New Upstream Snapshot - r-cran-goftest

Ready changes

Summary

Merged new upstream version: 1.2-3+git20191127.1.ef942bf (was: 1.2-3).

Resulting package

Built on 2023-01-20T01:25 (took 5m46s)

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-goftest-dbgsymapt install -t fresh-snapshots r-cran-goftest

Lintian Result

Diff

diff --git a/DESCRIPTION b/DESCRIPTION
index 80fa4f2..fb44922 100755
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: goftest
 Type: Package
 Title: Classical Goodness-of-Fit Tests for Univariate Distributions
-Version: 1.2-3
-Date: 2021-10-07
+Version: 1.2-2
+Date: 2019-11-27
 Authors@R: c(person("Julian", "Faraway", role = "aut"),
 	     person("George", "Marsaglia", role = "aut"),
 	     person("John",   "Marsaglia", role = "aut"),
@@ -15,13 +15,11 @@ Description: Cramer-Von Mises and Anderson-Darling tests of goodness-of-fit
 	     efficient algorithms.
 URL: https://github.com/baddstats/goftest
 BugReports: https://github.com/baddstats/goftest/issues
-License: GPL (>= 2)
+License: GPL (>=2)
 NeedsCompilation: yes
-Packaged: 2021-10-07 07:30:39 UTC; adrian
+Packaged: 2023-01-20 01:20:55 UTC; root
 Author: Julian Faraway [aut],
   George Marsaglia [aut],
   John Marsaglia [aut],
   Adrian Baddeley [aut, cre]
 Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>
-Repository: CRAN
-Date/Publication: 2021-10-07 09:20:02 UTC
diff --git a/MD5 b/MD5
deleted file mode 100644
index 1441789..0000000
--- a/MD5
+++ /dev/null
@@ -1,33 +0,0 @@
-29111628fe1b2d270ef5df0c5ff3c6c5 *DESCRIPTION
-15b924eea35078a33ce9a9597f352439 *NAMESPACE
-e45a60eb1d479da9631e0dd3a45d9ec1 *R/RCS/andarl.R,v
-d7a9c3d741437b87b272318a38e01fd7 *R/RCS/cramer.R,v
-f149bfb2b89d8c70e8807f2a5357ee48 *R/RCS/cvmtest.R,v
-56ce831b8afd6f55b7f7180a470226c1 *R/RCS/recog.R,v
-2d55fdb30747e83b2380d5bf777b1898 *R/andarl.R
-e967698c8e63feae74fefd362e996d6d *R/braun.R
-e2fbe5802aff0bcb1ff080f9ce8dbf65 *R/cramer.R
-723adf9bac297d8d04daba9b6df77847 *R/cvmtest.R
-d3146be4396e0b2db19377e00d110794 *R/oldRCS/andarl.R,v
-8052568d7f91b67ab5e8794f559b4cab *R/oldRCS/cramer.R,v
-2c68dc762655bbc1958556cb1d84fa55 *R/oldRCS/cvmtest.R,v
-56ce831b8afd6f55b7f7180a470226c1 *R/oldRCS/recog.R,v
-b5dc530d801f347fcc103ba9a9ef2bd6 *R/recog.R
-29b8a1f246a7963b8d8e9cb77543f0c2 *build/partial.rdb
-4ca09aee4f6ec8772a37f287a23a503a *man/RCS/ad.test.Rd,v
-8c8471c1ed64c58a562fa42ffcd35b6b *man/RCS/cvm.test.Rd,v
-62bc401099f8d0a7522fb4d24b5f71a4 *man/RCS/goftest-package.Rd,v
-6e322a80bcf915758cf6b34edfe8c55c *man/RCS/pAD.Rd,v
-d0f7bb029253ac0d213e7f7be7f21690 *man/RCS/pCvM.Rd,v
-b26573082916c8dc121ae0610198cfcc *man/ad.test.Rd
-c4a9a49bd5eb2b0bc0c0c2d26eed4929 *man/cvm.test.Rd
-aeefaa4a55958c09f7f8cc3ad4dc2eae *man/goftest-package.Rd
-d31fca48d10c5dd9e63be3955c14412d *man/pAD.Rd
-b6294a6cc54d82ceb166326a40f07b4c *man/pCvM.Rd
-a4f4486d8a6e4b08d73001ef3134c2a2 *man/recogniseCdf.Rd
-ca3270c176ce09cd15243bf4df918aa9 *src/ADinf.c
-d7fa22cf49d25bc1ceb8fa5b02dc5aa2 *src/AnDarl.c
-db80933a3060fbea79e3fee3c0e04278 *src/RCS/ADinf.c,v
-ba414da1b88f5baa526cf93958359949 *src/RCS/AnDarl.c,v
-a197e1a03ae0ab8761473c5ad9bc9508 *src/init.c
-84f17e3621a4165a4d306993459dfaa8 *tests/all.R
diff --git a/R/RCS/andarl.R,v b/R/RCS/andarl.R,v
deleted file mode 100755
index 79ed77a..0000000
--- a/R/RCS/andarl.R,v
+++ /dev/null
@@ -1,424 +0,0 @@
-head	1.10;
-access;
-symbols;
-locks
-	adrian:1.10; strict;
-comment	@# @;
-
-
-1.10
-date	2018.06.06.08.25.51;	author adrian;	state Exp;
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-desc
-@@
-
-
-1.10
-log
-@Summary: tweak
-@
-text
-@##
-## andarl.R
-##
-##  Anderson-Darling test and null distribution
-##
-## $Revision: 1.9 $ $Date: 2018/06/06 08:10:11 $
-##
-
-ad.test <- function(x, null="punif", ..., estimated=FALSE, nullname) {
-  xname <- deparse(substitute(x))
-  nulltext <- deparse(substitute(null))
-  if(is.character(null)) nulltext <- null
-  if(missing(nullname) || is.null(nullname)) {
-    reco <- recogniseCdf(nulltext)
-    nullname <- if(!is.null(reco)) reco else 
-                paste("distribution", sQuote(nulltext))
-  }
-  stopifnot(is.numeric(x))
-  x <- as.vector(x)
-  n <- length(x)
-  F0 <- getCdf(null)
-  U <- F0(x, ...)
-  if(any(U < 0 | U > 1))
-    stop("null distribution function returned values outside [0,1]")
-  if(!estimated || n <= 4) {
-    #' simple null hypothesis
-    z <- do.goftest.AD(U)
-    PVAL <- z$pvalue
-    STATISTIC <- z$adstat
-    names(STATISTIC) <- "An"
-  } else {
-    #' composite - use Braun (1980)
-    first <- sample(n, ceiling(n/2), replace=TRUE)
-    z1 <- do.goftest.AD(U[first])
-    z2 <- do.goftest.AD(U[-first])
-    STATISTIC <- max(z1$adstat, z2$adstat)
-    names(STATISTIC) <- "AnMax"
-    PVAL <- 1 - (1 - z1$pvalue) * (1 - z2$pvalue)
-  }
-  METHOD <- c("Anderson-Darling test of goodness-of-fit",
-              if(estimated) "(with Braun's adjustment)" else NULL,
-              paste("Null hypothesis:", nullname))
-
-  extras <- list(...)
-  parnames <- intersect(names(extras), names(formals(F0)))
-  if(length(parnames) > 0) {
-    pars <- extras[parnames]
-    pard <- character(length(parnames))
-    for(i in seq_along(parnames))
-      pard[i] <- paste(parnames[i], "=", paste(pars[[i]], collapse=" "))
-    pard <- paste("with",
-                  ngettext(length(pard), "parameter", "parameters"),
-                  "  ", 
-                  paste(pard, collapse=", "))
-    METHOD <- c(METHOD, pard)
-  }
-  
-  coda <- paste("Parameters assumed to",
-                if(estimated) "have been estimated from data" else "be fixed")
-  METHOD <- c(METHOD, coda)
-  
-  out <- list(statistic = STATISTIC,
-               p.value = PVAL,
-               method = METHOD,
-               data.name = xname)
-  class(out) <- "htest"
-  return(out)
-}
-
-do.goftest.AD <- function(U) {
-  ## Internal: call Marsaglia C code
-  U <- sort(U)
-  n <- length(U)
-  z <- .C(CgofADtestR,
-          x = as.double(U),
-          n = as.integer(n),
-          adstat = as.double(numeric(1)),
-          pvalue = as.double(numeric(1)),
-	  PACKAGE="goftest"
-          )
-  return(z[c("adstat", "pvalue")])
-}
-
-pAD <- function(q, n=Inf, lower.tail=TRUE, fast=TRUE) {
-  q <- as.numeric(q)
-  p <- rep(NA_real_, length(q))
-  if(any(ones <- is.infinite(q) & (q == Inf)))
-    p[ones] <- 1
-  if(any(zeroes <- (is.finite(q) & q <= 0) | (is.infinite(q) & (q == -Inf))))
-    p[zeroes] <- 0
-  ok <- is.finite(q) & (q > 0)
-  nok <- sum(ok)
-  if(nok > 0) {
-    if(is.finite(n)) {
-      z <- .C(CgofADprobN,
-              a       = as.double(q[ok]),
-              na      = as.integer(nok),
-              nsample = as.integer(n),
-              prob    = as.double(numeric(nok)),
-	      PACKAGE="goftest")
-      p[ok] <- z$prob
-    } else if(fast) {
-      ## fast version adinf()
-      z <- .C(CgofADprobApproxInf,
-              a    = as.double(q[ok]),
-              na   = as.integer(nok),
-              prob = as.double(numeric(nok)),
-	      PACKAGE="goftest")
-      p[ok] <- z$prob
-    } else {
-      ## slow, accurate version ADinf()
-      z <- .C(CgofADprobExactInf,
-              a    = as.double(q[ok]),
-              na   = as.integer(nok),
-              prob = as.double(numeric(nok)),
-	      PACKAGE="goftest")
-      p[ok] <- z$prob
-    }
-      
-  }
-  if(!lower.tail)
-    p <- 1 - p
-  return(p)
-}
-
-qAD <- local({
-
-  f <- function(x, N, P, Fast) {
-    pAD(x, N, fast=Fast) - P
-  }
-    
-  qAD <- function(p, n=Inf, lower.tail=TRUE, fast=TRUE) {
-    ## quantiles of null distribution of Anderson-Darling test statistic
-    stopifnot(all(p >= 0))
-    stopifnot(all(p <= 1))
-    if(!lower.tail) p <- 1-p
-    ans <- rep(NA_real_, length(p))
-    for(i in which(p >= 0 & p < 1)) 
-      ans[i] <- uniroot(f, c(0, 1), N=n, P=p[i], Fast=fast, extendInt="up")$root
-    return(ans)
-  }
-
-  qAD
-})
-
-
-  
-
-@
-
-
-1.9
-log
-@Summary: d'oh
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.8 $ $Date: 2018/06/06 08:05:21 $
-d58 2
-a59 2
-  coda <- paste("Parameters assumed to be",
-                if(estimated) "estimated from data" else "fixed")
-@
-
-
-1.8
-log
-@Summary: handles composite case using Braun 1980
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.7 $ $Date: 2018/03/29 13:51:49 $
-d59 1
-a59 1
-                if(!estimated) "estimated from data" else "fixed")
-@
-
-
-1.7
-log
-@Summary: removed unused variable
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.6 $ $Date: 2014/06/24 02:12:20 $
-d9 1
-a9 1
-ad.test <- function(x, null="punif", ..., nullname) {
-d21 1
-a21 3
-  F0 <- if(is.function(null)) null else
