {
+            item[names[col]] = df[names[col]][row];
+        }
+        results.push(item);
+    }
+    return results;
+  };
+
+  window.HTMLWidgets.transposeArray2D = function(array) {
+      if (array.length === 0) return array;
+      var newArray = array[0].map(function(col, i) {
+          return array.map(function(row) {
+              return row[i]
+          })
+      });
+      return newArray;
+  };
+  // Split value at splitChar, but allow splitChar to be escaped
+  // using escapeChar. Any other characters escaped by escapeChar
+  // will be included as usual (including escapeChar itself).
+  function splitWithEscape(value, splitChar, escapeChar) {
+    var results = [];
+    var escapeMode = false;
+    var currentResult = "";
+    for (var pos = 0; pos < value.length; pos++) {
+      if (!escapeMode) {
+        if (value[pos] === splitChar) {
+          results.push(currentResult);
+          currentResult = "";
+        } else if (value[pos] === escapeChar) {
+          escapeMode = true;
+        } else {
+          currentResult += value[pos];
+        }
+      } else {
+        currentResult += value[pos];
+        escapeMode = false;
+      }
+    }
+    if (currentResult !== "") {
+      results.push(currentResult);
+    }
+    return results;
+  }
+  // Function authored by Yihui/JJ Allaire
+  window.HTMLWidgets.evaluateStringMember = function(o, member) {
+    var parts = splitWithEscape(member, '.', '\\');
+    for (var i = 0, l = parts.length; i < l; i++) {
+      var part = parts[i];
+      // part may be a character or 'numeric' member name
+      if (o !== null && typeof o === "object" && part in o) {
+        if (i == (l - 1)) { // if we are at the end of the line then evalulate
+          if (typeof o[part] === "string")
+            o[part] = tryEval(o[part]);
+        } else { // otherwise continue to next embedded object
+          o = o[part];
+        }
+      }
+    }
+  };
+
+  // Retrieve the HTMLWidget instance (i.e. the return value of an
+  // HTMLWidget binding's initialize() or factory() function)
+  // associated with an element, or null if none.
+  window.HTMLWidgets.getInstance = function(el) {
+    return elementData(el, "init_result");
+  };
+
+  // Finds the first element in the scope that matches the selector,
+  // and returns the HTMLWidget instance (i.e. the return value of
+  // an HTMLWidget binding's initialize() or factory() function)
+  // associated with that element, if any. If no element matches the
+  // selector, or the first matching element has no HTMLWidget
+  // instance associated with it, then null is returned.
+  //
+  // The scope argument is optional, and defaults to window.document.
+  window.HTMLWidgets.find = function(scope, selector) {
+    if (arguments.length == 1) {
+      selector = scope;
+      scope = document;
+    }
+
+    var el = scope.querySelector(selector);
+    if (el === null) {
+      return null;
+    } else {
+      return window.HTMLWidgets.getInstance(el);
+    }
+  };
+
+  // Finds all elements in the scope that match the selector, and
+  // returns the HTMLWidget instances (i.e. the return values of
+  // an HTMLWidget binding's initialize() or factory() function)
+  // associated with the elements, in an array. If elements that
+  // match the selector don't have an associated HTMLWidget
+  // instance, the returned array will contain nulls.
+  //
+  // The scope argument is optional, and defaults to window.document.
+  window.HTMLWidgets.findAll = function(scope, selector) {
+    if (arguments.length == 1) {
+      selector = scope;
+      scope = document;
+    }
+
+    var nodes = scope.querySelectorAll(selector);
+    var results = [];
+    for (var i = 0; i < nodes.length; i++) {
+      results.push(window.HTMLWidgets.getInstance(nodes[i]));
+    }
+    return results;
+  };
+
+  var postRenderHandlers = [];
+  function invokePostRenderHandlers() {
+    while (postRenderHandlers.length) {
+      var handler = postRenderHandlers.shift();
+      if (handler) {
+        handler();
+      }
+    }
+  }
+
+  // Register the given callback function to be invoked after the
+  // next time static widgets are rendered.
+  window.HTMLWidgets.addPostRenderHandler = function(callback) {
+    postRenderHandlers.push(callback);
+  };
+
+  // Takes a new-style instance-bound definition, and returns an
+  // old-style class-bound definition. This saves us from having
+  // to rewrite all the logic in this file to accomodate both
+  // types of definitions.
+  function createLegacyDefinitionAdapter(defn) {
+    var result = {
+      name: defn.name,
+      type: defn.type,
+      initialize: function(el, width, height) {
+        return defn.factory(el, width, height);
+      },
+      renderValue: function(el, x, instance) {
+        return instance.renderValue(x);
+      },
+      resize: function(el, width, height, instance) {
+        return instance.resize(width, height);
+      }
+    };
+
+    if (defn.find)
+      result.find = defn.find;
+    if (defn.renderError)
+      result.renderError = defn.renderError;
+    if (defn.clearError)
+      result.clearError = defn.clearError;
+
+    return result;
+  }
+})();
+</script>
+<link 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+
+
