CRAN Package Check Results for Package libcoin

Last updated on 2020-08-15 03:48:10 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.0-5 8.21 49.31 57.52 ERROR
r-devel-linux-x86_64-debian-gcc 1.0-5 5.85 37.72 43.57 ERROR
r-devel-linux-x86_64-fedora-clang 1.0-6 115.78 OK
r-devel-linux-x86_64-fedora-gcc 1.0-6 103.95 OK
r-devel-windows-ix86+x86_64 1.0-5 21.00 96.00 117.00 ERROR
r-patched-linux-x86_64 1.0-5 5.97 64.81 70.78 OK
r-patched-solaris-x86 1.0-6 135.30 OK
r-release-linux-x86_64 1.0-5 6.06 64.07 70.13 OK
r-release-macos-x86_64 1.0-5 OK
r-release-windows-ix86+x86_64 1.0-5 21.00 107.00 128.00 OK
r-oldrel-macos-x86_64 1.0-5 OK
r-oldrel-windows-ix86+x86_64 1.0-5 23.00 128.00 151.00 OK

Additional issues

clang-ASAN clang-UBSAN gcc-ASAN

Check Details

Version: 1.0-5
Check: whether package can be installed
Result: WARN
    Found the following significant warnings:
     libcoin.c:7655:22: warning: incompatible pointer to integer conversion passing 'int *' to parameter of type 'int'; remove & [-Wint-conversion]
     libcoin.c:7655:27: warning: incompatible pointer to integer conversion passing 'int *' to parameter of type 'int'; remove & [-Wint-conversion]
     libcoin.c:7656:42: warning: incompatible pointer to integer conversion passing 'int *' to parameter of type 'int'; dereference with * [-Wint-conversion]
Flavor: r-devel-linux-x86_64-debian-clang

Version: 1.0-5
Check: running R code from vignettes
Result: ERROR
    Errors in running code in vignettes:
    when running code in 'libcoin.Rnw'
     ...
    
    > isequal <- function(a, b) {
    + attributes(a) <- NULL
    + attributes(b) <- NULL
    + if (!isTRUE(all.equal(a, b))) {
    + print(a, digits .... [TRUNCATED]
    
    > library("libcoin")
    
    > set.seed(290875)
    
    > x <- gl(5, 20)
    
    > y <- round(runif(length(x)), 1)
    
    > ls1 <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(y,
    + ncol = 1))
    
    > ls1$LinearStatistic
    [1] 8.8 9.5 10.3 9.8 10.5
    
    > tapply(y, x, sum)
     1 2 3 4 5
     8.8 9.5 10.3 9.8 10.5
    
    > ls2 <- LinStatExpCov(X = x, Y = matrix(y, ncol = 1))
    
    > all.equal(ls1[-grep("Xfactor", names(ls1))], ls2[-grep("Xfactor",
    + names(ls2))])
    [1] TRUE
    
    > X <- rbind(0, diag(nlevels(x)))
    
    > ix <- unclass(x)
    
    > ylev <- sort(unique(y))
    
    > Y <- rbind(0, matrix(ylev, ncol = 1))
    
    > iy <- .bincode(y, breaks = c(-Inf, ylev, Inf))
    
    > ls3 <- LinStatExpCov(X = X, ix = ix, Y = Y, iy = iy)
    
    > all.equal(ls1[-grep("Table", names(ls1))], ls3[-grep("Table",
    + names(ls3))])
    [1] TRUE
    
    > ls3 <- LinStatExpCov(X = X, ix = factor(ix), Y = Y,
    + iy = factor(iy))
    
    > all.equal(ls1[-grep("Table", names(ls1))], ls3[-grep("Table",
    + names(ls3))])
    [1] TRUE
    
    > ls4 <- LinStatExpCov(ix = ix, Y = Y, iy = iy)
    
    > all.equal(ls3[-grep("Xfactor", names(ls3))], ls4[-grep("Xfactor",
    + names(ls4))])
    [1] TRUE
    