-        if(is.character(null)) get(null, mode="function") else
-        stop("Argument 'null' should be a function, or the name of a function")
-d25 15
-a39 12
-  U <- sort(U)
-  ## call Marsaglia C code
-  z <- .C(CgofADtestR,
-          x = as.double(U),
-          n = as.integer(n),
-          adstat = as.double(numeric(1)),
-          pvalue = as.double(numeric(1)),
-	  PACKAGE="goftest"
-          )
-  STATISTIC <- z$adstat
-  names(STATISTIC) <- "An"
-  PVAL <- z$pvalue
-d41 1
-d43 1
-d48 1
-a48 1
-    pard <- character(0)
-d57 5
-d68 14
-@
-
-
-1.6
-log
-@polished output
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.5 $ $Date: 2014/06/24 01:54:16 $
-a27 1
-  k <- seq_len(n)
-d29 1
-a29 1
-  z <- .C("ADtestR",
-d33 2
-a34 1
-          pvalue = as.double(numeric(1))
-d73 1
-a73 1
-      z <- .C("ADprobN",
-d77 2
-a78 2
-              prob    = as.double(numeric(nok))
-              )
-d82 1
-a82 1
-      z <- .C("ADprobApproxInf",
-d85 2
-a86 2
-              prob = as.double(numeric(nok))
-              )
-d90 1
-a90 1
-      z <- .C("ADprobExactInf",
-d93 2
-a94 2
-              prob = as.double(numeric(nok))
-              )
-@
-
-
-1.5
-log
-@recognises standard distributions
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.4 $ $Date: 2014/06/09 05:07:09 $
-d39 15
-a53 2
-  METHOD <- paste("Anderson-Darling test of", nullname)
-  ALTERN <- paste("Not the", nullname)
-a55 1
-               alternative = ALTERN,
-@
-
-
-1.4
-log
-@tweak
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.3 $ $Date: 2014/06/09 05:02:30 $
-d13 3
-a15 2
-  if(missing(nullname))
-    nullname <- if(identical(null, "punif")) "uniform distribution" else
-d17 1
-d50 1
-a50 1
-pAD <- function(q, n=Inf, lower.tail=TRUE) {
-d68 8
-d77 2
-a78 1
-      z <- .C("ADprobInf",
-d85 1
-d94 2
-a95 2
-  f <- function(x, N, P) {
-    pAD(x, N) - P
-d98 1
-a98 1
-  qAD <- function(p, n=Inf, lower.tail=TRUE) {
-d105 1
-a105 1
-      ans[i] <- uniroot(f, c(0, 1), N=n, P=p[i], extendInt="up")$root
-@
-
-
-1.3
-log
-@buglet fix
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.2 $ $Date: 2014/06/09 04:34:57 $
-d35 1
-d91 2
-a92 1
-    for(i in which(p > 0 & p < 1)) 
-@
-
-
-1.2
-log
-@tweaked
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.1 $ $Date: 2014/06/09 04:26:35 $
-d52 1
-a52 1
-  if(any(zeroes <- (is.finite(q) & q < 0) | (is.infinite(q) & (q == -Inf))))
-d54 1
-a54 1
-  ok <- is.finite(q) & (q >= 0)
-@
-
-
-1.1
-log
-@Initial revision
-@
-text
-@d6 1
-a6 1
-## $Revision$ $Date$
-d47 1
-a47 1
-pAnDarl <- function(q, n=Inf, lower.tail=TRUE) {
-d78 23
-@
diff --git a/R/RCS/cramer.R,v b/R/RCS/cramer.R,v
deleted file mode 100755
index 75ac4a1..0000000
--- a/R/RCS/cramer.R,v
+++ /dev/null
@@ -1,398 +0,0 @@
-head	1.9;
-access;
-symbols;
-locks
-	adrian:1.9; strict;
-comment	@# @;
-
-
-1.9
-date	2019.11.27.01.50.20;	author adrian;	state Exp;
-branches;
-next	1.8;
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-date	2019.11.26.04.05.19;	author adrian;	state Exp;
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-@
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-@##
-## cramer.R
-##
-## Distribution of the Cramer-Von Mises test statistic
-##
-## $Revision: 1.8 $ $Date: 2019/11/26 04:05:19 $
-##
-## ..................................................................
-##
-##      From Matlab code written by Julian Faraway (faraway@@umich.edu)
-##	Translated to R by Adrian Baddeley
-##
-##	Reference: S. Csorgo and J.J. Faraway,
-##      The exact and asymptotic distributions of Cramer-von Mises statistics
-##	Journal of the Royal Statistical Society, Series B
-##      58 (1996) 221-234.
-##
-
-pCvM <- local({
-
-  ## all functions are vectorised
-  D2 <- function(x) {
-    z <- (x^2)/4
-    b <- besselK(x=z, nu=1/4) + besselK(x=z, nu=3/4)
-    b * sqrt((x^3)/(8*pi))
-  }
-
-  D3 <- function(x) {
-    z <- (x^2)/4
-    b <- 2*besselK(z, nu=1/4) + 3*besselK(z, nu=3/4) - besselK(z, nu=5/4)
-    b * sqrt((x^5)/(32 * pi))
-  }
-
-  ED2 <- function(x) { exp(-(x^2)/4) * D2(x) }
-
-  ED3 <- function(x) { exp(-(x^2)/4) * D3(x) }
-
-  Ak <- function(k, x) {
-    #' original code (transliterated from Matlab) for reference
-    twosqrtx <- 2 * sqrt(x)
-    x34 <- x^(3/4)
-    x54 <- x^(5/4)
-    (2*k+1)*gamma(k+1/2)*ED2((4*k+3)/twosqrtx)/(9*x34) +
-      gamma(k+1/2)*ED3((4*k+1)/twosqrtx)/(72*x54) +
-        2*(2*k+3)*gamma(k+3/2)*ED3((4*k+5)/twosqrtx)/(12*x54) +
-          7*(2*k+1)*gamma(k+1/2)*ED2((4*k+1)/twosqrtx)/(144*x34) +
-            7*(2*k+1)*gamma(k+1/2)*ED2((4*k+5)/twosqrtx)/(144*x34)
-  }
-
-  AkOnFk <- function(k, x) {
-    #' calculates A(k, x)/factorial(k)
-    #' Adrian Baddeley, 26 nov 2019
-    twosqrtx <- 2 * sqrt(x)
-    fk1x <- (4*k+1)/twosqrtx
-    fk3x <- (4*k+3)/twosqrtx
-    fk5x <- (4*k+5)/twosqrtx
-    x34 <- x^(3/4)
-    x54 <- x^(5/4)
-    #'evaluate gamma(k+1/2)/factorial(k) = gamma(k+1/2)/gamma(k+1)
-    gf <- if(k < 100) {
-            gamma(k+1/2)/factorial(k)
-          } else if(k <= 1e15) {
-            exp(lgamma(k+1/2)-lgamma(k+1))
-          } else exp(-10*k)
-    gf * (
-      ED3(fk1x)/(72*x54) +
-      (2*k+1) * (
-        ED2(fk3x)/(9*x34) +
-        (2*k+3)*ED3(fk5x)/(12*x54) +
-        7*(ED2(fk1x)+ED2(fk5x))/(144*x34)
-      )
-    )
-  }
-
-  psi1 <- function(x) {
-    ## Leading term in expansion of small-sample cdf of Cramer-Von Mises
-    m <- length(x)
-    tot <- numeric(m)
-    active <- rep(TRUE, m)
-    for(k in 0:200) {
-      ## WAS:      z <- -Ak(k,x[active])/(pi*factorial(k))
-      z <- -AkOnFk(k,x[active])/pi
-      tot[active] <- tot[active] + z
-      active[active] <- (abs(z) >= 1e-9)
-      if((k > 20) && (ok <- !any(active))) break
-    }
-    if(!ok)
-      warning("Series did not converge after 200 iterations (small sample cdf)",
-              call.=FALSE)
-    return(tot + Vinf(x)/12)
-  }
-
-  Vinf <- function(x) {
-    ## cdf of asymptotic distribution of Cramer-von Mises
-    m <- length(x)
-    tot <- numeric(m)
-    active <- rep(TRUE, m)
-    for(k in 0:200) {
-      q <- (4*k+1)^2/(16*x[active])
-      z <- ((-1)^k)*choose(-1/2,k)*sqrt(4*k+1)*
-        exp(-q)*besselK(q, nu=1/4)/sqrt(x[active])
-      tot[active] <- tot[active] + z
-      active[active] <- (abs(z) >= 1e-9)
-      if((k > 10) && (ok <- !any(active))) break
-    }
-    if(!ok)
-      warning("Series did not converge after 200 iterations (asymptotic cdf)",
-              call.=FALSE)
-    return(tot/pi)
-  }
-
-  Vn <- function(x, n) {
-    ## cdf of small-sample distribution of Cramer-von Mises statistic
-    ## First order approximation, Csorgo and Faraway equation (1.8)
-    Vinf(x) + psi1(x)/n
-  }
-    
-  pCvM <- function(q, n=Inf, lower.tail=TRUE) {
-    ## cdf of null distribution of Cramer-von Mises test statistic
-    nn <- min(100, n)
-    lower <- 1/(12 * nn)
-    upper <- nn/3
-    m <- length(q)
-    p <- numeric(m)
-    unknown <- rep(TRUE, m)
-    if(any(zeroes <- (q <= lower))) {
-      p[zeroes] <- 0
-      unknown[zeroes] <- FALSE
-    }
-    if(any(ones <- (q >= upper))) {
-      p[ones] <- 1
-      unknown[ones] <- FALSE
-    }
-    if(any(unknown))
-      p[unknown] <- if(is.infinite(n)) Vinf(q[unknown]) else Vn(q[unknown], n)
-    p[p < 2e-10] <- 0
-    p[(1-p) < 2e-10] <- 1
-    return(if(lower.tail) p else 1-p)
-  }
-
-  pCvM
-})
-
-qCvM <- local({
-
-  f <- function(x, N, P) {
-    pCvM(x, N) - P
-  }
-    
-  qCvM <- function(p, n=Inf, lower.tail=TRUE) {
-    ## quantiles of null distribution of Cramer-von Mises test statistic
-    stopifnot(all(p >= 0))
-    stopifnot(all(p <= 1))
-    if(!lower.tail) p <- 1-p
-    lower <- if(is.finite(n)) (1/(12 * n)) else 0
-    upper <- if(is.finite(n)) n/3 else Inf
-    ans <- numeric(length(p))
-    small <- (p <= 2e-10)
-    large <- (1-p <= 2e-10)
-    ans[small] <- lower
-    ans[large] <- upper
-    for(i in which(!small & !large))
-      ans[i] <- uniroot(f, c(lower, 1), N=n, P=p[i], extendInt="up")$root
-    return(ans)
-  }
-
-  qCvM
-})
-
-
-  
-
-@
-
-
-1.8
-log
-@Summary: more tweaks
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.7 $ $Date: 2019/11/26 03:59:06 $
-d136 2
-a137 2
-    p[p < 2e-11] <- 0
-    p[(1-p) < 2e-11] <- 1
-d158 2
-a159 2
-    small <- (p <= 2e-11)
-    large <- (1-p <= 2e-11)
-@
-
-
-1.7
-log
-@Summary: more tweaks
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.6 $ $Date: 2019/11/26 03:55:16 $
-d85 1
-a85 1
-      if(ok <- !any(active)) break
-d104 1
-a104 1
-      if(ok <- !any(active)) break
-@
-
-
-1.6
-log
-@Summary: more numerical stabilisation