+<h1 class="title toc-ignore">Model based Imputation Methods</h1>
+<h4 class="author">Gregor de Cillia</h4>
+
+
+
+<p>This vignette showcases the functions <code>regressionImp()</code>
+and <code>rangerImpute()</code>, which can both be used to generate
+imputations for several variables in a dataset using a formula
+interface.</p>
+<div id="data" class="section level2">
+<h2>Data</h2>
+<p>For data, a subset of <code>sleep</code> is used. The columns have
+been selected deliberately to include some interactions between the
+missing values.</p>
+<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(VIM)</span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>dataset <span class="ot">&lt;-</span> sleep[, <span class="fu">c</span>(<span class="st">&quot;Dream&quot;</span>, <span class="st">&quot;NonD&quot;</span>, <span class="st">&quot;BodyWgt&quot;</span>, <span class="st">&quot;Span&quot;</span>)]</span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>dataset<span class="sc">$</span>BodyWgt <span class="ot">&lt;-</span> <span class="fu">log</span>(dataset<span class="sc">$</span>BodyWgt)</span>
+<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>dataset<span class="sc">$</span>Span <span class="ot">&lt;-</span> <span class="fu">log</span>(dataset<span class="sc">$</span>Span)</span>
+<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="fu">aggr</span>(dataset)</span></code></pre></div>
+<p><img 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gAA1GC4zg1b4fxgdt+QvXezv5+GCIAiku9OHACAGvyrq2dEFTB4yTvH+Oway1Disr/OXXb/soIDlMUGAEXkuxMHAKAGpmpsZ+9oae9Y57ssd5LDAUAe/GoBm2JveQF1/jM+rB8rZopTyIXqej6n1POvxO09iJ9GD4BiY7aZvj+S4hVJAMiZXy1gBtY2NshAlYYQpmZkZ2/Pr+dzLCM1WF0MAABA9n61gC25cGXJ539tu+jMRVmFAwD4Abws7dLBfSevP07PLRNz9K3dOgaMHtG9mSpcNQKFRHm/MQDAr+GnRgT3nBz1moeYKjq6alh5fOzVU/u2bB606+KBEbYwfwooHqkKGF6SeGLPsdupH8q+m83LbB2yY3IrKIsAEEyUvHlkSHSuTfDOHauG+5hxaQivenf34LJJs/ZPHtXKM262HdURAkA2KUoN78HS9n6hiVU4RqPTabXGLti8XtuggAFANOGLqCMJuNfak+ETbT93m8OUTXzG7z5Z8cpp/qFjaPYyagMEgHySL2QW3D904IXQZvj+pwVVgu+URQ6EXjcAEE6UlZ6FrP38LGv1SqVbdPS3RZnp1EQFAKUkL2DikqISuuuYJb85a7Pg0TEA5MCUOcp48cei75rNiYs+FeHKHCpiAoBiUvRCtG1uSystKia7ayMAiozp2t6Lmxu1fteLqq9f5iWHrYvM4Xq1oyouACgk+dMqevNxS4fsHT1tid/xNV1NYMkyAGTAdAYunrPt6rJZXq6Xhw3t6maujpVmP7kceSgmRdRq5eKBVMcHAAWkmG6B6XWaPs6h3aLuNnvMbS112N/cxCl1Wnd9rT+UNQCIxnZfdP6Sysypocd2LY+pmf6LMXWcB2/ctnmGO5vi4ACgghQFrOzKnH5L48ppHD1NZcTn8b55ExfA0CIAskHT8Z5xMCFka0ZiYnpeBeLqWTk62WjB5SJQWJIXMP7946ffKnkuvRazoo1GY5rFkZyc/P79+zrfysjI4NWqxADIKaamlbuvFdVRACAHJC9gvLy8YrpzwIjWjap6IYSioqLi4uLqfKu8vLykpITkeAD4qV9vop2Zsp3UyACQA5IXMJaBoTb+ND2jElmpyCAg2Vm+fHl9byUmJg4fPpzMYAD4Fb/eRJvUsACQD5IXMHbHhRsDL48LmeB66M/gVnpKjew+DIBGBJpoA/ADkq8D41/fvjlJRE+PHN/GQJWrWotu8On6RjkAAFLjx0xp7jjjyve3YPiHA0PsvaCPFFBEkt+BYarGdg5elg5edb7LcjeUvCYCAOrBz0t+llUqRvxnKZkZjKcPH6h923Ye5yXfiH+dDL3ogSKSvIDBzrAAkEacGzWp7bRb/954zWv3Tx0fwri+U8kMCgA5AX3jAZBjNO3OC8P3jRIhwaMdIXvpE7dNcKuVsxhN2cC9cydqwgOAUlDAAJBnXPuuw+wRQgLLt6ff0gcFj/CuPVooyk2MTc1s4WlNRXgAIIQQhmEIIRqN7AdIUMAAaAyY7Zecb48QEleXl/NE/20ki1e+3D6m644W1YX7qQsOKLpZs2b5+fm5ubmRfFwoYAA0DvjHW6uGj950NbNUWHsjdIadPzGHwMVVpcWEfFWDwkB4GUJFVIchQEhYXU35X0h1Rbm8/H8R1dso0MzMzMzMjMxgakABA6BR4MWuGbP6Sqlr4BRvcdyBU8WeEwe5sAqfnjt8k/XbwZMETKPn8/nlnz6u9mvW8K9qIBzHu9WMSVEcBkLH9tyPCqc6EITj+OoOtlRHgXCECgsLqY7iG79awEIsdbIWJZ4fZ4RVnw42nczcmxneC7ZeBoAsgvjTZ99we4bHHAnW5d8Q2QWL+65c688Sz/H/rfX8S2nVAbYN3dOSxWKp6ugtuvyckHgbYkVby1F/RZk7t6Y2jP3Tf9O1tO0xYwW1YWQk3L02ru9raoNACCHkipCWlhbVUXzjVwsYJiy5f3jrESM/HTz+TWkJPf7KP8y6lp7QDVw6OuvDUjAAiCUuyC1Apk6OmhhCLDsHm5LjqQVif2OaQf/pQ5b4bzi0pyfMpAeUSUlJuX37to+Pj4ODA5nH/dUCNnqK55FFvw+7tf7zn1f2vlHn59iB0cVRAXBvBgCxMK4KF1WWV+AIIUzb3IydnpwuQsY0xDCzNBPtSaA6PqDQdu7ceWTr1l4jR0ZERJB53F8tYG7zr6f1ffg4/VMV786G37YxZh+Y2abOOzDj1tATAADCMR09XFgRZ/acm+M5wJhhY29VcOjac4GvK7M6PTVDxLCkOj6g6CiYwiHJJA6mjr1PF3uEBPpZgwvoA/r1bQUTQAAgC2YQOHvk+p47A+zzwlJjRnbuYrPkj8EDSwdapEXtfqPavQPV8QFAAelaSbWRQSQAgB/A1Dv/eT3GKjQsT4eBGC4zts67FrB+61oRpu4yfs+mQVSHBwAFpL2LwsvSLh3cd/L64/TcMjFH39qtY8DoEd2bqVI+9RWAJotl2mn2zs9No7Q6rInNnvo6u1LL0kIbNjUCikmqAsZPjQjuOTnqNQ8xVXR01bDy+Nirp/Zt2Txo18UDI2zhGRgAZKBx9G3tqQ4CAApJUcBEyZtHhkTn2gTv3LFquI8Zl4bwqnd3Dy6bNGv/5FGtPG/NtqMTHycAAAkKXz16kPSuVFC7FQfddFCANyUhAUAhyQuY8EXUkQTca+3J8Im2nysVpmziM373yYpXTvMPHXs5Y5mjbCqYmF9RVlbBEzM5amqqSlAlgWKpeLi2R6+lsQWi2tULIcQOhAIGFJDkBUyUlZ6FrKf6WdaqIHSLjv62aHN6phARWsDw8rTzO/8Mi7587/mbwioRjhDCaEoa5o4+XYPGz5zUq5kKjP+Dpk+cFbFgTVyFbdDaZcEeRtxaKUbTpSYqACglxY7MyhxlvPhjkaj2D4uLPhXhyhwOkfUEL74536/XpnT9tn36TB1sZ66vwWFiwqri/Levnty+EDrgyLE5Z6+F+qpDDQNNnCDxQQLfbPTOiAUdlKmOBQA5IcU0etf2XtzwqPW7pnhPd/gvlXjJYesic7jt27kSOIlDlL574baCHnufHQy2+r67x/rcmJAOgQvCRsXNbQYDiqBpqy4t49NMbSygyw0AX0hxB6YzcPGcbVeXzfJyvTxsaFc3c3WsNPvJ5chDMSmiVisXD9Qm8G5ImJyYotJl2eA6qhdCiG7QZewA825JqUIEBQw0cRwHJ1u0/UZs/jQLAxhwAAAhJN00erb7ovOXVGZODT22a3lMzQNljKnjPHjjts0z3NlERkc3MTeuOBf7tKK7B7eOtwUZcfdy9LwMoXqBJo/hOjdsRWz/2X1DBH8uCGhtqsaEMgYUnnQLmWk63jMOJoRszUhMTM+rQFw9K0cnGy0WwbEhxHAZO8s/bEKXNinjxgb4uduZ62twWJioqiQ/O+1J7Nl9YcfftN0R7gY9rUCTx7+6ekZUAYOXvHOMz66xDCUu++vTnt2/rOAAZbEBQJGG/O5nalq5+1oRFkpdaBajI2+phC5eu3fhsI28r6YPY5iyvmuPCUcPL+pvBZu3gKYPUzW2s3e0tHes812WO8nhACAP5P/mRaV5UOjJoDWVBZlpr7LzSyr4OIuroW9m28xKVxkGUYCiYLaZvj9yOtVRACBX5L+A1aBxdK1ddK2pDgMAAIC8aCwFDABFNMXe8gLq/Gd8WD9WzBSnkAvV9XxOqWdmynZSIwNADkABA0B+GVjb2CADVRpCmJqRnb09v57PsYxIDQsA+SDnBUyUefb3XbGF4h98hOk8fNUwJzn/7wBAKksuXFny+V/bLjpzkdJYAJA7Uv7i/0FXbJ8B3iaEzQvE2FxG/r3II3ffC9i65sbqdYSrVO2/YpgTUQcEoDHARQIRxmTABFyg2KQpYD/uih3d2zuAsHY3NEP/uREdBrp1cZorWnrr+lQzyFigwET59/aErttz+taz7FIhTVmvmUfXIVMXzOzfHDpaA4UkeQH7WVdsB8LXM9MtBgxssyiqoV8zZMiQo0eP1veumppaQw8AgCyJs48FdxgemSXiGjt597DRo396/TT+0PKAkydnnrq6sRORPdwAaBwkL2AUdMWmGfZZucdaR7tht1+RkZGRkZF1vpWYmDh8+PAGfTsAslUas3zG0Tfa3X4/dWCmp07NhaO48OHm4f3mbZmwqs/rLb4UBwgA6SSvCVR0xaaZtB3U1b6udogAKAZ+wsUrBZwuq/fM/rd6IYRoWh4z96zpysk+e5rK2ACgiOQFjOPgZIue34jNr+MJGNlE1ZUVldUiqsMAQObw8rJymqmri16tkUJM183dHCv6SE1UAFBK8gLGcJ0btsL5wey+IXvvZn8/DZFM1WdG6qlqDz9V3+pOAJoMhr1jc9qH1LSSWhmHl6Wl5iBzaFIDqMRgMFIQYjDIXs8keQH7tyt2/M4xPhYaHI7qN3SDT5NXTugWfiPHjvK3hO1UQJNHtxm9bJTepcWT9iSWfqlheNnziMkLT/M8godQGRtQeMuWLbsdH//HH3+QfFwpNrT8SVdsQ/JmujPcxm8PI+1oAJBux/hRjwRf/iS0NPt4fIL7lXWu7i0ttbHCN88TEjKKxGoeHapTELJr+OFwsajw/ZuGf08DicXisoJcyiPhV1XwykooD6OsIFeAUAa1QSCEEPrBvYm6urq7OwVbIkhewKjrii3mV5SVVfDETI6amqoS3HaBpu9u9NETvG9eUVJi8HKS7uQk1fyRocRG1YlHNkSHL+rbwGPx+fyKwo+be7dq4Pc0nACho/NGU77mU4hQ5uN7CefqXXtDEhzhGGZLp/43nlgkKioqojqKb8h/Cya8PO38zj/Doi/fe/6msEqEI4QwmpKGuaNP16DxMyf1agZrOEGTdaio6hBZx2KxWAYI5ZB1uB9QQ+gSQt5Uh9EboZJhk3rMWEFtGBkJd0+smDb3XDy1YSCEtg7x09TUpDqKb0hbwPCytEsH9528/jg9t0zM0bd26xgwekT3ZqoEFxO8+OZ8v16b0vXb9ukzdbCdub4Gh4kJq4rz3756cvtC6IAjx+acvRbqqw41DAAAqHL37t2zZ89++aOTk9PQoUNJOK5UBYyfGhHcc3LUax5iqujoqmHl8bFXT+3bsnnQrosHRtgyiYtOlL574baCHnufHQy2+n7d2frcmJAOgQvCRsXNbUb97TUAMoaXJJ7Yc+x26oey7yb/MltHhE2mJCgAEELHjh3L2rq15qb5A0K/OzvLbQETJW8eGRKdaxO8c8eq4T5mXBrCq97dPbhs0qz9k0e18rw1246waiJMTkxR6bJscB3VCyFEN+gydoB5t6RUIYICBpo63oOl7f1CE6twjEan02qNObB5UMAAtfwRmoYQQugZQjfJOqjkD0qFL6KOJOBeK0+GT2xnxqUhhBCmbOIzfvfJ1d74w0PHXhK4rphuYm5ckRD7tKLutwUZcfdy9EwMoXqBJk9w/9CBF0Kb4fufFlQJvlNWd480AJo2ye/ARFnpWch6ql/t1Vd0i47+tmhzeqYQORJVURguY2f5h03o0iZl3NgAP3c7c30NDgsTVZXkZ6c9iT27L+z4m7Y7wt3kfyYKAA0kLikqobuGLPnNWRsu2ACoIcU6MGWOMl78sUhU+4fFRZ+KcGUOh8gJFTSL0ZG3VEIXr927cNhG3lcD/ximrO/aY8LRw4v6W1E+3RYAmWPYNrelJRQVixCCAgZADSnWgbm29+KGR63fNcV7usN//eh5yWHrInO47du5EjiJAyGEVJoHhZ4MWlNZkJn2Kju/pIKPs7ga+ma2zax0lWHyIVAU9Objlg7ZO3raEr/ja7qaEL5nEQCNkRR3YDoDF8/ZdnXZLC/Xy8OGdnUzV8dKs59cjjwUkyJqtXLxQNlsS0Tj6Fq76ELDN6CwML1O08c5tFvU3WaPua2lDvubgQelTo/i1lIVGQBUkebxEdt90flLKjOnhh7btTymZlgPY+o4D964bfMMdzax8QEAEEIIlV2Z029pXDmNo6epjPi8b/tz4IJ6fgqApky6+Q80He8ZBxNCtmYkJqbnVX3w+nAAACAASURBVCCunpWjk40WDGsAICv8+8dPv1XyXHotZkUbDRg7BwChhrWSYmpauftaERYKAKBevLy8YrpzwIjWUL0A+NevFrAp9pYXUOc/48P6sWKmOIVcqK8vsVLPvxK394B7MQCIxTIw1MafpmdUIisVqmMBQE78agEzsLaxQQaqNIQwNSM7e3t+PZ9jGanBBSIAhGN3XLgx8PK4kAmuh/4MbqWnBGkGwC8XsCUXriz5/K9tF525KKtwAAB14l/fvjlJRE+PHN8mMoTNrbWdELt/WcEBiiIDgDKSPwPjx0xxns/YmrC5c62BQvzDgaF+O20jbq/yIngpGAAKD1M1tnPwsnTwqvNdFgV7CQJAuV8vYPy85GdZpWLEf5aSmcF4+vCB2rdlCucl34h/ncwsEBMdIwCAuo1kAZBbv1zAxLlRk9pOu/Xvs6957f6p40MY13eqA9x+AUIVFxfb29vz+fU9dyVeRERE374N3eAYACBrv1zAaNqdF4bvGyVCgkc7QvbSJ26bULuHLkZTNnDv3MkaWhMCQlVWVmJ5ea/JOtx0hAoKCsg6moRI2kgWgMbh14cQufZdh9kjhASWb0+/pQ8KHuENt1qAJDSESNvJvM695+QCaRvJAtBISH67hDsM37RhuAPcZwFApv82kr2dVVSS//59XnFxduzfI21zoyaP2ppKdXgAUECKDS1vLWztOCQiG+ZqAECen20kS3V8AFBA8gLG9uzmp/oy9u4n/OefBQAQo2YjWb96NpLNTKcmKgAoJXkBoxkN33N8avHvvy05/jSvCqoYAGT4aiPZb33eSJaKmACgmOQFjH9tUb+5F4rK49cHuRqqslXUNb9mOOpMfW0SAQBSY7q29+LmRq3f9aLq65c/byTr1Y6quACgkBQbWiprG5uY8E1MzJ3qeJfloAvTOwAg3M82kqU6PgAoIHkBY3rPOXZqjgxCAQD8AGwkC0AtDdkPrJFZvXr17du363yrvLw8Ly+P5HgAkBBsJAvAN6QtYI2wI0BAQICXV92tUDMyMv744w+S4wFAKrCRLJA7XC53IUIrEEIIiRBqyeWSc1ypCljj7AjQvHnz5s2b1/lWYmIimw2DMEBO8fMSzhyKfu+6YHrHz/sxV58b7zg/2alLwJip47pbwxREQLHVq1fPnTv3yx/V1NTIOa4UBey/jgA7Vg33MePSEF717u7BZZNm7Z88qpXnrdl29J9/CQDgVwgyoqYOnLD7WTGz064Z0zt+fhVTYlS+vnNiS9zJsJ2//X0ufLgtIQOJ1QhdJeJ7GkiAUDxClVSH8QEh/oe36Q9uURtGbtpzQTWP8jAQQvyKcrG47g4WdDpdU5O0dm//kbyAfe4IsPZk+ETbz5Xqc0eAildO8w8dezljmSNUMACIwHu0asCIsCRum0m7Vk4L0v0yQs/qsiP709zYIxvmL/z78IRB1i3ur3BvaAnj8/lFNFpfVfUGfk/D8UqL53FU6AyKn9Dzystoty+/fhhLbRgiAV/Aq4pcMI7aMBBCvPKysrIyqqP4huSnSE1HgKn1dATYnJ4pRFDAACDEhT93Jomd5sdcDfWo/VCBpmLpO37HlRZcL/9NO7ZcWnGgTwOPxWKxVLR0F11+3sDvabgVbS1HbT9m7tya2jD2T/9N19K2x4wV1IaRkXD3xIppc8/FUxsGQmjrED91deqvb74mzTqwLx0Bvv3hzx0BOHI8jwOAxiX2XjHDc9mEVvU+Elf1njbRe9uMu7cRamgBA0BqMTExZ86ckeIHp0yZ4ujoKPVxpVgH5treixsetX7XFO/pDspfXv7cEaB9O1d5ncQBQKNT8AnX6Gxv+IPmADR9ezst9DiXvJgA+M4///xzPyvf2kOyjjCPzx319Iwnt4D9rCOANtyBAUAQFS7G5/F+2HFUVFFeiZSUf/QRAGTP3NnDY0CwRD+SndjQcVFpHpNCRwAAyOFoz9h7/3ZCdR/v+vbZrLx3Ja6cblv3+hAAmjbpGhfWdAR4n5cef/OfCxf+ufkoLff9k8Oz2kIfRACI1HuQr/Lr3Qs3P6t7Tjleen/tgoi3Sm0C4AEYUEQNqThMTSt3306dO/u3bwX9bACQAfORvy9rR49b3KljSFhsduVXY4l4RdaN7WM7dA+Np7Wat3G8DXUxAkAZKQsYXvLsyJIh7ez0OCwlJRZby8ozYN7+hCLYpRkAQrGdZ5+K+b0H98nOCb6WOgZ2rX07d+/epYOHvYGutf/UiEQl32Vnzy39bo49AApBmmdg4txzkzoO2p1SzTV28ujmq0cvzEhMOL1x1IWzcUdv/N33R1OmAACSwTQ9Z5953ufi7q17jl+6nRD3qlqMMKaqqWO30f1HTAsZ6KQJyy6BopKigFVcWT45PE2j24aTB2d4atckj7gofvuIfrMipiwP8A/rqkJwkAAoNkzFtsfMbT1mIoREvPJKsZIKhwmzfQGQ/G5J8Oj8xQ/KXVaHz/q3eiGEaJqtpu7+Xw/uh0vn4wWEBggA+AqdraIK1QsAhJBUBSzvw0fM1MVZt1YOYdrOLma00pJSeBAGAABA9qR4XsVV4YpzklOLa62uxEtSkt9jdg52CrRHJgAAAMpIXsA4HSdPdhOeXzRh55PiLzdbeMmz3RMXnlcfumh0M3ikDAAAQPak2E4lMxO19jYKPzWl9dXfnVwdLLWxwjcvnjx9U0oz9io/Piv4+L9f7TAyYoE/seECAAAANaTYTiX13NaIWB5NSYnGz0t+kJdc8zJTSQl9fHz++OMvH2R9bAcFDAAAgIxIXsCUBhwqqjokg1B+SsyvKCur4ImZHDU1VSUYqQQAAIUm/4uO8fK0cxsm9faw1lHhqGro6BvoaalzuFpWrXtP2nAurfyHjboBAAA0WdJOGcTL0i4d3Hfy+uP03DIxR9/arWPA6BHdm6kSvD4FL74536/XpnT9tn36TB1sZ66vwWFiwqri/Levnty+EDrgyLE5Z6+F+qrDshgAAFA0UhUwfmpEcM/JUa95iKmio6uGlcfHXj21b8vmQbsuHhhhS+CGlqL03Qu3FfTY++xgsNX320msz40J6RC4IGxU3FyY+ggAAIpGiiFEUfLmkSHRuTbBO29nFZXkv3+fV1ycHfv3SNvcqMmjtqaKCIxOmJyYotJl1OA6qhdCiG7QZewA81dJqUICDwkAAKBxkLyACV9EHUnAvVaeDJ/YzoxLQwghTNnEZ/zuk6u98YeHjr0ksILRTcyNKxJin1bU/bYgI+5ejp6JIdx+AQCA4pFiGn1WehaynupnWats0C06+tuizemZQuRIVEVhuIyd5R82oUublHFjA/zc7cz1NTgsTFRVkp+d9iT27L6w42/a7gh3g94fAACgeCT/3Y8pc5Tx4o9Foto/LC76VIQrczhETqigWYyOvKUSunjt3oXDNvK+mnGIYcr6rj0mHD28qL+V/M+kBAAAQDjJCxjTtb0XNzxq/a4p3tMdlL+8zEsOWxeZw23fzpXASRwIIaTSPCj0ZNCayoLMtFfZ+SUVfJzF1dA3s21mpasMkw8BAEBhSXEHpjNw8ZxtV5fN8nK9PGxoVzdzdaw0+8nlyEMxKaJWKxcP1JZJVaFxdK2d1A1gITMAAIAa0jw+YrsvOn9JZebU0GO7lsfUDOthTB3nwRu3bZ7hziY2PoTw8rTzO/8Mi7587/mbwioRjhDCaEoa5o4+XYPGz5zUq5kK3IcBAIACkryA8T+9yfpEd5h6MCFka0ZiYnpeBeLqWTk62WixiA8PFjIDAEAjIKiuqiotluhHRAJ+Aw8qRTf6WwtbD3m7MuXWDEtNK3dfqwYG8EOwkBkAAOSekZHRwfXrHxzcLukP6k8Z3ZDjSl7A2J7d/FRnxt79NN2y9qbMhKtZyLzshwuZuyWlChEUMAAAoMr8+fPnz59P/nElL2A0o+F7jmcMmv7bEqXfp/V01pflVEC6iblxxbnYpxXdPbh1vF2zkNnrFxcyX79+PT09vc633r17V1ZW9v3rTxEKkyDcBqlv/fcVhD6REsC7el4/d+7chw8fSAkBIYQ8PDxcXFxqvVhJ4v+IFITakHUseSPgVT08eYDqKJBIKHh582Le62RqwyjOfScS8Cn/C/n4JqO6oozyMBBClcVFVIdQmxTPwK4t6rfgamX56/VBrmtpLA6Xw/xqIRa7376siL513jBJEx2RC5kzMjISEhLqfIvP57dt27bWi/7+/hnjx9f9AzIwR0uLxar9GHHw4MFXtLVJi2GSm9v3L6akpNRX+GXB1NS0VgHT0dEZOWNGQmUlOQE40uk+Pj7kHEuuaGtrTxw7ujI/lepAkG+7tsZ4CZvqSHycmvP5fF2qw9BkCjGnlpSHgRAaMWigpaUl1VF8A8NxCTckEdzdOGzDnfqevbF85h6a403kUrDy5KjQxWv3XnyW9/1C5rFLQhf1b6Zc/w8DAABoqiQvYNQQw0JmAAAAX2ssBaxuoupKnojO5sCqZgAAUDgS9BHkZV7aNHVgRw8XZ/d2vSdsuv6B8l1Mqs+M1FPVHn6qmupAAAAAkO5XJ3EIUnb0az/1coG45n4t8fGdS+eubr5yKsSBqAkbUqBb+I0cq+lYuy8+AAAABfCrQ4gnf9MNPCp0D/lrx9zu1rSsK5tCJm6+j/fen3pquB48iQIAAEC6Xy1gU80Yf2svjX+43KlmhqHwZainy9I3wRfe7+kmgxZSdRLzK6CZLwAAgBq/+gys4BOu7uph92V+PMOmlZsWKi8tl/kUELw87dyGSb09rHVUOKoaOvoGelrqHK6WVevekzacS5P98QEAAMilX30GJhZjylzlr8odxuESunVl3aCZLwAAgLpJs50KeaCZLwAAgHpIMI2eAjXNfEf9sJnvq6RUyufzAwAAIN2v34GJS+7tXrLwn38rnvjdnUJcmHRk6cKEr+9+mI6/LRvakqj7OkKb+QIAAGhKfnUWYpAyFs37hc+xA6OLowIIWxsmztrb12NCrF7vepv5euy4f3aMlXzfSAIAACDerxaw5zev5dW34cfX6PqO7VvqEVlPoJkvAACAOjSWXojQzBcAAMA3GksBAwAAAL4BD48AAAA0SlDAALGElaUV/O/v6kW8snLerzxFBaBJgEQgAwwhEklYnvfmTV658Nu/UpqGs6M5RRGRrvp4kN5f7Z9fm2L6zbWR8PESl565G9+Q1zkTkA/O//9AIpBCvjtxNCaizCMju407nFb53QUBOxCviiIjBN77BzHnrt1Pep1bzENsDUNrJ89Ovbt7GLHJOLgwfuuo0BvlopxHVRmp4wZe+2ZuKM578+AV1pe0s0386f7fq/449fh9Wa1fpkr+/7u8piP87iCeHJz/iOIUqCFXiYCaei5AASOI4NGO5cdKvJdGrxzooK30zUUXTVX2hxdmn18yYsKft3IEDDVDMyMtZayqKOfQzg1Lphn6zvx7/5peZjL/P01X4qqoIBGbUfNvnG/eVHXqt3j5XF9ysgUviJ7cZ+oFtQ4Du/vosb+Zqsq014FRc1mg+PxHcpECNeQnEZAC5AIOCME7M1zLamYcn5KDl8fObclmGHacd+jBu0rxv6+Kq3IeHVnYyZjJbjkvtpykUKqvLfafHJ0r/vknZRjDpbF6Gr0icigNQsFQev7jcpUCNeQhEfCmnwtQwAgiytrR2cB/W7qA/EOLP+zpzmW7L0+oqutd3tM1Hsrc7ns+NNVT+HtlR/pzbebcpeyXqSKi8PzHIQXq19RzASZxEIafumtAlw3C/pODPM3VmV/drNNNBvZrI8sD/zPOpG/q4tSb083rGhIQv/urk+0q69Nvd3clYeBCcHfjsA13+LVexTA6k83VNG7m7h8wqKudmmxXoIszd3Vvvcn8QNyuHoQ2hQE/Qtn5j+QsBWrIQyKgpp8L8AyMKLznJw/deZdRvGXOP1u+fUfWD7HxspJymoGxfj3nJ03XyIBRVlJGzoUKpqJvKEwOu5CG6zVv5WKjw+YXZjx79OKjhlsnd83MA8e3rlk3aP+tfUEmsswmZst+Q3SWDWiZ0DOwq6u5tvJ/3Z4Zdr2n9ILNd2SAuvMfyVkK1JCLREBNPheggBGEf2f3tnjdYQdubhjYotZDbIyEjlcY7QdHodHI67nFsDZDrz66Lb5+YnkH/c9nlzD/9pqgcY96Ho49a/xyY2+/pVsf9v/dk/njL2oAwbPDm06/Zesq5T88e/DhN2+xe9lPbPRJK48oPv+RHKVADXlIBNT0cwEKGEGqcz+UmvadNNhZT6bno9zj3z0SVdgrbNGXpEUIMfTaL5jfznLyX3fHbGk3Jthr9cHH+WJPY5ldeir13Pnq7U5ZfTuoC5z/tchDIqCmnwtQwAii3NLNtuDClRc8TxcS15z8C686M87CcEo971aXVOC9SAyGX1RUgaOvWy3jlUVFvEpUiSNUzatGGEbSVfm3cX28sW3np+6LAmwb+VWnPKL2/EdylgI15DURUBPKBShgBKFp+QR0iFjj75U0on8rY+7XpwXDbvZ0WSYP3abbpBB9wQ8/w3S1IedMZXkPCdLrNn/ocu7vk7s7GSjTcF5e4qVd8+dd0Bx4rvXHe38v2PLQPniLvozzVvwxPvrghScfykVfnnvgFSkXDsa1tpzX6JNWHlF4/iM5S4EacpIIqKnnAtXTIJsK3vmJNiZ1s5lIdXDkqnx5YJy7NgPD6EoqqipKdAyjqTkM251YLvqwq4u6Zd/N8aUyns/Mf7zKjcNQt3B2NlOlKRs7tvFs7WDEoXGbB257UKKAc6lJAOf/96hPBLzJ5wJMoweygFe+TbgZ9yQjv4qubtLCu2PbZhp0hEQVZdXKqhxZT+cVPlzQssO1YQ/vLHEo3tvL4WTgy3MjdSufre8VmDDu5tGhRk1xOjGQT5QmAmr6uQBDiLJVM9Y8bWkAOYejrO8Z/+rSgXtL2/j6+vq297DTUcI4pq16DGlV61N0riqnzh8nlij3fZ6Gu2czFsK0nF0M1j5LFSJdrvPUee2aLQl/OWhpy8Y+bNJ4kHz+I8pb/8lVIqCmnwtQwIhT/1gzSQlMZd8zUWHimb8vRG7FMZqyfos27dr7+vr6+rb3dDAgf99smrqGWnnW+1Ic6dAtbM3yDybmi9sa01i6+mqZqa+FqLEnrZyi/PxH8tD6T54SASlALlA9htlk/GismaQQKO57xi/KiP/nyLYVU4d2bW2lycIQwjAlHfu2/ccv3nz48pN35SKyIim9ONaEZdRxRuTzSsHz1W7cllNOp7xNPjGxOdt+wUNqmh01eXJw/uOUp0AN+UkEvMnnAhQwgggezLdTbrU6qRoX54X30OkZkS/G8fKn6zrYBh5+T1IM8tT3TFyZ9zL29N4NC8YN8G1poEzDEKY69BSPrKN/vL0+wMViwN48sTj39BgbJQwhhClZ/hb5lsxfHgpEHs5/XL5SoAa1iYA39Vxo7M/w5EatsebUZ6lChLjOU+e1u/tHOEkxcDy7+BSdibiSLybpgD+AKWvo6WhwlWhifs3OtAw1QwNVsgZRMO1286KfZJ4YpYdh+n13P017cDnm6sPUZwcHy7pzj6KSh/MfyVcK1KA2EVBTzwV4BkaQH441kxUE9X3P+J9S7l27fPnylctXbj15W46pWbTq0GXkuvldunT0sNEkuUmDsCTrWcLzrE88hrpJizbtbLUa+eZ98kwuzn8kDylQQ64SATXhXKD6FrDJ+NFYM0kh/HAtznmZjloI004sGd2zlZkqHcMY6lae/SYs++tE3KtCqsZy+Jln5vmbffXYHGMbtZ954rUcjS01MXJw/uOUpkANeUsEvInnAhQwwvxgrJnq0GSPFx3IRghj6LYa+cfF1GKKHw7zHi5xUlIy77Hk8M2kzPzCj29fxh1b0dtSSclp8YM6d4wCDafQ5/8X8pUIeJPPBVjILCN4RXb83Relmi083M3JHPH+fPTq4tzcwmqGhr6hljIZI914weMTx05dvnr12q2EzBKkYd3a19+/U6dOnfw87bTJHq0Q3J5u2+X2yPsPVrh8dWh+0mpvj3Cff15taQ/9ZmWO2vMfUZACnw8rT4mAFCAXqK6gTY6osiArJelZZhElM3nFZS8Oz+npoM2q+Z2BMTWbd599+EUZeREIil7fPf33qimB7e11lDCMrmLi1n3EvI0HLz/NqSDpr6T86ECO7piL1bVerr4ywVBz+FkSJ4ApImrPf1weUqCGHCQC3vRzAQoYccRFD3eM8tBnYQghdmB02dO13duN2PaAxEwWZe/vp09Xcxi4YHvk+avX/zl1aNuiQEd1hsGAg1RMmRWVvU2IiVgX0s1WhYYhTDkompyEqY6daaVkP/N26Tevlt+Z66DReVd2E5g7LJ+oP/9xuUuBz0FRlAh4088FKGBEEWXu6anD0vMc9/v+vVPcVAOjKz/d+aOvtbJ6hy2pJIUgTFzpzG429Xrx178yxCU3ptuxXVYlCUmKAsdxUUVO0s0TYWvnjOrjba/LpmEIY2pYtV0RS9Jz48q0QyPtuFquv63cc+LSrbhbl07sWTXcTZvrMPHgnfgaCSm5jf/yU57IwfmPy1EK1KA6EfAmnwtQwAgiTFzprOy48GEljotzdvqrB0bzcBwXvFzrwWmxiKQYeKd/0zCccOW74YLrk000hp2R7TkqKM783H2gi7ulBhNDCGFMDas2vUfP/+PAxYTsUhJ/edQ8SP8xpteGV43/+lN+yMP5j1ObAjXkKRHwpp8LsA6MIMLM9GxNH39nZYS+mhXDsG7TSmtdFkkx0LR1NUqfvMwRd7L46qm1ODc5pVjLWUumD7KrT49rHhjNQxhTw9Kt3YBZUzp06NDBx9lUlYpWa6zu216mr/3xUlaMrWPaBNZxyg15OP8RpSlQQ64SATX9XIACRhC6kYlB8fVHr4X+zb86VUVvnzwr1PMiKQZmq0GDzHcuC5ik/Mfsvq2s9Lh48et7xzfOXnzHbML/Wsl0uhHdsvvcP3p7U5qrX2BcfUtrfYqDUDDycP4jSlOghlwlAlKAXKD6FrDJEKZu6aDOsem3OurOrTXt1AYczMm8s3dSK3Vlt5WPyYui6uW+Uc4adAwhjMZg0DCE0dWdRkS8IG3Fh+DJ0c1H72aTOM+qlsrM6+G/b7qQVTMmUpl8dGYPF0tze59+U/+6k99YB0oaAfk4/3E5SIEalCcCrhC5AAWMOKL8W2v726nSvqx6wZh6nlOiSV/xLirNvH/+8O4d23fsPnzuXkYpqWdqdcxYPQZGV7XyHb7474svPpL6Hy8uvDrPXZ2GKTVfeF+A43j5jem2TJpGy94jhvVw1GFy3ZberyQzHsUiL+c/TnEK1KA0EXCFyQUoYAThl3x48+5TFS4qe5Nw9fSxw4ePnb2R+KEJLHWXHO9Dwult8wb7WKjQMSV9lz5TfifpSrT6zmxblnHf7Y+LRTiO4+LCowEaDKuJl4vFOI5XJ29oy9EIiCxs/NuoyyM4/79HWSLgipML0ImDIFWnhxmNZRx8t6/XTyf9EE38/sHZ++9EP/wM3cSzTxtjcp/U4hXZ9y9EHzt6NPrS4zyGRdt+Q4YOHTrQv4W2jB5FCOMXOba/PurpnXk1LVurLow27/9g/P1na9wYCCHx2+3+zTY4Xkrf6tvYuw/IIQrPfyS/KVCD7ERAipQLVFfQpkJcdHlKc6vfoj+QP1rxKzNl2YHkrZ38j6Aw9cah9dP6O2vTMYypqsahYUqGbcbuTaqQxdF4Z4ZpaI+88O8Uav7d2dYss6k3v0yprowO5JC7FZMCofD8x+U5BWqQmwi4AuUCzEIkCF6h1n5Sl41TW/ucGNjFxUyL/dU+DrbTQ3rI8NBKffa+/7izrhvpqtdn106fH/aw2m5gT0fS/lfjVe8TLp89ffrU6fO3Xn6iG7h26jczfOOA3r52rHdxUX/MmzNplGWb+4taED1HC1Nis6qLyvgIsRBCorRr19+qtPNz/7cFHF7yqVisYk1JY76mj8LzH8lfCtSgKhGQIuUC1RW0qeCdG2dhUDeLcVTE8+af0AHNuDSubf81l96Qd6VVfXWGtQoNo6taeAfO3Hg09rsH6MUH+ijX0ZyNAOKcsK4qWr3C3whxHK96sMCBye0ZkffvML8wa1dXNW6P8LzGP+4vj+Tt/MepS4EaFCYCrkC5AAWs6eHn3No0tKUajW3RfemZdJKnGvEfhS//63T8+8r6ckNY8j7jXZFM5mSJiy+H2LKUjDz6De3f2oBJ0x96/KMYx0WFabeP/z64hQrTYsKl4safs+CnKE2BzyFQmAi44uQCFLAmRVhwf+cYdy06y7jjnKMvy5rACSqh6szza0b39HKwd/IbufnORzGO43h55AA2xtRtNTbiRVOYOAx+SOFT4AuFyAWYhUgQ3vsHMeeu3U96nVvMQ2wNQ2snz069u3sYkTYnCy9+un/J5Pl/P0IeE3/fsTrYWYPMAe7qs2Ntxpzh/fAz7L7h6Xv6KJEU0X/En9KTPqk1s9VTbgJD/vKK8vMfUZ0CNeQ5EVATzAUoYA0nzD6/ZMSEP2/lCBhqhmZGWspYVVHO25xSAdPQd+bf+9f0MpPxw2O8PDlqZcjsrberHEeF7lg31kOb9B42ooyr+668FiKEEF54b2fowQz9DgP6+DQ31USf0uJORl4p9VqyZf2E/k56jbXpGqgP5ec/kosUqAGJQC4oYA1VETfPs/Ofn7xnbVg7bUBr489XNjjvQ8KpbQvnbozTnHnl/vq2XBlGUH1mmF7/w2WYmn2X3m0M6l7XwWwdsmNyKxJmYeHF50Y5jitYePPEZPsvV9+CrP2DfRZz/0ra30+ziVz5gc+oP/+RnKVADUgEUkABa6jwHipT8ufciVvh9v1oSfWz/7X3Wqu9rTxmjAwj4F9b2HHB1eoffkap07rra/1J2NKcf2ms6XBh+NtaC1qrL401D8Yj3oV3p2BbdSBD1J//SM5SoAYkAilgHVhD3Y8X6SnOJAAABCZJREFUtl48yqXOsX4lxxHBnmtW3UdIlgnM8l8b92itDA8gEQxDVTnvC8XI6KsxErzw3bsKmpkcdOcGBKP+/EfylgI1IBHIAAOxDVVSTjMw1q/n75Gma2TAKCshNyJKMdv07qZ2Y/mI/115U1Vzb49XZV/534il11S79vJo7H1rwHfg/K8bJAIp4A6s4TAaVv94No2mWGPdmEbvDfvnpA9d2dV6raaxuSGnMvfN+0KBlvecwxt6UzArDMgcnP91gUQgBRQwQDBMr/Pa22nBV0+fj3vxrliopG3u2LZHbz87DRg3AYoEEoEEMImjoYKUaeeU9TXqW9VRXZJf1UtUFUVqTHIBry7OzS2sZmjoG2opw1B1UwXn/89AIsgQ3IE1VLdJIfqCH36C6UpSKHICL38ZuXJeaMSVl5/4OEIYU9O+0+glG1cMbaFCdWiAcHD+1wsSQfbgDgwQS/z2wMDWo6/r9Js8KaCtvT6zLCft0Zmwv04V+Ec8ih5mAlegQDFAIpABChgglChplbvH4fYxD7f4qX95Uo2X3pzp0f3Wb4/il7aEBwBAEUAikAKuAwChhBlpbzT9+viofz3PClPz7uuvk5WWIaQsLgBIBYlACihggFA0bV2N0pSXOeJvXhXnJqcUa+lqwekGFAQkAingLxIQitlq0CDz+8sCJu2+lZZfIURIUPz69p6QgYvvmAUGtYL1m0BBQCKQAp6BAaLxkvdPHjLjQGKxCNEYdCQS4jR1x2GbI3eNbEHm5hoAUAsSQfaggAFZEJdlPbp9Nym7SMDUNG3p5ethqQo3+0DxQCLIFhQwAAAAjRJcDQDCCPMTLx0O+/vAxaRPQoSEb84t6eduoadjZOfRK2Tn/U/in38DAE0AJAJp4A4MEKPi4e99+iy+kSfEEcYwHfj3VocdQ9cXuPXt4aZZ8vTi6TuFbuvu3pjbEp5eg6YNEoFUOAANJ3rzV0cOq1nwvvi3eW8f7h1uy2ayjIZG54lxHMdxccGpYBOmzZy7fIrDBEC2IBHIBUOIgAiCxAdPRF5TVg93N9EzaT1iVUhrjNa6exe9mlWcmE6n7h7MnLcfYPAENG2QCOSCZr6AELwqHk3PQK/mgoimb6hPo9GV/2tRzlRWpuN8SFvQ1EEikAruwABRsP82L6T9aI9DAJo0SATyQAEDAADQKMEQIiAIXn1jRVe/7RhCCIkLXvKr0Zc/IoR/TK5C7akMDwByQCKQCAoYIAJN16F9uwL+f39s0U736/cxneZtjRx04YYfNG2QCOSCdWAAAAAaJbgSAAAA0ChBAQMAANAoQQEDAADQKEEBAwAA0ChBAQMAANAoQQEDAADQKEEBAwAA0ChBAQMAANAoQQEDAADQKEEBAwAA0ChBAQMAANAoQQEDAADQKEEBAwAA0ChBAQMAANAo/R/HzPdZ0G7+SgAAAABJRU5ErkJggg==" style="display: block; margin: auto;" /></p>
+<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="fu">str</span>(dataset)</span>
+<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; &#39;data.frame&#39;:    62 obs. of  4 variables:</span></span>
+<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  $ Dream  : num  NA 2 NA NA 1.8 0.7 3.9 1 3.6 1.4 ...</span></span>
+<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  $ NonD   : num  NA 6.3 NA NA 2.1 9.1 15.8 5.2 10.9 8.3 ...</span></span>
+<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  $ BodyWgt: num  8.803 0 1.2194 -0.0834 7.8427 ...</span></span>
+<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  $ Span   : num  3.65 1.5 2.64 NA 4.23 ...</span></span></code></pre></div>
+</div>
+<div id="imputation" class="section level2">
+<h2>Imputation</h2>
+<p>In order to invoke the imputation methods, a formula is used to
+specify which variables are to be estimated and which variables should
+be used as regressors. We will start by imputing <code>NonD</code> based
+in <code>BodyWgt</code> and <code>Span</code>.</p>
+<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>imp_regression <span class="ot">&lt;-</span> <span class="fu">regressionImp</span>(NonD <span class="sc">~</span> BodyWgt <span class="sc">+</span> Span, dataset)</span>