    > ls3$Table
    , , 1
    
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
    [1,] 0 0 0 0 0 0 0 0 0 0 0 0
    [2,] 0 0 4 4 1 2 3 0 1 2 3 0
    [3,] 0 2 2 1 2 2 5 0 1 1 3 1
    [4,] 0 1 1 4 0 1 5 2 0 2 3 1
    [5,] 0 0 2 2 4 2 2 1 3 2 1 1
    [6,] 0 1 3 1 1 1 2 2 2 6 1 0
    
    
    > xtabs(~ix + iy)
     iy
    ix 1 2 3 4 5 6 7 8 9 10 11
     1 0 4 4 1 2 3 0 1 2 3 0
     2 2 2 1 2 2 5 0 1 1 3 1
     3 1 1 4 0 1 5 2 0 2 3 1
     4 0 2 2 4 2 2 1 3 2 1 1
     5 1 3 1 1 1 2 2 2 6 1 0
    
    > ls1$Covariance
     [1] 1.3572364 -0.3393091 -0.3393091 -0.3393091 -0.3393091 1.3572364
     [7] -0.3393091 -0.3393091 -0.3393091 1.3572364 -0.3393091 -0.3393091
    [13] 1.3572364 -0.3393091 1.3572364
    
    > vcov(ls1)
     [,1] [,2] [,3] [,4] [,5]
    [1,] 1.3572364 -0.3393091 -0.3393091 -0.3393091 -0.3393091
    [2,] -0.3393091 1.3572364 -0.3393091 -0.3393091 -0.3393091
    [3,] -0.3393091 -0.3393091 1.3572364 -0.3393091 -0.3393091
    [4,] -0.3393091 -0.3393091 -0.3393091 1.3572364 -0.3393091
    [5,] -0.3393091 -0.3393091 -0.3393091 -0.3393091 1.3572364
    
    > doTest(ls1, teststat = "maximum", pvalue = FALSE)
    $TestStatistic
    [1] 0.8411982
    
    $p.value
    [1] NA
    
    
    > doTest(ls1, teststat = "maximum")
    $TestStatistic
    [1] 0.8411982
    
    $p.value
    [1] 0.8852087
    
    
    > doTest(ls1, teststat = "maximum", log = TRUE)
    $TestStatistic
    [1] 0.8411982
    
    $p.value
    [1] 0.108822
    
    
    > doTest(ls1, teststat = "maximum", lower = TRUE)
    $TestStatistic
    [1] 0.8411982
    
    $p.value
    [1] 0.1150168
    
    
    > doTest(ls1, teststat = "maximum", lower = TRUE, log = TRUE)
    $TestStatistic
    [1] 0.8411982
    
    $p.value
    [1] -2.164164
    
    
    > doTest(ls1, teststat = "quadratic")
    $TestStatistic
    [1] 1.077484
    
    $p.value
    [1] 0.897828
    
    
    > set.seed(29)
    
    > ls1d <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(y,
    + ncol = 1), nresample = 10, standardise = TRUE)
    
    > set.seed(29)
    
    > ls1s <- LinStatExpCov(X = as.double(1:5)[x], Y = matrix(y,
    + ncol = 1), nresample = 10, standardise = TRUE)
    
    > ls1c <- lmult(c(1:5), ls1d)
    
    > stopifnot(isequal(ls1c, ls1s))
    
    > set.seed(29)
    
    > ls1d <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(c(y,
    + y), ncol = 2), nresample = 10, standardise = TRUE)
    
    > set.seed(29)
    
    > ls1s <- LinStatExpCov(X = as.double(1:5)[x], Y = matrix(c(y,
    + y), ncol = 2), nresample = 10, standardise = TRUE)
    
    > ls1c <- lmult(c(1:5), ls1d)
    
    > stopifnot(isequal(ls1c, ls1s))
    