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.5 $ $Date: 2019/11/26 03:49:24 $
-d158 5
-a162 3
-    ans[p == 0] <- lower
-    ans[p == 1] <- upper
-    for(i in which(p > 0 & p < 1)) 
-@
-
-
-1.5
-log
-@Summary: more tweaks
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.4 $ $Date: 2019/11/26 03:31:49 $
-d136 2
-@
-
-
-1.4
-log
-@Summary: d'oh
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.3 $ $Date: 2019/11/26 03:29:57 $
-d84 1
-a84 1
-      active[active] <- (abs(z) >= 1e-7)
-d103 1
-a103 1
-      active[active] <- (abs(z) >= 1e-7)
-d120 3
-a122 7
-    if(is.finite(n)) {
-      lower <- 1/(12 * n)
-      upper <- n/3
-    } else {
-      lower <- 0
-      upper <- Inf
-    }
-@
-
-
-1.3
-log
-@Summary: bug fixes
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.2 $ $Date: 2014/06/09 04:34:49 $
-d71 1
-@
-
-
-1.2
-log
-@renamed
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.1 $ $Date: 2014/06/08 10:20:20 $
-d39 1
-d50 24
-d79 3
-a81 2
-    for(k in 0:20) {
-      z <- -Ak(k,x[active])/(pi*factorial(k))
-d84 1
-a84 1
-      if(!any(active)) break
-d86 3
-d97 1
-a97 1
-    for(k in 0:10) {
-d103 1
-a103 1
-      if(!any(active)) break
-d105 3
-@
-
-
-1.1
-log
-@Initial revision
-@
-text
-@d6 1
-a6 1
-## $Revision$ $Date$
-d19 1
-a19 1
-pcramer <- local({
-d85 1
-a85 1
-  pcramer <- function(q, n=Inf, lower.tail=TRUE) {
-d110 1
-a110 1
-  pcramer
-d113 1
-a113 1
-qcramer <- local({
-d116 1
-a116 1
-    pcramer(x, N) - P
-d119 1
-a119 1
-  qcramer <- function(p, n=Inf, lower.tail=TRUE) {
-d134 1
-a134 1
-  qcramer
-@
diff --git a/R/RCS/cvmtest.R,v b/R/RCS/cvmtest.R,v
deleted file mode 100755
index 7a186df..0000000
--- a/R/RCS/cvmtest.R,v
+++ /dev/null
@@ -1,249 +0,0 @@
-head	1.7;
-access;
-symbols;
-locks
-	adrian:1.7; strict;
-comment	@# @;
-
-
-1.7
-date	2018.06.06.08.25.46;	author adrian;	state Exp;
-branches;
-next	1.6;
-
-1.6
-date	2018.06.06.08.09.39;	author adrian;	state Exp;
-branches;
-next	1.5;
-
-1.5
-date	2018.06.06.08.05.35;	author adrian;	state Exp;
-branches;
-next	1.4;
-
-1.4
-date	2014.06.24.02.13.27;	author adrian;	state Exp;
-branches;
-next	1.3;
-
-1.3
-date	2014.06.24.01.54.26;	author adrian;	state Exp;
-branches;
-next	1.2;
-
-1.2
-date	2014.06.08.11.32.51;	author adrian;	state Exp;
-branches;
-next	1.1;
-
-1.1
-date	2014.06.08.11.05.58;	author adrian;	state Exp;
-branches;
-next	;
-
-
-desc
-@@
-
-
-1.7
-log
-@Summary: tweak
-@
-text
-@##
-## cvmtest.R
-##
-## Cramer-von Mises test
-##
-## $Revision: 1.6 $ $Date: 2018/06/06 08:09:39 $
-##
-
-cvm.test <- function(x, null="punif", ..., estimated=FALSE, nullname) {
-  xname <- deparse(substitute(x))
-  nulltext <- deparse(substitute(null))
-  if(is.character(null)) nulltext <- null
-  if(missing(nullname) || is.null(nullname)) {
-    reco <- recogniseCdf(nulltext)
-    nullname <- if(!is.null(reco)) reco else 
-                paste("distribution", sQuote(nulltext))
-  }
-  stopifnot(is.numeric(x))
-  x <- as.vector(x)
-  n <- length(x)
-  F0 <- getCdf(null)
-  U <- F0(x, ...)
-  if(any(U < 0 | U > 1))
-    stop("null distribution function returned values outside [0,1]")
-  if(!estimated || n <= 4) {
-    #' simple null hypothesis
-    z <- do.goftest.CvM(U)
-    PVAL <- z$pvalue
-    STATISTIC <- z$omega2
-    names(STATISTIC) <- "omega2"
-  } else {
-    #' composite - use Braun (1980)
-    first <- sample(n, ceiling(n/2), replace=TRUE)
-    z1 <- do.goftest.CvM(U[first])
-    z2 <- do.goftest.CvM(U[-first])
-    PVAL <- 1 - (1 - z1$pvalue) * (1 - z2$pvalue)
-    STATISTIC <- max(z1$omega2, z2$omega2)
-    names(STATISTIC) <- "omega2max"
-  }
-  METHOD <- c("Cramer-von Mises test of goodness-of-fit",
-              if(estimated) "(with Braun's adjustment)" else NULL,
-              paste("Null hypothesis:", nullname))
-
-  extras <- list(...)
-  parnames <- intersect(names(extras), names(formals(F0)))
-  if(length(parnames) > 0) {
-    pars <- extras[parnames]
-    pard <- character(0)
-    for(i in seq_along(parnames))
-      pard[i] <- paste(parnames[i], "=", paste(pars[[i]], collapse=" "))
-    pard <- paste("with",
-                  ngettext(length(pard), "parameter", "parameters"),
-                  "  ", 
-                  paste(pard, collapse=", "))
-    METHOD <- c(METHOD, pard)
-  }
-
-  coda <- paste("Parameters assumed to",
-                if(estimated) "have been estimated from data" else "be fixed")
-  METHOD <- c(METHOD, coda)
-
-  out <- list(statistic = STATISTIC,
-               p.value = PVAL,
-               method = METHOD,
-               data.name = xname)
-  class(out) <- "htest"
-  return(out)
-}
-
-#' not exported
-
-do.goftest.CvM <- function(U) {
-  U <- sort(U)
-  n <- length(U)
-  k <- seq_len(n)
-  omega2 <- 1/(12 * n) + sum((U - (2*k - 1)/(2*n))^2)
-  pvalue <- pCvM(omega2, n=n, lower.tail=FALSE)
-  return(list(omega2=omega2, pvalue=pvalue))
-}
-@
-
-
-1.6
-log
-@Summary: d'oh
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.5 $ $Date: 2018/06/06 08:05:35 $
-d58 2
-a59 2
-  coda <- paste("Parameters assumed to be",
-                if(estimated) "estimated from data" else "fixed")
-@
-
-
-1.5
-log
-@Summary: handles composite case using Braun 1980
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.4 $ $Date: 2014/06/24 02:13:27 $
-d59 1
-a59 1
-                if(!estimated) "estimated from data" else "fixed")
-d62 1
-a62 1
-  out <- list(statistic = omega2,
-@
-
-
-1.4
-log
-@polished output
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.3 $ $Date: 2014/06/24 01:54:26 $
-d9 1
-a9 1
-cvm.test <- function(x, null="punif", ..., nullname) {
-d21 1
-a21 3
-  F0 <- if(is.function(null)) null else
-        if(is.character(null)) get(null, mode="function") else
-        stop("Argument 'null' should be a function, or the name of a function")
-d25 15
-a39 5
-  U <- sort(U)
-  k <- seq_len(n)
-  omega2 <- 1/(12 * n) + sum((U - (2*k - 1)/(2*n))^2)
-  PVAL <- pCvM(omega2, n=n, lower.tail=FALSE)
-  names(omega2) <- "omega2"
-d41 1
-d43 1
-d57 5
-d70 10
-@
-
-
-1.3
-log
-@recognises standard distributions
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.2 $ $Date: 2014/06/08 11:32:51 $
-d33 14
-a46 1
-              paste("to", nullname))
-@
-
-
-1.2
-log
-@minor
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.1 $ $Date: 2014/06/08 11:05:58 $
-d13 3
-a15 2
-  if(missing(nullname))
-    nullname <- if(identical(null, "punif")) "uniform distribution" else
-d17 1
-d30 1
-a30 1
-  PVAL <- pcvm(omega2, n=n, lower.tail=FALSE)
-d32 2
-a33 2
-  METHOD <- paste("Cramer-von Mises test of", nullname)
-  ALTERN <- paste("Not the", nullname)
-a35 1
-               alternative = ALTERN,
-@
-
-
-1.1
-log
-@Initial revision
-@
-text
-@d6 1
-a6 1
-## $Revision$ $Date$
-d10 2
-a11 2
-  xname <- short.deparse(substitute(x))
-  nulltext <- short.deparse(substitute(null))
-@
diff --git a/R/RCS/recog.R,v b/R/RCS/recog.R,v
deleted file mode 100755
index dce9ea5..0000000
--- a/R/RCS/recog.R,v
+++ /dev/null
@@ -1,124 +0,0 @@
-head	1.4;
-access;
-symbols;
-locks
-	adrian:1.4; strict;
-comment	@# @;
-
-
-1.4
-date	2014.06.24.02.13.35;	author adrian;	state Exp;
-branches;
-next	1.3;
-
-1.3
-date	2014.06.24.01.55.53;	author adrian;	state Exp;
-branches;
-next	1.2;
-
-1.2
-date	2014.06.24.01.49.05;	author adrian;	state Exp;
-branches;
-next	1.1;
-
-1.1
-date	2014.06.24.01.48.14;	author adrian;	state Exp;
-branches;
-next	;
-
-
-desc
-@@
-
-
-1.4
-log
-@neatened
-@
-text
-@##  recog.R
-##
-## $Revision: 1.3 $ $Date: 2014/06/24 01:55:53 $
-##
-
-recogniseCdf <- function(s="punif") {
-  if(!is.character(s) || length(s) != 1) return(NULL)
-  if(nchar(s) <= 1 || substr(s,1,1) != "p") return(NULL)
-  root <- substr(s, 2, nchar(s))
-  a <- switch(root,
-              beta     = "beta",
-              binom    = "binomial",
-              birthday = "birthday coincidence",
-              cauchy   = "Cauchy",
-              chisq    = "chi-squared",
-              exp      = "exponential",
-              f        = "F",
-              gamma    = "Gamma",
-              geom     = "geometric",
-              hyper    = "hypergeometric",
-              lnorm    = "log-normal",
-              logis    = "logistic",
-              nbinom   = "negative binomial",
-              norm     = "Normal",
-              pois     = "Poisson",
-              t        = "Student's t",
-              tukey    = "Tukey (Studentized range)",
-              unif     = "uniform",
-              weibull  = "Weibull",
-              NULL)
-  if(!is.null(a))
-    return(paste(a, "distribution"))
-  b <- switch(root,
-              AD     = "Anderson-Darling",