+<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; There still missing values in variable NonD . Probably due to missing values in the regressors.</span></span>
+<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>imp_ranger <span class="ot">&lt;-</span> <span class="fu">rangerImpute</span>(NonD <span class="sc">~</span> BodyWgt <span class="sc">+</span> Span, dataset)</span>
+<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a><span class="fu">aggr</span>(imp_regression, <span class="at">delimiter =</span> <span class="st">&quot;_imp&quot;</span>)</span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+<p>We can see that for <code>regrssionImp()</code> there are still
+missings in <code>NonD</code> for all observations where
+<code>Span</code> is unobserved. This is because the regression model
+could not be applied to those observations. The same is true for the
+values imputed via <code>rangerImpute()</code>.</p>
+</div>
+<div id="diagnosing-the-results" class="section level2">
+<h2>Diagnosing the results</h2>
+<p>As we can see in the next two plots, the correlation structure of
+<code>NonD</code> and <code>BodyWgt</code> is preserved by both
+imputation methods. In the case of <code>regressionImp()</code> all
+imputed values almost follow a straight line. This suggests that the
+variable <code>Span</code> had little to no effect on the model.</p>
+<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>imp_regression[, <span class="fu">c</span>(<span class="st">&quot;NonD&quot;</span>, <span class="st">&quot;BodyWgt&quot;</span>, <span class="st">&quot;NonD_imp&quot;</span>)] <span class="sc">|&gt;</span> </span>
+<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">marginplot</span>(<span class="at">delimiter =</span> <span class="st">&quot;_imp&quot;</span>)</span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+<p>For <code>rangerImpute()</code> on the other hand, <code>Span</code>
+played an important role in the generation of the imputed values.</p>
+<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>imp_ranger[, <span class="fu">c</span>(<span class="st">&quot;NonD&quot;</span>, <span class="st">&quot;BodyWgt&quot;</span>, <span class="st">&quot;NonD_imp&quot;</span>)] <span class="sc">|&gt;</span> </span>
+<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">marginplot</span>(<span class="at">delimiter =</span> <span class="st">&quot;_imp&quot;</span>)</span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>imp_ranger[, <span class="fu">c</span>(<span class="st">&quot;NonD&quot;</span>, <span class="st">&quot;Span&quot;</span>, <span class="st">&quot;NonD_imp&quot;</span>)] <span class="sc">|&gt;</span> </span>
+<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">marginplot</span>(<span class="at">delimiter =</span> <span class="st">&quot;_imp&quot;</span>)</span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+</div>
+<div id="imputing-multiple-variables" class="section level2">
+<h2>Imputing multiple variables</h2>
+<p>To impute several variables at once, the formula in
+<code>rangerImpute()</code> and <code>regressionImp()</code> can be
+specified with more than one column name in the left hand side.</p>
+<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>imp_regression <span class="ot">&lt;-</span> <span class="fu">regressionImp</span>(Dream <span class="sc">+</span> NonD <span class="sc">~</span> BodyWgt <span class="sc">+</span> Span, dataset)</span>
+<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; There still missing values in variable Dream . Probably due to missing values in the regressors.</span></span>
+<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; There still missing values in variable NonD . Probably due to missing values in the regressors.</span></span>
+<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a>imp_ranger <span class="ot">&lt;-</span> <span class="fu">rangerImpute</span>(Dream <span class="sc">+</span> NonD <span class="sc">~</span> BodyWgt <span class="sc">+</span> Span, dataset)</span>
+<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a><span class="fu">aggr</span>(imp_regression, <span class="at">delimiter =</span> <span class="st">&quot;_imp&quot;</span>)</span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+<p>Again, there are missings left for both <code>Dream</code> and
+<code>NonD</code>.</p>
+</div>
+<div id="performance-of-method" class="section level2">
+<h2>Performance of method</h2>
+<p>In order to validate the performance of <code>regressionImp()</code>
+the <code>iris</code> dataset is used. Firstly, some values are randomly
+set to <code>NA</code>.</p>
+<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(reactable)</span>
+<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(iris)</span>
+<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> iris</span>
+<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a><span class="fu">colnames</span>(df) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;S.Length&quot;</span>,<span class="st">&quot;S.Width&quot;</span>,<span class="st">&quot;P.Length&quot;</span>,<span class="st">&quot;P.Width&quot;</span>,<span class="st">&quot;Species&quot;</span>)</span>
+<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a><span class="co"># randomly produce some missing values in the data</span></span>