    > t1 <- ctabs(ix = ix, iy = iy)
    
    > t2 <- xtabs(~ix + iy)
    
    > max(abs(t1[-1, -1] - t2))
    [1] 0
    
    > N <- 20
    
    > P <- 3
    
    > Lx <- 10
    
    > Ly <- 5
    
    > Q <- 4
    
    > B <- 2
    
    > iX2d <- rbind(0, matrix(runif(Lx * P), nrow = Lx))
    
    > ix <- sample(1:Lx, size = N, replace = TRUE)
    
    > levels(ix) <- 1:Lx
    
    > ixf <- factor(ix, levels = 1:Lx, labels = 1:Lx)
    
    > x <- iX2d[ix + 1, ]
    
    > Xfactor <- diag(Lx)[ix, ]
    
    > iY2d <- rbind(0, matrix(runif(Ly * Q), nrow = Ly))
    
    > iy <- sample(1:Ly, size = N, replace = TRUE)
    
    > levels(iy) <- 1:Ly
    
    > iyf <- factor(iy, levels = 1:Ly, labels = 1:Ly)
    
    > y <- iY2d[iy + 1, ]
    
    > weights <- sample(0:5, size = N, replace = TRUE)
    
    > block <- sample(gl(B, ceiling(N/B))[1:N])
    
    > subset <- sort(sample(1:N, floor(N * 1.5), replace = TRUE))
    
    > subsety <- sample(1:N, floor(N * 1.5), replace = TRUE)
    
    > r1 <- rep(1:ncol(x), ncol(y))
    
    > r1Xfactor <- rep(1:ncol(Xfactor), ncol(y))
    
    > r2 <- rep(1:ncol(y), each = ncol(x))
    
    > r2Xfactor <- rep(1:ncol(y), each = ncol(Xfactor))
    
    > LECV <- function(X, Y, weights = integer(0), subset = integer(0),
    + block = integer(0)) {
    + if (length(weights) == 0)
    + weights <- .... [TRUNCATED]
    
    > cmpr <- function(ret1, ret2) {
    + if (inherits(ret1, "LinStatExpCov")) {
    + if (!ret1$varonly)
    + ret1$Covariance <- vcov(ret1 .... [TRUNCATED]
    
    > LECVxyws <- LinStatExpCov(x, y, weights = weights,
    + subset = subset)
    
    > LEVxyws <- LinStatExpCov(x, y, weights = weights,
    + subset = subset, varonly = TRUE)
    
    > testit <- function(...) {
    + a <- LinStatExpCov(x, y, ...)
    + b <- LECV(x, y, ...)
    + d <- LinStatExpCov(X = iX2d, ix = ix, Y = iY2d, iy = .... [TRUNCATED]
    
    > stopifnot(testit() && testit(weights = weights) &&
    + testit(subset = subset) && testit(weights = weights, subset = subset) &&
    + testit(blo .... [TRUNCATED]
    
    > testit <- function(...) {
    + a <- LinStatExpCov(X = ix, y, ...)
    + b <- LECV(Xfactor, y, ...)
    + d <- LinStatExpCov(X = integer(0), ix = ix .... [TRUNCATED]
    
    > stopifnot(testit() && testit(weights = weights) &&
    + testit(subset = subset) && testit(weights = weights, subset = subset) &&
    + testit(blo .... [TRUNCATED]
    
    > LinStatExpCov(X = iX2d, ix = ix, Y = iY2d, iy = iy,
    + weights = weights, subset = subset, nresample = 10)$PermutedLinearStatistic
    
     *** caught segfault ***
    address 0x5d66000, cause 'memory not mapped'
    Segmentation fault
    
    ... incomplete output. Crash?
    