-              CvM    = "Cramer-von Mises",
-              wilcox = "Wilcoxon Rank Sum",
-              NULL)
-  if(!is.null(b))
-    return(paste("null distribution of", b, "Test Statistic"))
-  return(NULL)
-}
-
-         
-@
-
-
-1.3
-log
-@minor
-@
-text
-@d3 1
-a3 1
-## $Revision: 1.2 $ $Date: 2014/06/24 01:49:05 $
-d6 1
-a6 1
-recogniseCdf <- function(s) {
-@
-
-
-1.2
-log
-@bug fix
-@
-text
-@d3 1
-a3 1
-## $Revision: 1.1 $ $Date: 2014/06/24 01:48:14 $
-d35 1
-a35 1
-              CvM    = "Cramer-von Mises"
-@
-
-
-1.1
-log
-@Initial revision
-@
-text
-@d3 1
-a3 1
-## $Revision$ $Date$
-d8 1
-a8 1
-  if(substr(s,1,1) != "p" || nchar(s) > 1) return(NULL)
-@
diff --git a/R/oldRCS/andarl.R,v b/R/oldRCS/andarl.R,v
deleted file mode 100755
index 4cb4dae..0000000
--- a/R/oldRCS/andarl.R,v
+++ /dev/null
@@ -1,273 +0,0 @@
-head	1.6;
-access;
-symbols;
-locks
-	adrian:1.6; strict;
-comment	@# @;
-
-
-1.6
-date	2014.06.24.02.12.20;	author adrian;	state Exp;
-branches;
-next	1.5;
-
-1.5
-date	2014.06.24.01.54.16;	author adrian;	state Exp;
-branches;
-next	1.4;
-
-1.4
-date	2014.06.09.05.07.09;	author adrian;	state Exp;
-branches;
-next	1.3;
-
-1.3
-date	2014.06.09.05.02.30;	author adrian;	state Exp;
-branches;
-next	1.2;
-
-1.2
-date	2014.06.09.04.34.57;	author adrian;	state Exp;
-branches;
-next	1.1;
-
-1.1
-date	2014.06.09.04.26.35;	author adrian;	state Exp;
-branches;
-next	;
-
-
-desc
-@@
-
-
-1.6
-log
-@polished output
-@
-text
-@##
-## andarl.R
-##
-##  Anderson-Darling test and null distribution
-##
-## $Revision: 1.5 $ $Date: 2014/06/24 01:54:16 $
-##
-
-ad.test <- function(x, null="punif", ..., nullname) {
-  xname <- deparse(substitute(x))
-  nulltext <- deparse(substitute(null))
-  if(is.character(null)) nulltext <- null
-  if(missing(nullname) || is.null(nullname)) {
-    reco <- recogniseCdf(nulltext)
-    nullname <- if(!is.null(reco)) reco else 
-                paste("distribution", sQuote(nulltext))
-  }
-  stopifnot(is.numeric(x))
-  x <- as.vector(x)
-  n <- length(x)
-  F0 <- if(is.function(null)) null else
-        if(is.character(null)) get(null, mode="function") else
-        stop("Argument 'null' should be a function, or the name of a function")
-  U <- F0(x, ...)
-  if(any(U < 0 | U > 1))
-    stop("null distribution function returned values outside [0,1]")
-  U <- sort(U)
-  k <- seq_len(n)
-  ## call Marsaglia C code
-  z <- .C("ADtestR",
-          x = as.double(U),
-          n = as.integer(n),
-          adstat = as.double(numeric(1)),
-          pvalue = as.double(numeric(1))
-          )
-  STATISTIC <- z$adstat
-  names(STATISTIC) <- "An"
-  PVAL <- z$pvalue
-  METHOD <- c("Anderson-Darling test of goodness-of-fit",
-              paste("Null hypothesis:", nullname))
-  extras <- list(...)
-  parnames <- intersect(names(extras), names(formals(F0)))
-  if(length(parnames) > 0) {
-    pars <- extras[parnames]
-    pard <- character(0)
-    for(i in seq_along(parnames))
-      pard[i] <- paste(parnames[i], "=", paste(pars[[i]], collapse=" "))
-    pard <- paste("with",
-                  ngettext(length(pard), "parameter", "parameters"),
-                  "  ", 
-                  paste(pard, collapse=", "))
-    METHOD <- c(METHOD, pard)
-  }
-  out <- list(statistic = STATISTIC,
-               p.value = PVAL,
-               method = METHOD,
-               data.name = xname)
-  class(out) <- "htest"
-  return(out)
-}
-
-pAD <- function(q, n=Inf, lower.tail=TRUE, fast=TRUE) {
-  q <- as.numeric(q)
-  p <- rep(NA_real_, length(q))
-  if(any(ones <- is.infinite(q) & (q == Inf)))
-    p[ones] <- 1
-  if(any(zeroes <- (is.finite(q) & q <= 0) | (is.infinite(q) & (q == -Inf))))
-    p[zeroes] <- 0
-  ok <- is.finite(q) & (q > 0)
-  nok <- sum(ok)
-  if(nok > 0) {
-    if(is.finite(n)) {
-      z <- .C("ADprobN",
-              a       = as.double(q[ok]),
-              na      = as.integer(nok),
-              nsample = as.integer(n),
-              prob    = as.double(numeric(nok))
-              )
-      p[ok] <- z$prob
-    } else if(fast) {
-      ## fast version adinf()
-      z <- .C("ADprobApproxInf",
-              a    = as.double(q[ok]),
-              na   = as.integer(nok),
-              prob = as.double(numeric(nok))
-              )
-      p[ok] <- z$prob
-    } else {
-      ## slow, accurate version ADinf()
-      z <- .C("ADprobExactInf",
-              a    = as.double(q[ok]),
-              na   = as.integer(nok),
-              prob = as.double(numeric(nok))
-              )
-      p[ok] <- z$prob
-    }
-      
-  }
-  if(!lower.tail)
-    p <- 1 - p
-  return(p)
-}
-
-qAD <- local({
-
-  f <- function(x, N, P, Fast) {
-    pAD(x, N, fast=Fast) - P
-  }
-    
-  qAD <- function(p, n=Inf, lower.tail=TRUE, fast=TRUE) {
-    ## quantiles of null distribution of Anderson-Darling test statistic
-    stopifnot(all(p >= 0))
-    stopifnot(all(p <= 1))
-    if(!lower.tail) p <- 1-p
-    ans <- rep(NA_real_, length(p))
-    for(i in which(p >= 0 & p < 1)) 
-      ans[i] <- uniroot(f, c(0, 1), N=n, P=p[i], Fast=fast, extendInt="up")$root
-    return(ans)
-  }
-
-  qAD
-})
-
-
-  
-
-@
-
-
-1.5
-log
-@recognises standard distributions
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.4 $ $Date: 2014/06/09 05:07:09 $
-d39 15
-a53 2
-  METHOD <- paste("Anderson-Darling test of", nullname)
-  ALTERN <- paste("Not the", nullname)
-a55 1
-               alternative = ALTERN,
-@
-
-
-1.4
-log
-@tweak
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.3 $ $Date: 2014/06/09 05:02:30 $
-d13 3
-a15 2
-  if(missing(nullname))
-    nullname <- if(identical(null, "punif")) "uniform distribution" else
-d17 1
-d50 1
-a50 1
-pAD <- function(q, n=Inf, lower.tail=TRUE) {
-d68 8
-d77 2
-a78 1
-      z <- .C("ADprobInf",
-d85 1
-d94 2
-a95 2
-  f <- function(x, N, P) {
-    pAD(x, N) - P
-d98 1
-a98 1
-  qAD <- function(p, n=Inf, lower.tail=TRUE) {
-d105 1
-a105 1
-      ans[i] <- uniroot(f, c(0, 1), N=n, P=p[i], extendInt="up")$root
-@
-
-
-1.3
-log
-@buglet fix
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.2 $ $Date: 2014/06/09 04:34:57 $
-d35 1
-d91 2
-a92 1
-    for(i in which(p > 0 & p < 1)) 
-@
-
-
-1.2
-log
-@tweaked
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.1 $ $Date: 2014/06/09 04:26:35 $
-d52 1
-a52 1
-  if(any(zeroes <- (is.finite(q) & q < 0) | (is.infinite(q) & (q == -Inf))))
-d54 1
-a54 1
-  ok <- is.finite(q) & (q >= 0)
-@
-
-
-1.1
-log
-@Initial revision
-@
-text
-@d6 1
-a6 1
-## $Revision$ $Date$
-d47 1
-a47 1
-pAnDarl <- function(q, n=Inf, lower.tail=TRUE) {
-d78 23
-@
diff --git a/R/oldRCS/cramer.R,v b/R/oldRCS/cramer.R,v
deleted file mode 100755
index 3a0eb16..0000000
--- a/R/oldRCS/cramer.R,v
+++ /dev/null
@@ -1,200 +0,0 @@
-head	1.2;
-access;
-symbols;
-locks
-	adrian:1.2; strict;
-comment	@# @;
-
-
-1.2
-date	2014.06.09.04.34.49;	author adrian;	state Exp;
-branches;
-next	1.1;
-
-1.1
-date	2014.06.08.10.20.20;	author adrian;	state Exp;
-branches;
-next	;
-
-
-desc
-@@
-
-
-1.2
-log
-@renamed
-@
-text
-@##
-## cramer.R
-##
-## Distribution of the Cramer-Von Mises test statistic
-##
-## $Revision: 1.1 $ $Date: 2014/06/08 10:20:20 $
-##
-## ..................................................................
-##
-##      From Matlab code written by Julian Faraway (faraway@@umich.edu)
-##	Translated to R by Adrian Baddeley
-##
-##	Reference: S. Csorgo and J.J. Faraway,
-##      The exact and asymptotic distributions of Cramer-von Mises statistics
-##	Journal of the Royal Statistical Society, Series B
-##      58 (1996) 221-234.
-##
-
-pCvM <- local({
-
-  ## all functions are vectorised
-  D2 <- function(x) {
-    z <- (x^2)/4
-    b <- besselK(x=z, nu=1/4) + besselK(x=z, nu=3/4)
-    b * sqrt((x^3)/(8*pi))
-  }
-
-  D3 <- function(x) {
-    z <- (x^2)/4
-    b <- 2*besselK(z, nu=1/4) + 3*besselK(z, nu=3/4) - besselK(z, nu=5/4)
-    b * sqrt((x^5)/(32 * pi))
-  }
-
-  ED2 <- function(x) { exp(-(x^2)/4) * D2(x) }
-
-  ED3 <- function(x) { exp(-(x^2)/4) * D3(x) }
-
-  Ak <- function(k, x) {
-    twosqrtx <- 2 * sqrt(x)
-    x34 <- x^(3/4)
-    x54 <- x^(5/4)
-    (2*k+1)*gamma(k+1/2)*ED2((4*k+3)/twosqrtx)/(9*x34) +
-      gamma(k+1/2)*ED3((4*k+1)/twosqrtx)/(72*x54) +
-        2*(2*k+3)*gamma(k+3/2)*ED3((4*k+5)/twosqrtx)/(12*x54) +
-          7*(2*k+1)*gamma(k+1/2)*ED2((4*k+1)/twosqrtx)/(144*x34) +
-            7*(2*k+1)*gamma(k+1/2)*ED2((4*k+5)/twosqrtx)/(144*x34)
-  }
-
-  psi1 <- function(x) {
-    ## Leading term in expansion of small-sample cdf of Cramer-Von Mises
-    m <- length(x)
-    tot <- numeric(m)
-    active <- rep(TRUE, m)
-    for(k in 0:20) {
-      z <- -Ak(k,x[active])/(pi*factorial(k))