+<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">1</span>)</span>
+<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a>nbr_missing <span class="ot">&lt;-</span> <span class="dv">50</span></span>
+<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a>y <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">row=</span><span class="fu">sample</span>(<span class="fu">nrow</span>(iris),<span class="at">size =</span> nbr_missing,<span class="at">replace =</span> T),</span>
+<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a>                <span class="at">col=</span><span class="fu">sample</span>(<span class="fu">ncol</span>(iris)<span class="sc">-</span><span class="dv">1</span>,<span class="at">size =</span> nbr_missing,<span class="at">replace =</span> T))</span>
+<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a>y<span class="ot">&lt;-</span>y[<span class="sc">!</span><span class="fu">duplicated</span>(y),]</span>
+<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a>df[<span class="fu">as.matrix</span>(y)]<span class="ot">&lt;-</span><span class="cn">NA</span></span>
+<span id="cb8-13"><a href="#cb8-13" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb8-14"><a href="#cb8-14" aria-hidden="true" tabindex="-1"></a><span class="fu">aggr</span>(df)</span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">sapply</span>(df, <span class="cf">function</span>(x)<span class="fu">sum</span>(<span class="fu">is.na</span>(x)))</span>
+<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; S.Length  S.Width P.Length  P.Width  Species </span></span>
+<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;       12       10       13       12        0</span></span></code></pre></div>
+<p>We can see that there are missings in all variables and some
+observations reveal missing values on several points. In the next step
+we perform a multiple variable imputation and <code>Species</code>
+serves as a regressor.</p>
+<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>imp_regression <span class="ot">&lt;-</span> <span class="fu">regressionImp</span>(S.Length <span class="sc">+</span> S.Width <span class="sc">+</span> P.Length <span class="sc">+</span> P.Width <span class="sc">~</span> Species, df)</span>
+<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a><span class="fu">aggr</span>(imp_regression, <span class="at">delimiter =</span> <span class="st">&quot;imp&quot;</span>)</span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+<p>The plot indicates that all missing values have been imputed by the
+<code>regressionImp()</code> algorithm. The following table displays the
+rounded first five results of the imputation for all variables.</p>
+<div class="reactable html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-866e13789732348fd263" style="width:auto;height:auto;"></div>
+<script type="application/json" data-for="htmlwidget-866e13789732348fd263">{"x":{"tag":{"name":"Reactable","attribs":{"data":{"TRUE1":[4.4,7,5.4,6.1,6.5],"IMPUTED1":[5.02,5.92,5.02,5.92,6.56],"TRUE2":[2.5,3,3,3.2,3.4],"IMPUTED2":[2.78,2.98,2.98,2.98,3.45],"TRUE3":[4.1,5.6,1.4,1.3,5.1],"IMPUTED3":[4.26,5.57,1.47,1.47,5.57],"TRUE4":[0.1,1.5,2.1,1.5,0.2],"IMPUTED4":[0.25,1.31,2.02,1.31,0.25]},"columns":[{"id":"TRUE1","name":"True","type":"numeric"},{"id":"IMPUTED1","name":"Imputed","type":"numeric"},{"id":"TRUE2","name":"True","type":"numeric"},{"id":"IMPUTED2","name":"Imputed","type":"numeric"},{"id":"TRUE3","name":"True","type":"numeric"},{"id":"IMPUTED3","name":"Imputed","type":"numeric"},{"id":"TRUE4","name":"True","type":"numeric"},{"id":"IMPUTED4","name":"Imputed","type":"numeric"}],"columnGroups":[{"name":"S.Length","columns":["TRUE1","IMPUTED1"]},{"name":"S.Width","columns":["TRUE2","IMPUTED2"]},{"name":"P.Length","columns":["TRUE3","IMPUTED3"]},{"name":"P.Width","columns":["TRUE4","IMPUTED4"]}],"highlight":true,"bordered":true,"striped":true,"dataKey":"e81afb282c46c4abfe771448f14986c9"},"children":[]},"class":"reactR_markup"},"evals":[],"jsHooks":[]}</script>
+</div>
+
+
+
+<!-- code folding -->
+
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+  (function () {
+    var script = document.createElement("script");
+    script.type = "text/javascript";
+    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+    document.getElementsByTagName("head")[0].appendChild(script);
+  })();
+</script>
+
+</body>
+</html>
diff --git a/man/irmi.Rd b/man/irmi.Rd
index 94782a0..064d3b9 100644
--- a/man/irmi.Rd
+++ b/man/irmi.Rd
@@ -17,7 +17,7 @@ irmi(
   noise = TRUE,
   noise.factor = 1,
   force = FALSE,
-  robMethod = "MM",
+  robMethod = "lmrob",
   force.mixed = TRUE,
   mi = 1,
   addMixedFactors = FALSE,
@@ -62,7 +62,8 @@ residuals.}
 \item{force}{if TRUE, the algorithm tries to find a solution in any case,
 possible by using different robust methods automatically.}
 
-\item{robMethod}{regression method when the response is continuous.}
+\item{robMethod}{regression method when the response is continuous. Default is
+MM-regression with \code{lmrob}.}
 
 \item{force.mixed}{if TRUE, the algorithm tries to find a solution in any
 case, possible by using different robust methods automatically.}
@@ -151,8 +152,6 @@ R package VIM.  \emph{Journal of
 Statistical Software}, 74(7), 1-16.