     'libcoin.Rnw' using 'UTF-8'... failed to complete the test
Flavor: r-devel-linux-x86_64-debian-clang

Version: 1.0-5
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
     ...
    --- re-building 'libcoin.Rnw' using Sweave
    
     *** caught segfault ***
    address 0xfffffffe1499c1fc, cause 'memory not mapped'
    
    Traceback:
     1: .LinStatExpCov2d(X = X, Y = Y, ix = ix, iy = iy, weights = weights, subset = subset, block = block, varonly = varonly, checkNAs = checkNAs, nresample = nresample, standardise = standardise, tol = tol)
     2: LinStatExpCov(X = iX2d, ix = ix, Y = iY2d, iy = iy, weights = weights, subset = subset, nresample = 10)
     3: eval(expr, .GlobalEnv)
     4: eval(expr, .GlobalEnv)
     5: withVisible(eval(expr, .GlobalEnv))
     6: doTryCatch(return(expr), name, parentenv, handler)
     7: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     8: tryCatchList(expr, classes, parentenv, handlers)
     9: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call)[1L] prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
    10: try(withVisible(eval(expr, .GlobalEnv)), silent = TRUE)
    11: evalFunc(ce, options)
    12: tryCatchList(expr, classes, parentenv, handlers)
    13: tryCatch(evalFunc(ce, options), finally = { cat("\n") sink()})
    14: driver$runcode(drobj, chunk, chunkopts)
    15: utils::Sweave(...)
    16: engine$weave(file, quiet = quiet, encoding = enc)
    17: doTryCatch(return(expr), name, parentenv, handler)
    18: tryCatchOne(expr, names, parentenv, handlers[[1L]])
    19: tryCatchList(expr, classes, parentenv, handlers)
    20: tryCatch({ engine$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) } outputs <- c(outputs, output)}, error = function(e) { thisOK <<- FALSE fails <<- c(fails, file) message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
    21: tools:::buildVignettes(dir = "/home/hornik/tmp/R.check/r-devel-clang/Work/PKGS/libcoin.Rcheck/vign_test/libcoin", ser_elibs = "/tmp/RtmpNuJRng/file6ca5787e440.rds")
    An irrecoverable exception occurred. R is aborting now ...
    Segmentation fault
Flavor: r-devel-linux-x86_64-debian-clang

Version: 1.0-5
Check: whether package can be installed
Result: WARN
    Found the following significant warnings:
     libcoin.c:7655:22: warning: passing argument 1 of ‘S_rcont2’ makes integer from pointer without a cast [-Wint-conversion]
     libcoin.c:7655:27: warning: passing argument 2 of ‘S_rcont2’ makes integer from pointer without a cast [-Wint-conversion]
     libcoin.c:7656:53: warning: passing argument 5 of ‘S_rcont2’ makes integer from pointer without a cast [-Wint-conversion]
Flavors: r-devel-linux-x86_64-debian-gcc, r-devel-windows-ix86+x86_64

Version: 1.0-5
Check: running R code from vignettes
Result: ERROR
    Errors in running code in vignettes:
    when running code in ‘libcoin.Rnw’
     ...
    
    > isequal <- function(a, b) {
    + attributes(a) <- NULL
    + attributes(b) <- NULL
    + if (!isTRUE(all.equal(a, b))) {
    + print(a, digits .... [TRUNCATED]
    
    > library("libcoin")
    
    > set.seed(290875)
    
    > x <- gl(5, 20)
    
    > y <- round(runif(length(x)), 1)
    
    > ls1 <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(y,
    + ncol = 1))
    
    > ls1$LinearStatistic
    [1] 8.8 9.5 10.3 9.8 10.5
    
    > tapply(y, x, sum)
     1 2 3 4 5
     8.8 9.5 10.3 9.8 10.5
    
    > ls2 <- LinStatExpCov(X = x, Y = matrix(y, ncol = 1))
    
    > all.equal(ls1[-grep("Xfactor", names(ls1))], ls2[-grep("Xfactor",
    + names(ls2))])
    [1] TRUE
    
    > X <- rbind(0, diag(nlevels(x)))
    
    > ix <- unclass(x)
    