-      tot[active] <- tot[active] + z
-      active[active] <- (abs(z) >= 1e-7)
-      if(!any(active)) break
-    }
-    return(tot + Vinf(x)/12)
-  }
-
-  Vinf <- function(x) {
-    ## cdf of asymptotic distribution of Cramer-von Mises
-    m <- length(x)
-    tot <- numeric(m)
-    active <- rep(TRUE, m)
-    for(k in 0:10) {
-      q <- (4*k+1)^2/(16*x[active])
-      z <- ((-1)^k)*choose(-1/2,k)*sqrt(4*k+1)*
-        exp(-q)*besselK(q, nu=1/4)/sqrt(x[active])
-      tot[active] <- tot[active] + z
-      active[active] <- (abs(z) >= 1e-7)
-      if(!any(active)) break
-    }
-    return(tot/pi)
-  }
-
-  Vn <- function(x, n) {
-    ## cdf of small-sample distribution of Cramer-von Mises statistic
-    ## First order approximation, Csorgo and Faraway equation (1.8)
-    Vinf(x) + psi1(x)/n
-  }
-    
-  pCvM <- function(q, n=Inf, lower.tail=TRUE) {
-    ## cdf of null distribution of Cramer-von Mises test statistic
-    if(is.finite(n)) {
-      lower <- 1/(12 * n)
-      upper <- n/3
-    } else {
-      lower <- 0
-      upper <- Inf
-    }
-    m <- length(q)
-    p <- numeric(m)
-    unknown <- rep(TRUE, m)
-    if(any(zeroes <- (q <= lower))) {
-      p[zeroes] <- 0
-      unknown[zeroes] <- FALSE
-    }
-    if(any(ones <- (q >= upper))) {
-      p[ones] <- 1
-      unknown[ones] <- FALSE
-    }
-    if(any(unknown))
-      p[unknown] <- if(is.infinite(n)) Vinf(q[unknown]) else Vn(q[unknown], n)
-    return(if(lower.tail) p else 1-p)
-  }
-
-  pCvM
-})
-
-qCvM <- local({
-
-  f <- function(x, N, P) {
-    pCvM(x, N) - P
-  }
-    
-  qCvM <- function(p, n=Inf, lower.tail=TRUE) {
-    ## quantiles of null distribution of Cramer-von Mises test statistic
-    stopifnot(all(p >= 0))
-    stopifnot(all(p <= 1))
-    if(!lower.tail) p <- 1-p
-    lower <- if(is.finite(n)) (1/(12 * n)) else 0
-    upper <- if(is.finite(n)) n/3 else Inf
-    ans <- numeric(length(p))
-    ans[p == 0] <- lower
-    ans[p == 1] <- upper
-    for(i in which(p > 0 & p < 1)) 
-      ans[i] <- uniroot(f, c(lower, 1), N=n, P=p[i], extendInt="up")$root
-    return(ans)
-  }
-
-  qCvM
-})
-
-
-  
-
-@
-
-
-1.1
-log
-@Initial revision
-@
-text
-@d6 1
-a6 1
-## $Revision$ $Date$
-d19 1
-a19 1
-pcramer <- local({
-d85 1
-a85 1
-  pcramer <- function(q, n=Inf, lower.tail=TRUE) {
-d110 1
-a110 1
-  pcramer
-d113 1
-a113 1
-qcramer <- local({
-d116 1
-a116 1
-    pcramer(x, N) - P
-d119 1
-a119 1
-  qcramer <- function(p, n=Inf, lower.tail=TRUE) {
-d134 1
-a134 1
-  qcramer
-@
diff --git a/R/oldRCS/cvmtest.R,v b/R/oldRCS/cvmtest.R,v
deleted file mode 100755
index 7f73b37..0000000
--- a/R/oldRCS/cvmtest.R,v
+++ /dev/null
@@ -1,147 +0,0 @@
-head	1.4;
-access;
-symbols;
-locks
-	adrian:1.4; strict;
-comment	@# @;
-
-
-1.4
-date	2014.06.24.02.13.27;	author adrian;	state Exp;
-branches;
-next	1.3;
-
-1.3
-date	2014.06.24.01.54.26;	author adrian;	state Exp;
-branches;
-next	1.2;
-
-1.2
-date	2014.06.08.11.32.51;	author adrian;	state Exp;
-branches;
-next	1.1;
-
-1.1
-date	2014.06.08.11.05.58;	author adrian;	state Exp;
-branches;
-next	;
-
-
-desc
-@@
-
-
-1.4
-log
-@polished output
-@
-text
-@##
-## cvmtest.R
-##
-## Cramer-von Mises test
-##
-## $Revision: 1.3 $ $Date: 2014/06/24 01:54:26 $
-##
-
-cvm.test <- function(x, null="punif", ..., nullname) {
-  xname <- deparse(substitute(x))
-  nulltext <- deparse(substitute(null))
-  if(is.character(null)) nulltext <- null
-  if(missing(nullname) || is.null(nullname)) {
-    reco <- recogniseCdf(nulltext)
-    nullname <- if(!is.null(reco)) reco else 
-                paste("distribution", sQuote(nulltext))
-  }
-  stopifnot(is.numeric(x))
-  x <- as.vector(x)
-  n <- length(x)
-  F0 <- if(is.function(null)) null else
-        if(is.character(null)) get(null, mode="function") else
-        stop("Argument 'null' should be a function, or the name of a function")
-  U <- F0(x, ...)
-  if(any(U < 0 | U > 1))
-    stop("null distribution function returned values outside [0,1]")
-  U <- sort(U)
-  k <- seq_len(n)
-  omega2 <- 1/(12 * n) + sum((U - (2*k - 1)/(2*n))^2)
-  PVAL <- pCvM(omega2, n=n, lower.tail=FALSE)
-  names(omega2) <- "omega2"
-  METHOD <- c("Cramer-von Mises test of goodness-of-fit",
-              paste("Null hypothesis:", nullname))
-  extras <- list(...)
-  parnames <- intersect(names(extras), names(formals(F0)))
-  if(length(parnames) > 0) {
-    pars <- extras[parnames]
-    pard <- character(0)
-    for(i in seq_along(parnames))
-      pard[i] <- paste(parnames[i], "=", paste(pars[[i]], collapse=" "))
-    pard <- paste("with",
-                  ngettext(length(pard), "parameter", "parameters"),
-                  "  ", 
-                  paste(pard, collapse=", "))
-    METHOD <- c(METHOD, pard)
-  }
-  out <- list(statistic = omega2,
-               p.value = PVAL,
-               method = METHOD,
-               data.name = xname)
-  class(out) <- "htest"
-  return(out)
-}
-
-@
-
-
-1.3
-log
-@recognises standard distributions
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.2 $ $Date: 2014/06/08 11:32:51 $
-d33 14
-a46 1
-              paste("to", nullname))
-@
-
-
-1.2
-log
-@minor
-@
-text
-@d6 1
-a6 1
-## $Revision: 1.1 $ $Date: 2014/06/08 11:05:58 $
-d13 3
-a15 2
-  if(missing(nullname))
-    nullname <- if(identical(null, "punif")) "uniform distribution" else
-d17 1
-d30 1
-a30 1
-  PVAL <- pcvm(omega2, n=n, lower.tail=FALSE)
-d32 2
-a33 2
-  METHOD <- paste("Cramer-von Mises test of", nullname)
-  ALTERN <- paste("Not the", nullname)
-a35 1
-               alternative = ALTERN,
-@
-
-
-1.1
-log
-@Initial revision
-@
-text
-@d6 1
-a6 1
-## $Revision$ $Date$
-d10 2
-a11 2
-  xname <- short.deparse(substitute(x))
-  nulltext <- short.deparse(substitute(null))
-@
diff --git a/R/oldRCS/recog.R,v b/R/oldRCS/recog.R,v
deleted file mode 100755
index dce9ea5..0000000
--- a/R/oldRCS/recog.R,v
+++ /dev/null
@@ -1,124 +0,0 @@
-head	1.4;
-access;
-symbols;
-locks
-	adrian:1.4; strict;
-comment	@# @;
-
-
-1.4
-date	2014.06.24.02.13.35;	author adrian;	state Exp;
-branches;
-next	1.3;
-
-1.3
-date	2014.06.24.01.55.53;	author adrian;	state Exp;
-branches;
-next	1.2;
-
-1.2
-date	2014.06.24.01.49.05;	author adrian;	state Exp;
-branches;
-next	1.1;
-
-1.1
-date	2014.06.24.01.48.14;	author adrian;	state Exp;
-branches;
-next	;
-
-
-desc
-@@
-
-
-1.4
-log
-@neatened
-@
-text
-@##  recog.R
-##
-## $Revision: 1.3 $ $Date: 2014/06/24 01:55:53 $
-##
-
-recogniseCdf <- function(s="punif") {
-  if(!is.character(s) || length(s) != 1) return(NULL)
-  if(nchar(s) <= 1 || substr(s,1,1) != "p") return(NULL)
-  root <- substr(s, 2, nchar(s))
-  a <- switch(root,
-              beta     = "beta",
-              binom    = "binomial",
-              birthday = "birthday coincidence",
-              cauchy   = "Cauchy",
-              chisq    = "chi-squared",
-              exp      = "exponential",
-              f        = "F",
-              gamma    = "Gamma",
-              geom     = "geometric",
-              hyper    = "hypergeometric",
-              lnorm    = "log-normal",
-              logis    = "logistic",
-              nbinom   = "negative binomial",
-              norm     = "Normal",
-              pois     = "Poisson",
-              t        = "Student's t",
-              tukey    = "Tukey (Studentized range)",
-              unif     = "uniform",
-              weibull  = "Weibull",
-              NULL)
-  if(!is.null(a))
-    return(paste(a, "distribution"))
-  b <- switch(root,
-              AD     = "Anderson-Darling",
-              CvM    = "Cramer-von Mises",
-              wilcox = "Wilcoxon Rank Sum",
-              NULL)
-  if(!is.null(b))
-    return(paste("null distribution of", b, "Test Statistic"))
-  return(NULL)
-}
-
-         
-@
-
-
-1.3
-log
-@minor
-@
-text
-@d3 1
-a3 1
-## $Revision: 1.2 $ $Date: 2014/06/24 01:49:05 $
-d6 1
-a6 1
-recogniseCdf <- function(s) {
-@
-
-
-1.2
-log
-@bug fix
-@
-text
-@d3 1
-a3 1
-## $Revision: 1.1 $ $Date: 2014/06/24 01:48:14 $
-d35 1
-a35 1
-              CvM    = "Cramer-von Mises"
-@
-
-
-1.1
-log
-@Initial revision
-@
-text
-@d3 1
-a3 1
-## $Revision$ $Date$
-d8 1
-a8 1
-  if(substr(s,1,1) != "p" || nchar(s) > 1) return(NULL)
-@
diff --git a/build/partial.rdb b/build/partial.rdb
deleted file mode 100644
index 34c1576..0000000
Binary files a/build/partial.rdb and /dev/null differ
diff --git a/debian/changelog b/debian/changelog
index 67785e2..1500194 100644
--- a/debian/changelog
+++ b/debian/changelog
@@ -1,8 +1,12 @@
-r-cran-goftest (1.2-3-2) UNRELEASED; urgency=medium
+r-cran-goftest (1.2-3+git20191127.1.ef942bf-1) UNRELEASED; urgency=medium
 