 }
 \seealso{
-\code{\link[mi:04mi]{mi::mi()}}
-
 Other imputation methods: 
 \code{\link{hotdeck}()},
 \code{\link{impPCA}()},
diff --git a/man/prepare.Rd b/man/prepare.Rd
index 45fbbd3..64df1cc 100644
--- a/man/prepare.Rd
+++ b/man/prepare.Rd
@@ -7,8 +7,8 @@
 prepare(
   x,
   scaling = c("none", "classical", "MCD", "robust", "onestep"),
-  transformation = c("none", "minus", "reciprocal", "logarithm", "exponential",
-    "boxcox", "clr", "ilr", "alr"),
+  transformation = c("none", "minus", "reciprocal", "logarithm", "exponential", "boxcox",
+    "clr", "ilr", "alr"),
   alpha = NULL,
   powers = NULL,
   start = 0,
diff --git a/vignettes/VisualImp.Rmd b/vignettes/VisualImp.Rmd
index fa6fe51..e7e98cc 100644
--- a/vignettes/VisualImp.Rmd
+++ b/vignettes/VisualImp.Rmd
@@ -29,7 +29,6 @@ The following example demonstrates the functionality of the plotting functions u
 
 ```{r setup, message=F}
 library(VIM)
-library(magrittr)
 dataset <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")] # dataset with missings
 dataset$BodyWgt <- log(dataset$BodyWgt)
 dataset$Span <- log(dataset$Span)
@@ -140,9 +139,9 @@ dataset <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")]
 dataset$BodyWgt <- log(dataset$BodyWgt)
 dataset$Span <- log(dataset$Span)
 imp_knn <- kNN(dataset, variable = "NonD") 
-dataset[, c("NonD", "Span")] %>% 
+dataset[, c("NonD", "Span")] |> 
   marginplot()
-imp_knn[, c("NonD", "Span", "NonD_imp")] %>% 
+imp_knn[, c("NonD", "Span", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
 ```
 
diff --git a/vignettes/donorImp.Rmd b/vignettes/donorImp.Rmd
index 2b80854..5d74498 100644
--- a/vignettes/donorImp.Rmd
+++ b/vignettes/donorImp.Rmd
@@ -30,7 +30,6 @@ The following example demonstrates the functionality of `hodeck()` and `kNN()` u
 
 ```{r setup, message=F}
 library(VIM)
-library(magrittr)
 dataset <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")]
 dataset$BodyWgt <- log(dataset$BodyWgt)
 dataset$Span <- log(dataset$Span)
@@ -65,15 +64,15 @@ The specified variables in `matchImpute()` serve as a donor and enable imputatio
 As we can see in the next two plots, the origninal data structure of `NonD` and `Span` is preserved by `hotdeck()`. `kNN()` reveals the typically procedure of methods, which are based on similar data points weighted by the distance. 
 
 ```{r, fig.height=5}
-imp_hotdeck[, c("NonD", "Span", "NonD_imp")] %>% 
+imp_hotdeck[, c("NonD", "Span", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
-imp_knn[, c("NonD", "Span", "NonD_imp")] %>% 
+imp_knn[, c("NonD", "Span", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
 ```
 
 `matchImpute()` works by sampling values from the suitable donors and also provides reasonable results.
 ```{r, fig.height=5}
-imp_match[, c("NonD", "Span", "NonD_imp")] %>% 
+imp_match[, c("NonD", "Span", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
 ```
 
diff --git a/vignettes/irmi.Rmd b/vignettes/irmi.Rmd
index 6395143..f27bb94 100644
--- a/vignettes/irmi.Rmd
+++ b/vignettes/irmi.Rmd
@@ -31,7 +31,6 @@ The following example demonstrates the functionality of `irmi()` using a subset
 
 ```{r setup, message=F}
 library(VIM)
-library(magrittr)
 dataset <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")]
 dataset$BodyWgt <- log(dataset$BodyWgt)
 dataset$Span <- log(dataset$Span)
@@ -61,14 +60,14 @@ We can see that `irmi()` imputed all missing values for all variables in our dat
 As we can see in the next plot, for imputing missing values in `NonD` `Bodygt` plays an important role. The original data structure of `NonD` and `BodyWgt` is preserved by the `irmi()` imputation method.
 
 ```{r, fig.height=5}
-imp_irmi[, c("NonD", "BodyWgt", "NonD_imp")] %>% 
+imp_irmi[, c("NonD", "BodyWgt", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
 ```
 
 The same is true for the data structure of `Span` and `BodyWgt`.
 
 ```{r, fig.height=5}
-imp_irmi[, c("Span", "BodyWgt", "Span_imp")] %>% 
+imp_irmi[, c("Span", "BodyWgt", "Span_imp")] |> 
   marginplot(delimiter = "_imp")
 ```
 
diff --git a/vignettes/modelImp.Rmd b/vignettes/modelImp.Rmd
index 5fb3eb1..6dc7dda 100644
--- a/vignettes/modelImp.Rmd
+++ b/vignettes/modelImp.Rmd
@@ -28,7 +28,6 @@ deliberately to include some interactions between the missing values.
 
 ```{r setup, message = FALSE}
 library(VIM)
-library(magrittr)
 dataset <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")]
 dataset$BodyWgt <- log(dataset$BodyWgt)
 dataset$Span <- log(dataset$Span)
@@ -61,7 +60,7 @@ As we can see in the next two plots, the correlation structure of `NonD` and
 suggests that the variable `Span` had little to no effect on the model.
 
 ```{r, fig.height=5}
-imp_regression[, c("NonD", "BodyWgt", "NonD_imp")] %>% 
+imp_regression[, c("NonD", "BodyWgt", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
 ```
 
@@ -69,9 +68,9 @@ For `rangerImpute()` on the other hand, `Span` played an important role in the
 generation of the imputed values.
 
 ```{r, fig.height=5}
-imp_ranger[, c("NonD", "BodyWgt", "NonD_imp")] %>% 
+imp_ranger[, c("NonD", "BodyWgt", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
-imp_ranger[, c("NonD", "Span", "NonD_imp")] %>% 
+imp_ranger[, c("NonD", "Span", "NonD_imp")] |> 
   marginplot(delimiter = "_imp")
 ```
 

Debdiff

[The following lists of changes regard files as different if they have different names, permissions or owners.]

Files in second set of .debs but not in first

-rw-r--r--  root/root   /usr/lib/R/site-library/VIM/doc/VIM.html
-rw-r--r--  root/root   /usr/lib/R/site-library/VIM/doc/VisualImp.html
-rw-r--r--  root/root   /usr/lib/R/site-library/VIM/doc/donorImp.html
-rw-r--r--  root/root   /usr/lib/R/site-library/VIM/doc/irmi.html
-rw-r--r--  root/root   /usr/lib/R/site-library/VIM/doc/modelImp.html
-rw-r--r--  root/root   /usr/lib/debug/.build-id/31/c1c32adda3b48d995487b37284cdfcf79e5369.debug

Files in first set of .debs but not in second

-rw-r--r--  root/root   /usr/lib/debug/.build-id/12/af0e7c100aa9d3a2910c87c3ae185abd478492.debug

Control files of package r-cran-vim: lines which differ (wdiff format)

• Depends: r-base-core (>= 4.2.1-2), 4.2.2.20221110-1), r-api-4.0, r-cran-colorspace, r-cran-car, r-cran-magrittr, r-cran-robustbase, r-cran-sp, r-cran-vcd, r-cran-mass, r-cran-nnet, r-cran-e1071, r-cran-rcpp, r-cran-laeken, r-cran-ranger, r-cran-mass, r-cran-data.table (>= 1.9.4), libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2)

Control files of package r-cran-vim-dbgsym: lines which differ (wdiff format)

• Build-Ids: 12af0e7c100aa9d3a2910c87c3ae185abd478492 31c1c32adda3b48d995487b37284cdfcf79e5369

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