    > ylev <- sort(unique(y))
    
    > Y <- rbind(0, matrix(ylev, ncol = 1))
    
    > iy <- .bincode(y, breaks = c(-Inf, ylev, Inf))
    
    > ls3 <- LinStatExpCov(X = X, ix = ix, Y = Y, iy = iy)
    
    > all.equal(ls1[-grep("Table", names(ls1))], ls3[-grep("Table",
    + names(ls3))])
    [1] TRUE
    
    > ls3 <- LinStatExpCov(X = X, ix = factor(ix), Y = Y,
    + iy = factor(iy))
    
    > all.equal(ls1[-grep("Table", names(ls1))], ls3[-grep("Table",
    + names(ls3))])
    [1] TRUE
    
    > ls4 <- LinStatExpCov(ix = ix, Y = Y, iy = iy)
    
    > all.equal(ls3[-grep("Xfactor", names(ls3))], ls4[-grep("Xfactor",
    + names(ls4))])
    [1] TRUE
    
    > ls3$Table
    , , 1
    
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
    [1,] 0 0 0 0 0 0 0 0 0 0 0 0
    [2,] 0 0 4 4 1 2 3 0 1 2 3 0
    [3,] 0 2 2 1 2 2 5 0 1 1 3 1
    [4,] 0 1 1 4 0 1 5 2 0 2 3 1
    [5,] 0 0 2 2 4 2 2 1 3 2 1 1
    [6,] 0 1 3 1 1 1 2 2 2 6 1 0
    
    
    > xtabs(~ix + iy)
     iy
    ix 1 2 3 4 5 6 7 8 9 10 11
     1 0 4 4 1 2 3 0 1 2 3 0
     2 2 2 1 2 2 5 0 1 1 3 1
     3 1 1 4 0 1 5 2 0 2 3 1
     4 0 2 2 4 2 2 1 3 2 1 1
     5 1 3 1 1 1 2 2 2 6 1 0
    
    > ls1$Covariance
     [1] 1.3572364 -0.3393091 -0.3393091 -0.3393091 -0.3393091 1.3572364
     [7] -0.3393091 -0.3393091 -0.3393091 1.3572364 -0.3393091 -0.3393091
    [13] 1.3572364 -0.3393091 1.3572364
    
    > vcov(ls1)
     [,1] [,2] [,3] [,4] [,5]
    [1,] 1.3572364 -0.3393091 -0.3393091 -0.3393091 -0.3393091
    [2,] -0.3393091 1.3572364 -0.3393091 -0.3393091 -0.3393091
    [3,] -0.3393091 -0.3393091 1.3572364 -0.3393091 -0.3393091
    [4,] -0.3393091 -0.3393091 -0.3393091 1.3572364 -0.3393091
    [5,] -0.3393091 -0.3393091 -0.3393091 -0.3393091 1.3572364
    
    > doTest(ls1, teststat = "maximum", pvalue = FALSE)
    $TestStatistic
    [1] 0.8411982
    
    $p.value
    [1] NA
    
    
    > doTest(ls1, teststat = "maximum")
    $TestStatistic
    [1] 0.8411982
    
    $p.value
    [1] 0.8852087
    
    
    > doTest(ls1, teststat = "maximum", log = TRUE)
    $TestStatistic
    [1] 0.8411982
    
    $p.value
    [1] 0.108822
    
    
    > doTest(ls1, teststat = "maximum", lower = TRUE)
    $TestStatistic
    [1] 0.8411982
    
    $p.value
    [1] 0.1150168
    
    
    > doTest(ls1, teststat = "maximum", lower = TRUE, log = TRUE)
    $TestStatistic
    [1] 0.8411982
    
    $p.value
    [1] -2.164164
    
    
    > doTest(ls1, teststat = "quadratic")
    $TestStatistic
    [1] 1.077484
    
    $p.value
    [1] 0.897828
    
    
    > set.seed(29)
    