+  [ Andreas Tille ]
   * Disable reprotest
 
- -- Andreas Tille <tille@debian.org>  Wed, 13 Oct 2021 06:55:46 +0200
+  [ Debian Janitor ]
+  * New upstream snapshot.
+
+ -- Andreas Tille <tille@debian.org>  Fri, 20 Jan 2023 01:20:58 -0000
 
 r-cran-goftest (1.2-3-1) unstable; urgency=medium
 
diff --git a/man/RCS/ad.test.Rd,v b/man/RCS/ad.test.Rd,v
deleted file mode 100755
index 2978182..0000000
--- a/man/RCS/ad.test.Rd,v
+++ /dev/null
@@ -1,201 +0,0 @@
-head	1.5;
-access;
-symbols;
-locks
-	adrian:1.5; strict;
-comment	@# @;
-
-
-1.5
-date	2018.06.06.08.15.34;	author adrian;	state Exp;
-branches;
-next	1.4;
-
-1.4
-date	2018.06.06.08.05.03;	author adrian;	state Exp;
-branches;
-next	1.3;
-
-1.3
-date	2018.03.29.13.57.43;	author adrian;	state Exp;
-branches;
-next	1.2;
-
-1.2
-date	2014.06.09.08.37.42;	author adrian;	state Exp;
-branches;
-next	1.1;
-
-1.1
-date	2014.06.09.04.48.21;	author adrian;	state Exp;
-branches;
-next	;
-
-
-desc
-@@
-
-
-1.5
-log
-@Summary: minor
-@
-text
-@\name{ad.test}
-\alias{ad.test}
-\title{
-  Anderson-Darling Test of Goodness-of-Fit
-}
-\description{
-  Performs the Anderson-Darling test
-  of goodness-of-fit to a specified continuous univariate
-  probability distribution.
-}
-\usage{
-ad.test(x, null = "punif", ..., estimated=FALSE, nullname)
-}
-\arguments{
-  \item{x}{
-    Numeric vector of data values.
-  }
-  \item{null}{
-    A function, or a character string giving the name of a function,
-    to compute the cumulative distribution function for the
-    null distribution.
-  }
-  \item{\dots}{
-    Additional arguments for the cumulative distribution function.
-  }
-  \item{estimated}{
-    Logical value indicating whether the parameters of the distribution
-    were estimated using the data \code{x} (composite null hypothesis),
-    or were fixed in advance (simple null hypothesis, the default).
-  }
-  \item{nullname}{
-    Optional character string describing the null distribution.
-    The default is \code{"uniform distribution"}.
-  }
-}
-\details{
-  This command performs the Anderson-Darling test
-  of goodness-of-fit to the distribution specified by the argument
-  \code{null}. It is assumed that the values in \code{x} are
-  independent and identically distributed random values, with some
-  cumulative distribution function \eqn{F}.
-  The null hypothesis is that \eqn{F} is the function
-  specified by the argument \code{null}, while the alternative
-  hypothesis is that \eqn{F} is some other function.
-
-  By default, the test assumes that all the parameters of the null
-  distribution are known in advance (a \emph{simple} null hypothesis).
-  This test does not account for the effect of estimating the parameters.
-
-  If the parameters of the distribution were estimated (that is,
-  if they were calculated from the same data \code{x}),
-  then this should be indicated by setting the argument \code{estimated=TRUE}.
-  The test will then use the adjustment method of Braun (1980)
-  to allow for parameter estimation. Braun's method involves randomly
-  dividing the data into two equally-sized subsets, so the \eqn{p}-value
-  is not the same if the test is repeated.
-}
-\value{
-  An object of class \code{"htest"} representing the result of
-  the hypothesis test.
-}
-\references{
-  Anderson, T.W. and Darling, D.A. (1952)
-  Asymptotic theory of certain 'goodness-of-fit' criteria based
-  on stochastic processes.
-  \emph{Annals of Mathematical Statistics} \bold{23}, 193--212.
-
-  Anderson, T.W. and Darling, D.A. (1954)
-  A test of goodness of fit.
-  \emph{Journal of the American Statistical Association} \bold{49}, 765--769.
-
-  Braun, H. (1980)
-  A simple method for testing goodness-of-fit in the presence of
-  nuisance parameters.
-  \emph{Journal of the Royal Statistical Society} \bold{42}, 53--63.
-  
-  Marsaglia, G. and Marsaglia, J. (2004)
-  Evaluating the Anderson-Darling Distribution.
-  \emph{Journal of Statistical Software} \bold{9} (2), 1--5.
-  February 2004. 
-  \url{http://www.jstatsoft.org/v09/i02}
-}
-\author{
-  Original C code by George Marsaglia and John Marsaglia. 
-  \R interface by Adrian Baddeley.
-}
-\seealso{
-  \code{\link{pAD}} for the null distribution of the test statistic.
-}
-\examples{
-x <- rnorm(10, mean=2, sd=1)
-ad.test(x, "pnorm", mean=2, sd=1)
-ad.test(x, "pnorm", mean=mean(x), sd=sd(x), estimated=TRUE)
-}
-\keyword{htest}
-@
-
-
-1.4
-log
-@Summary: new argument
-@
-text
-@d54 3
-a56 1
-  to allow for parameter estimation.
-@
-
-
-1.3
-log
-@Summary: added message about simple null
-@
-text
-@d12 1
-a12 1
-ad.test(x, null = "punif", ..., nullname)
-d26 5
-d46 9
-a54 3
-  This version of the test assumes that all the parameters of the null
-  distribution are known (a \emph{simple} null hypothesis). It does not
-  account for the effect of estimating the parameters.
-d70 5
-d91 1
-@
-
-
-1.2
-log
-@minor
-@
-text
-@d40 4
-@
-
-
-1.1
-log
-@Initial revision
-@
-text
-@d47 1
-a47 1
-  Asymptotic theory of certain `goodness-of-fit’ criteria based
-d53 1
-a53 1
-  \emph{Journal of the American Statistical Association} \bold{49}, 765-–769.
-d62 1
-a62 1
-  C code by G. and J. Marsaglia. 
-d69 2
-a70 2
-x <- runif(10)
-ad.test(x)
-a72 1
-
-@
diff --git a/man/RCS/cvm.test.Rd,v b/man/RCS/cvm.test.Rd,v
deleted file mode 100755
index 1dc4e31..0000000
--- a/man/RCS/cvm.test.Rd,v
+++ /dev/null
@@ -1,199 +0,0 @@
-head	1.6;
-access;
-symbols;
-locks
-	adrian:1.6; strict;
-comment	@# @;
-
-
-1.6
-date	2018.06.06.08.15.38;	author adrian;	state Exp;
-branches;
-next	1.5;
-
-1.5
-date	2018.06.06.08.04.55;	author adrian;	state Exp;
-branches;
-next	1.4;
-
-1.4
-date	2018.03.29.13.57.31;	author adrian;	state Exp;
-branches;
-next	1.3;
-
-1.3
-date	2014.06.09.08.37.47;	author adrian;	state Exp;
-branches;
-next	1.2;
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-branches;
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-desc
-@@
-
-
-1.6
-log
-@Summary: minor
-@
-text
-@\name{cvm.test}
-\alias{cvm.test}
-\title{
-  Cramer-Von Mises Test of Goodness-of-Fit
-}
-\description{
-  Performs the
-  \ifelse{latex}{\out{Cram\'er}}{Cramer}-von Mises test
-  of goodness-of-fit to a specified continuous univariate
-  probability distribution.
-}
-\usage{
-cvm.test(x, null = "punif", ..., estimated=FALSE, nullname)
-}
-\arguments{
-  \item{x}{
-    Numeric vector of data values.
-  }
-  \item{null}{
-    A function, or a character string giving the name of a function,
-    to compute the cumulative distribution function for the
-    null distribution.
-  }
-  \item{\dots}{
-    Additional arguments for the cumulative distribution function.
-  }
-  \item{estimated}{
-    Logical value indicating whether the parameters of the distribution
-    were estimated using the data \code{x} (composite null hypothesis),
-    or were fixed in advance (simple null hypothesis, the default).
-  }
-  \item{nullname}{
-    Optional character string describing the null distribution.
-    The default is \code{"uniform distribution"}.
-  }
-}
-\details{
-  This command performs the
-  \ifelse{latex}{\out{Cram\'er}}{Cramer}-von Mises test
-  of goodness-of-fit to the distribution specified by the argument
-  \code{null}. It is assumed that the values in \code{x} are
-  independent and identically distributed random values, with some
-  cumulative distribution function \eqn{F}.
-  The null hypothesis is that \eqn{F} is the function
-  specified by the argument \code{null}, while the alternative
-  hypothesis is that \eqn{F} is some other function.
-
-  By default, the test assumes that all the parameters of the null
-  distribution are known in advance (a \emph{simple} null hypothesis).
-  This test does not account for the effect of estimating the parameters.
-
-  If the parameters of the distribution were estimated (that is,
-  if they were calculated from the same data \code{x}),
-  then this should be indicated by setting the argument \code{estimated=TRUE}.
-  The test will then use the adjustment method of Braun (1980)
-  to allow for parameter estimation. Braun's method involves randomly
-  dividing the data into two equally-sized subsets, so the \eqn{p}-value
-  is not the same if the test is repeated.
-}
-\value{
-  An object of class \code{"htest"} representing the result of
-  the hypothesis test.
-}
-\references{
-  Braun, H. (1980)
-  A simple method for testing goodness-of-fit in the presence of
-  nuisance parameters.
-  \emph{Journal of the Royal Statistical Society} \bold{42}, 53--63.
-
-  \ifelse{latex}{\out{Cs\"org\H{o}}}{Csorgo}, S. and Faraway, J.J. (1996)
-  The exact and asymptotic distributions of
-  \ifelse{latex}{\out{Cram\'er}}{Cramer}-von Mises statistics.
-  \emph{Journal of the Royal Statistical Society, Series B}
-  \bold{58}, 221--234.
-}
-\author{
-  Adrian Baddeley.
-}
-\seealso{
-  \code{\link{pCvM}} for the null distribution of the test statistic.
-}
-\examples{
-x <- rnorm(10, mean=2, sd=1)
-cvm.test(x, "pnorm", mean=2, sd=1)
-cvm.test(x, "pnorm", mean=mean(x), sd=sd(x), estimated=TRUE)
-}
-\keyword{htest}
-
-@
-
-
-1.5
-log
-@Summary: new argument
-@
-text
-@d56 3
-a58 1
-  to allow for parameter estimation.
-@
-
-
-1.4
-log
-@Summary: added message about simple null
-@
-text
-@d13 1
-a13 1
-cvm.test(x, null = "punif", ..., nullname)
-d27 5
-d48 9
-a56 3
-  This version of the test assumes that all the parameters of the null
-  distribution are known (a \emph{simple} null hypothesis). It does not
-  account for the effect of estimating the parameters.
-d63 5
-d83 1
-@
-
-
-1.3
-log
-@minor
-@
-text
-@d42 4
-@
-
-
-1.2
-log
-@name change
-@
-text
-@d61 2
-a62 2
-x <- runif(10)
-cvm.test(x)
-@
-
-
-1.1
-log
-@Initial revision
-@
-text
-@d58 1
-a58 1
-  \code{\link{pcramer}} for the null distribution of the test statistic.
-@
diff --git a/man/RCS/goftest-package.Rd,v b/man/RCS/goftest-package.Rd,v
deleted file mode 100755
index 4e69238..0000000
--- a/man/RCS/goftest-package.Rd,v
+++ /dev/null
@@ -1,205 +0,0 @@
-head	1.6;
-access;
-symbols;
-locks
-	adrian:1.6; strict;
-comment	@# @;
-
-
-1.6
-date	2018.06.06.08.23.51;	author adrian;	state Exp;
-branches;
-next	1.5;
-
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-@@
-
-
-1.6
-log
-@Summary: more stuff
-@
-text
-@\name{goftest-package}
-\alias{goftest-package}
-\alias{goftest}
-\docType{package}
-\title{
-  Classical Goodness-of-Fit Tests
-}
-\description{
-  \ifelse{latex}{\out{Cram\'er}}{Cramer}-von Mises
-  and Anderson-Darling tests of goodness-of-fit
-  for continuous univariate distributions, using modern
-  algorithms to compute the null distributions.
-}
-\details{
-  The \pkg{goftest} package contains implementations of the
-  classical \ifelse{latex}{\out{Cram\'er}}{Cramer}-von Mises
-  and Anderson-Darling tests of goodness-of-fit
-  for continuous univariate distributions.
-
-  The \ifelse{latex}{\out{Cram\'er}}{Cramer}-von Mises test
-  is performed by \code{\link{cvm.test}}. The cumulative distribution
-  function of the null distribution of the test statistic
-  is computed by \code{\link{pCvM}}
-  using the algorithm of \ifelse{latex}{\out{Cs\"org\H{o}}}{Csorgo}
-  and Faraway (1996). The quantiles are computed by \code{\link{qCvM}}
-  by root-finding.
-
-  The Anderson-Darling test is performed by 
-  \code{\link{ad.test}}. The cumulative distribution
-  function of the null distribution of the test statistic
-  is computed by \code{\link{pAD}}
-  using the algorithm of Marsaglia and Marsaglia (2004).
-  The quantiles are computed by \code{\link{qAD}} by root-finding.
-
-  By default, each test assumes that the parameters of the null
-  distribution are known (a \emph{simple} null hypothesis).
-  If the parameters were estimated (calculated from the data)
-  then the user should set \code{estimated=TRUE} which uses
-  the method of Braun (1980) to adjust for the effect of 
-  estimating the parameters from the data. 
-}
-\author{
-  Adrian Baddeley, Julian Faraway, John Marsaglia, George Marsaglia.
-
-  Maintainer: Adrian Baddeley <adrian.baddeley@@uwa.edu.au>
-}
-\references{
-  Braun, H. (1980)
-  A simple method for testing goodness-of-fit in the presence of
-  nuisance parameters.
-  \emph{Journal of the Royal Statistical Society} \bold{42}, 53--63.
-
-  \ifelse{latex}{\out{Cs\"org\H{o}}}{Csorgo}, S. and Faraway, J.J. (1996)
-  The exact and asymptotic distributions of
-  \ifelse{latex}{\out{Cram\'er}}{Cramer}-von Mises statistics.
-  \emph{Journal of the Royal Statistical Society, Series B}
-  \bold{58}, 221--234.
-
-  Marsaglia, G. and Marsaglia, J. (2004)
-  Evaluating the Anderson-Darling Distribution.
-  \emph{Journal of Statistical Software} \bold{9} (2), 1--5.
-  February 2004. 
-  \url{http://www.jstatsoft.org/v09/i02}
-}
-\keyword{package}
-\keyword{htest}
-\seealso{
-  \code{\link[stats]{ks.test}}
-}
-\examples{
-  x <- rnorm(30, mean=2, sd=1)
-  # default behaviour: parameters fixed: simple null hypothesis
-  cvm.test(x, "pnorm", mean=2, sd=1)
-  ad.test(x, "pnorm", mean=2, sd=1)
-  # parameters estimated: composite null hypothesis
-  mu <- mean(x)
-  sigma <- sd(x)
-  cvm.test(x, "pnorm", mean=mu, sd=sigma, estimated=TRUE)
-  ad.test(x, "pnorm", mean=mu, sd=sigma, estimated=TRUE)
-}
-@
-
-
-1.5
-log
-@Summary: more stuff
-@
-text
-@d37 3
-a39 1
-  However, a method is provided to adjust for the effect of 
-d48 5
-@
-
-
-1.4
-log
-@Summary: added message about simple null
-@
-text
-@d35 4
-a38 3
-  It is assumed that all the parameters of the null
-  distribution are known (a \emph{simple} null hypothesis). The calculation does not
-  account for the effect of estimating the parameters.
-d64 2
-a65 1
-  x <- rnorm(10, mean=2, sd=1)
-d68 5
-@
-
-
-1.3
-log
-@minor
-@
-text
-@d34 4
-@
-
-
-1.2
-log
-@prettified.
-@
-text
-@d22 2
-a23 1
-  function of the null distribution is computed by \code{\link{pCvM}}
-d30 2
-a31 1
-  function of the null distribution is computed by \code{\link{pAD}}
-d59 3
-a61 2
-  x <- rnorm(10)
-  cvm.test(x, "pnorm")
-@
-
-
-1.1
-log
-@Initial revision
-@
-text
-@d15 17
-a31 7
-  \tabular{ll}{
-    Package: \tab goftest\cr
-    Type: \tab Package\cr
-    Version: \tab 1.0-0\cr
-    Date: \tab 2014-06-08\cr
-    License: \tab GPL\cr
-  }
-d34 1
-a34 1
-  Adrian Baddeley, Julian Faraway, John Marsaglia, George Marsaglia
-@
diff --git a/man/RCS/pAD.Rd,v b/man/RCS/pAD.Rd,v
deleted file mode 100755
index c918db4..0000000
--- a/man/RCS/pAD.Rd,v
+++ /dev/null
@@ -1,149 +0,0 @@
-head	1.3;
-access;
-symbols;
-locks
-	adrian:1.3; strict;
-comment	@# @;
-
-
-1.3
-date	2014.06.09.08.37.37;	author adrian;	state Exp;
-branches;
-next	1.2;
-
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-date	2014.06.09.05.02.24;	author adrian;	state Exp;
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-1.3
-log
-@minor
-@
-text
-@\name{pAD}
-\alias{pAD}
-\alias{qAD}
-\title{
-  Null Distribution of Anderson-Darling Test Statistic
-}
-\description{
-  \code{pAD} computes the cumulative distribution function,
-  and \code{qAD} computes the quantile function,
-  of the null distribution of the Anderson-Darling test
-  statistic.
-}
-\usage{
-  pAD(q, n = Inf, lower.tail = TRUE, fast=TRUE)
-  qAD(p, n = Inf, lower.tail = TRUE, fast=TRUE)
-}
-\arguments{
-  \item{q}{
-    Numeric vector of quantiles (values for which the
-    cumulative probability is required).
-  }
-  \item{p}{
-    Numeric vector of probabilities.
-  }
-  \item{n}{
-    Integer. Sample size for the Anderson-Darling test.
-  }
-  \item{lower.tail}{
-    Logical. If \code{TRUE} (the default),
-    probabilities are \eqn{P(X \le q)}{P(X <= q)},
-    and otherwise they are \eqn{P(X > q)}.
-  }
-  \item{fast}{
-    Logical value indicating whether to use a fast algorithm
-    or a slower, more accurate algorithm, in the case \code{n=Inf}.
-  }
-}
-\details{
-  \code{pAD} uses the algorithms and C code described
-  in Marsaglia and Marsaglia (2004).
-
-  \code{qAD} uses \code{\link[stats]{uniroot}} to find the
-  quantiles.
-
-  The argument \code{fast} applies only when \code{n=Inf}
-  and determines whether the asymptotic distribution is approximated
-  using the faster algorithm \code{adinf} (accurate to 4-5 places)
-  or the slower algorithm \code{ADinf} (accurate to 11 places)
-  described in Marsaglia and Marsaglia (2004).
-}
-\value{
-  A numeric vector of the same length as \code{p} or \code{q}.
-}
-\references{
-  Anderson, T.W. and Darling, D.A. (1952)
-  Asymptotic theory of certain 'goodness-of-fit' criteria based
-  on stochastic processes.
-  \emph{Annals of Mathematical Statistics} \bold{23}, 193--212.
-
-  Anderson, T.W. and Darling, D.A. (1954)
-  A test of goodness of fit.
-  \emph{Journal of the American Statistical Association} \bold{49}, 765--769.
-
-  Marsaglia, G. and Marsaglia, J. (2004)
-  Evaluating the Anderson-Darling Distribution.
-  \emph{Journal of Statistical Software} \bold{9} (2), 1--5.
-  February 2004. 
-  \url{http://www.jstatsoft.org/v09/i02}
-}
-\author{
-  Original C code by G. and J. Marsaglia.
-  \R interface by Adrian Baddeley.
-}
-\seealso{
- \code{\link{ad.test}}
-}
-\examples{
-  pAD(1.1, n=5)
-  pAD(1.1)
-  pAD(1.1, fast=FALSE)
-
-  qAD(0.5, n=5)
-  qAD(0.5)
-}
-\keyword{distribution}
-\keyword{htest}
-@
-
-
-1.2
-log
-@minor
-@
-text
-@d14 2
-a15 2
-  pAD(q, n = Inf, lower.tail = TRUE)
-  qAD(p, n = Inf, lower.tail = TRUE)
-d33 4
-d44 6
-d78 1
-d80 3
-a82 1
-  pAD(1.1, n=5)
-a83 1
-  qAD(0.5, n=5)
-@
-
-
-1.1
-log
-@Initial revision
-@
-text
-@d45 9
-@
diff --git a/man/RCS/pCvM.Rd,v b/man/RCS/pCvM.Rd,v
deleted file mode 100755
index 55833ae..0000000
--- a/man/RCS/pCvM.Rd,v
+++ /dev/null
@@ -1,95 +0,0 @@
-head	1.1;
-access;
-symbols;
-locks
-	adrian:1.1; strict;
-comment	@# @;
-
-
-1.1
-date	2014.06.08.11.23.35;	author adrian;	state Exp;
-branches;
-next	;
-
-
-desc
-@@
-
-
-1.1
-log
-@Initial revision
-@
-text
-@\name{pcramer}
-\alias{pcramer}
-\alias{qcramer}
-\title{
-  Null Distribution of Cramer-von Mises Test Statistic
-}
-\description{
-  \code{pcramer} computes the cumulative distribution function,
-  and \code{qcramer} computes the quantile function,
-  of the null distribution of the
-  \ifelse{latex}{\out{Cram\'er}}{Cramer}-von Mises test
-  statistic.
-}
-\usage{
-  pcramer(q, n = Inf, lower.tail = TRUE)
-  qcramer(p, n = Inf, lower.tail = TRUE)
-}
-\arguments{
-  \item{q}{
-    Numeric vector of quantiles (values for which the
-    cumulative probability is required).
-  }
-  \item{p}{
-    Numeric vector of probabilities.
-  }
-  \item{n}{
-    Integer. Sample size for the
-    \ifelse{latex}{\out{Cram\'er}}{Cramer}-von Mises test.
-  }
-  \item{lower.tail}{
-    Logical. If \code{TRUE} (the default),
-    probabilities are \eqn{P(X \le q)}{P(X <= q)},
-    and otherwise they are \eqn{P(X > q)}.
-  }
-}
-\details{
-  For finite \code{n} the cumulative distribution function is
-  approximated by the first order expansion
-  \eqn{V(x) + \psi_1(x)/n}{V(x) + psi1(x)/n},
-  equation (1.8) of
-  \ifelse{latex}{\out{Cs\"org\"o}}{Csorgo} and Faraway (1996).
-
-  \code{qcramer} uses \code{\link[stats]{uniroot}} to find the
-  quantiles.
-}
-\value{
-  A numeric vector of the same length as \code{p} or \code{q}.
-}
-\references{
-  \ifelse{latex}{\out{Cs\"org\H{o}}}{Csorgo}, S. and Faraway, J.J. (1996)
-  The exact and asymptotic distributions of
-  \ifelse{latex}{\out{Cram\'er}}{Cramer}-von Mises statistics.
-  \emph{Journal of the Royal Statistical Society, Series B}
-  \bold{58}, 221--234.
-}
-\author{
-  Original Matlab code by Julian Faraway,
-  translated to \R by Adrian Baddeley.
-}
-\seealso{
- \code{\link{cvm.test}}
-}
-\examples{
-  pcramer(1.1)
-  pcramer(1.1, n=5)
-  qcramer(0.5)
-  qcramer(0.5, n=5)
-}
-\keyword{distribution}
-\keyword{htest}
-
-@
diff --git a/man/ad.test.Rd b/man/ad.test.Rd
index 66270f1..25d9149 100755
--- a/man/ad.test.Rd
+++ b/man/ad.test.Rd
@@ -82,7 +82,7 @@ ad.test(x, null = "punif", ..., estimated=FALSE, nullname)
   Evaluating the Anderson-Darling Distribution.
   \emph{Journal of Statistical Software} \bold{9} (2), 1--5.
   February 2004. 
-  \doi{10.18637/jss.v009.i02}
+  \url{http://www.jstatsoft.org/v09/i02}
 }
 \author{
   Original C code by George Marsaglia and John Marsaglia. 
diff --git a/man/goftest-package.Rd b/man/goftest-package.Rd
index 25a9f28..c44d3b2 100755
--- a/man/goftest-package.Rd
+++ b/man/goftest-package.Rd
@@ -60,7 +60,7 @@
   Evaluating the Anderson-Darling Distribution.
   \emph{Journal of Statistical Software} \bold{9} (2), 1--5.
   February 2004. 
-  \doi{10.18637/jss.v009.i02}
+  \url{http://www.jstatsoft.org/v09/i02}
 }
 \keyword{package}
 \keyword{htest}
diff --git a/man/pAD.Rd b/man/pAD.Rd
index bfba5d6..7c7b215 100755
--- a/man/pAD.Rd
+++ b/man/pAD.Rd
@@ -65,7 +65,7 @@
   Evaluating the Anderson-Darling Distribution.
   \emph{Journal of Statistical Software} \bold{9} (2), 1--5.
   February 2004. 
-  \doi{10.18637/jss.v009.i02}
+  \url{http://www.jstatsoft.org/v09/i02}
 }
 \author{
   Original C code by G. and J. Marsaglia.
diff --git a/src/RCS/ADinf.c,v b/src/RCS/ADinf.c,v
deleted file mode 100755
index 0ff82cf..0000000
--- a/src/RCS/ADinf.c,v
+++ /dev/null
@@ -1,137 +0,0 @@
-head	1.1;
-access;
-symbols;
-locks
-	adrian:1.1; strict;
-comment	@ * @;
-
-
-1.1
-date	2014.06.09.10.18.10;	author adrian;	state Exp;
-branches;
-next	;
-
-
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-
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-1.1
-log
-@Initial revision
-@
-text
-@/* 
-   ADinf.c
-
-   $Revision: 1.1 $  $Date: 2014/06/09 07:53:21 $
-
-   Original C code by G. and J. Marsaglia
-
-   R interface by Adrian Baddeley
-
-*/
-
-#include <Rmath.h>
-
-double  ADinf(double z);
-
-/*
-  A procedure for evaluating the limiting distribution of the
-  Anderson-Darling statistic 
-  A_n=-n-(1/n)[ln(x_1(1-x_n)+3ln(x_2(1-x_{n-1})+5ln(x_3(1-x_{n-2})+...
-       +(2n-1)ln(x_n(1-x_1))]
-  where x_1<x_2<...<x_n is an ordered set of purported uniform [0,1) variates.
-  The function is ADinf(z)=lim_{n->infty} Pr[A_n<z]. About 15 digit accuracy.
-  If you don't need that much accuracy, use the quick-and-easy adinf(z).
-  ADinf uses a two-term recursion for coefficients in series
-  for which initial values require the complementary normal integral, 
-  included as cPhi(z). Otherwise, use erfc() if your C compiler has one with
-  adequate accuracy.
-*/
-
-double cPhi(double z); /* prototype; listing follows main */
-
-double ADf(double z,int j){ 
-  /* called by ADinf(); see article. */
-  double t,f,fnew,a,b,c,r;
-  int i;
-  t=(4*j+1)*(4*j+1)*1.23370055013617/z;
-  if(t>150.) return 0.;
-  a=2.22144146907918*exp(-t)/sqrt(t);
-  b=3.93740248643060*2.*cPhi(sqrt(2*t));/* initialization requires cPhi */
-  /*if you have erfc(), replace 2*cPhi(sqrt(2*t)) with erfc(sqrt(t))*/
-  r=z*.125; f=a+b*r;
-  for(i=1;i<200;i++) {
-    c=((i-.5-t)*b+t*a)/i;
-    a=b; b=c; r*=z/(8*i+8);
-    if(fabs(r)<1e-40 || fabs(c)<1.e-40) return f;
-    fnew=f+c*r;
-    if(f==fnew) return f;
-    f=fnew;            
-  }
-  return f;				
-}
-
-double ADinf(double z){
-  int j;
-  double ad,adnew,r;
-  if(z<.01) return 0.; /* avoids exponent limits; ADinf(.01)=.528e-52 */
-  r=1./z;
-  ad=r*ADf(z,0);
-  for(j=1;j<100;j++){
-    r*=(.5-j)/j;
-    adnew=ad+(4*j+1)*r*ADf(z,j);
-    if(ad==adnew) {return ad;}
-    ad=adnew;         
-  }
-  return ad;
-}
-
-/*
-  Complementary normal distribution function
-  cPhi(x) = integral from x to infinity of phi(x)=exp(-.5*t^2)/sqrt(2*pi)
-  13-15 digit accuracy for abs(x)<16.
-  Stores R(0),R(2),R(4),...,R(16), with cPhi(x)=R(x)*phi(x), phi normal density,
-  then uses Taylor series for 
-  R(z+h)=R(z)+hR'(z)+(1/2)h^2R''(z)+...
-  with -1<h<1, and R(z) one of R(0),R(2),R(4),...,R(16) 
-  stored as v[0],v[1],...v[8].
-  Examples: cPhi(2.75) needs R(2.75) and 2.75=2+.75 so use h=.75 and R(2)=v[1],
-            cPhi(3.3)  needs R(3.3) and 3.3=4-.7, so use h=-.7 and R(4)=v[2].
-*/
-double cPhi(double x){
-  long double v[]={
-    1.25331413731550025,  .421369229288054473,  .236652382913560671,
-    .162377660896867462,  .123131963257932296,  .0990285964717319214,
-    .0827662865013691773, .0710695805388521071, .0622586659950261958
-  };
-  double h,a,b,z,t,s,pwr;
-  int i,j;
-  j=(fabs(x)+1.)/2.;
-  a=v[j];    z=2*j;  h=fabs(x)-z;
-  b=z*a-1;   pwr=1;  s=a+h*b;
-  for(i=2;i<100;i+=2){/* begin i loop */
-    a=(a+z*b)/i;
-    b=(b+z*a)/(i+1);
-    pwr=pwr*h*h;
-    t=s;
-    s=s+pwr*(a+h*b);
-    if(s==t){ 
-      s*=exp(-.5*x*x-.91893853320467274178);
-      return ((x>0) ? s: 1-s);
-    }
-  } /* end i loop */
-  /* If not converged, return last estimate */
-  return ((x>0) ? s: 1-s);
-}
-
-/* R interface */
-
-void ADprobExactInf(double *a, int *na, double *prob) {
-  int i, m;
-  m = *na;
-  for(i = 0; i < m; i++) 
-    prob[i] = ADinf(a[i]);
-}
-@
diff --git a/src/RCS/AnDarl.c,v b/src/RCS/AnDarl.c,v
deleted file mode 100755
index 7cbce6a..0000000
--- a/src/RCS/AnDarl.c,v
+++ /dev/null
@@ -1,212 +0,0 @@
-head	1.2;
-access;
-symbols;
-locks
-	adrian:1.2; strict;
-comment	@ * @;
-
-
-1.2
-date	2018.03.29.08.07.08;	author adrian;	state Exp;
-branches;
-next	1.1;
-
-1.1
-date	2014.06.09.07.53.21;	author adrian;	state Exp;
-branches;
-next	;
-
-
-desc
-@@
-
-
-1.2
-log
-@Summary: bug fix
-@
-text
-@/* 
-   AnDarl.c
-
-   $Revision: 1.1 $  $Date: 2014/06/09 07:53:21 $
-
-   Original C code by G. and J. Marsaglia
-
-   R interface by Adrian Baddeley
-
-*/
-
-#include <Rmath.h>
-
-/*
-    Anderson-Darling test for uniformity.   Given an ordered set
-              x_1<x_2<...<x_n
-    of purported uniform [0,1) variates,  compute
-          a = -n-(1/n)*[ln(x_1*z_1)+3*ln(x_2*z_2+...+(2*n-1)*ln(x_n*z_n)]
-    where 
-          z_1=1-x_n
-          z_2=1-x_(n-1)
-          ...
-          z_n=1-x_1, 
-    then find
-          v=adinf(a) 
-    and return 
-          p=v+errfix(v), 
-    which should be uniform in [0,1),
-    that is, the p-value associated with the observed x_1<x_2<...<x_n.
-
-*/
-
-  /*  prototypes */
-double adinf(double z);
-double errfix(int n,double x);
-double AD(int n,double z);
-
-/* Short, practical version of full ADinf(z), z>0.   */
-double adinf(double z) { 
-  if(z<2.) return (
-		   exp(-1.2337141/z)/sqrt(z)
-		   )*(
-		      2.00012+(.247105-	
-			       (.0649821-
-				(.0347962-
-				 (.011672-.00168691*z)
-				 *z)*z)*z)*z);
-  /* max |error| < .000002 for z<2, (p=.90816...) */
-  return exp(
-	     -exp(1.0776-(2.30695-(.43424-(.082433-(.008056 -.0003146*z)
-					   *z)*z)*z)*z));
-  /* max |error|<.0000008 for 4<z<infinity */
-}
-
-/*
-  The procedure  errfix(n,x)  corrects the error caused
-  by using the asymptotic approximation, x=adinf(z).
-  Thus x+errfix(n,x) is uniform in [0,1) for practical purposes;
-  accuracy may be off at the 5th, rarely at the 4th, digit.
-*/
-
-double errfix(int n, double x) {
-  double c,t;
-  if(x>.8) return (-130.2137+
-		   (745.2337-
-		    (1705.091-
-		     (1950.646-
-		      (1116.360-255.7844*x)*x)*x)*x)*x)/n;
-  c=.01265+.1757/n;
-  if(x<c){ 
-    t=x/c;
-    t=sqrt(t)*(1.-t)*(49*t-102);
-    return t*(.0037/(n*n)+.00078/n+.00006)/n;
-  }
-  t=(x-c)/(.8-c);
-  t=-.00022633+(6.54034-(14.6538-(14.458-(8.259-1.91864*t)*t)*t)*t)*t;
-  return (t*(.04213+.01365/n)/n);
-}
-
-/* 
-   The function AD(n,z) returns Prob(A_n<z) where
-   A_n = -n-(1/n)*[ln(x_1*z_1)+3*ln(x_2*z_2+...+(2*n-1)*ln(x_n*z_n)]
-   where
-   z_1=1-x_n, z_2=1-x_(n-1)...z_n=1-x_1, 
-   and
-   x_1<x_2<...<x_n is an ordered set of iid uniform [0,1) variates.
-*/
-
-double AD(int n,double z){
-  double c,v,x;
-  x=adinf(z);
-  /* now x=adinf(z). Next, get v=errfix(n,x) and return x+v; */
-  if(x>.8) {
-    v=(-130.2137+(745.2337-(1705.091-(1950.646-(1116.360-255.7844*x)
-				      *x)*x)*x)*x)/n;
-    return x+v;
-  }
-  c=.01265+.1757/n;
-  if(x<c){ 
-    v=x/c;
-    v=sqrt(v)*(1.-v)*(49*v-102);
-    return(x+v*(.0037/(n*n)+.00078/n+.00006)/n);
-  }
-  v=(x-c)/(.8-c);
-  v=-.00022633+(6.54034-(14.6538-(14.458-(8.259-1.91864*v)*v)*v)*v)*v;
-  return (x+v*(.04213+.01365/n)/n);
-}
-
-/* You must give the ADtest(int n, double *x) routine a sorted array
-   x[0]<=x[1]<=..<=x[n-1]
-   that you are testing for uniformity.
-   It will return the p-value associated
-   with the Anderson-Darling test, using
-   the above adinf() and errfix( ,   )
-   Not well-suited for n<7,
-   (accuracy could drop to 3 digits).
-*/
-
-double ADtest(int n, double *x)
-{ int i;
-  double t,z=0;
-  for(i=0;i<n;i++)   {
-    t=x[i]*(1.-x[n-1-i]);
-    z=z-(i+i+1)*log(t);
-  }
-  return AD(n,-n+z/n);
-}
-
-double ADstat(int n, double *x)
-{ int i;
-  double t,z=0;
-  for(i=0;i<n;i++)   {
-    t=x[i]*(1.-x[n-1-i]);
-    z=z-(i+i+1)*log(t);
-  }
-  return (-n+z/n);
-}
-
-/* R interface */
-
-void ADprobN(double *a, int *na, int *nsample, double *prob) {
-  int i, m, N;
-  m = *na;
-  N = *nsample;
-  for(i = 0; i < m; i++) 
-    prob[i] = AD(N, a[i]);
-}
-  
-void ADprobApproxInf(double *a, int *na, double *prob) {
-  int i, m;
-  m = *na;
-  for(i = 0; i < m; i++) 
-    prob[i] = adinf(a[i]);
-}
-  
-void ADtestR(double *x, int *n, double *adstat, double *pvalue) {
-  double a, p;
-  int N;
-  N = *n;
-  a = ADstat(N, x);
-  p = AD(N, a);
-  *adstat = a;
-  *pvalue = 1. - p;
-}
-
-
-@
-
-
-1.1
-log
-@Initial revision
-@
-text
-@d4 1
-a4 1
-   $Revision$  $Date$
-d149 1
-a149 1
-void ADprobInf(double *a, int *na, double *prob) {
-d157 2
-a158 1
-  double N, a, p;
-@

Debdiff

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Files in second set of .debs but not in first

-rw-r--r--  root/root   /usr/lib/debug/.build-id/da/b77340109f78f347faa7a1786c51364d41cf17.debug

Files in first set of .debs but not in second

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

  • Depends: r-base-core (>= 4.2.2-1), 4.2.0-1~jan+unchanged1), r-api-4.0, libc6 (>= 2.4)

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

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