    > ls1d <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(y,
    + ncol = 1), nresample = 10, standardise = TRUE)
    
    > set.seed(29)
    
    > ls1s <- LinStatExpCov(X = as.double(1:5)[x], Y = matrix(y,
    + ncol = 1), nresample = 10, standardise = TRUE)
    
    > ls1c <- lmult(c(1:5), ls1d)
    
    > stopifnot(isequal(ls1c, ls1s))
    
    > set.seed(29)
    
    > ls1d <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(c(y,
    + y), ncol = 2), nresample = 10, standardise = TRUE)
    
    > set.seed(29)
    
    > ls1s <- LinStatExpCov(X = as.double(1:5)[x], Y = matrix(c(y,
    + y), ncol = 2), nresample = 10, standardise = TRUE)
    
    > ls1c <- lmult(c(1:5), ls1d)
    
    > stopifnot(isequal(ls1c, ls1s))
    
    > t1 <- ctabs(ix = ix, iy = iy)
    
    > t2 <- xtabs(~ix + iy)
    
    > max(abs(t1[-1, -1] - t2))
    [1] 0
    
    > N <- 20
    
    > P <- 3
    
    > Lx <- 10
    
    > Ly <- 5
    
    > Q <- 4
    
    > B <- 2
    
    > iX2d <- rbind(0, matrix(runif(Lx * P), nrow = Lx))
    
    > ix <- sample(1:Lx, size = N, replace = TRUE)
    
    > levels(ix) <- 1:Lx
    
    > ixf <- factor(ix, levels = 1:Lx, labels = 1:Lx)
    
    > x <- iX2d[ix + 1, ]
    
    > Xfactor <- diag(Lx)[ix, ]
    
    > iY2d <- rbind(0, matrix(runif(Ly * Q), nrow = Ly))
    
    > iy <- sample(1:Ly, size = N, replace = TRUE)
    
    > levels(iy) <- 1:Ly
    
    > iyf <- factor(iy, levels = 1:Ly, labels = 1:Ly)
    
    > y <- iY2d[iy + 1, ]
    
    > weights <- sample(0:5, size = N, replace = TRUE)
    
    > block <- sample(gl(B, ceiling(N/B))[1:N])
    
    > subset <- sort(sample(1:N, floor(N * 1.5), replace = TRUE))
    
    > subsety <- sample(1:N, floor(N * 1.5), replace = TRUE)
    
    > r1 <- rep(1:ncol(x), ncol(y))
    
    > r1Xfactor <- rep(1:ncol(Xfactor), ncol(y))
    
    > r2 <- rep(1:ncol(y), each = ncol(x))
    
    > r2Xfactor <- rep(1:ncol(y), each = ncol(Xfactor))
    
    > LECV <- function(X, Y, weights = integer(0), subset = integer(0),
    + block = integer(0)) {
    + if (length(weights) == 0)
    + weights <- .... [TRUNCATED]
    
    > cmpr <- function(ret1, ret2) {
    + if (inherits(ret1, "LinStatExpCov")) {
    + if (!ret1$varonly)
    + ret1$Covariance <- vcov(ret1 .... [TRUNCATED]
    
    > LECVxyws <- LinStatExpCov(x, y, weights = weights,
    + subset = subset)
    
    > LEVxyws <- LinStatExpCov(x, y, weights = weights,
    + subset = subset, varonly = TRUE)
    
    > testit <- function(...) {
    + a <- LinStatExpCov(x, y, ...)
    + b <- LECV(x, y, ...)
    + d <- LinStatExpCov(X = iX2d, ix = ix, Y = iY2d, iy = .... [TRUNCATED]
    
    > stopifnot(testit() && testit(weights = weights) &&
    + testit(subset = subset) && testit(weights = weights, subset = subset) &&
    + testit(blo .... [TRUNCATED]
    
    > testit <- function(...) {
    + a <- LinStatExpCov(X = ix, y, ...)
    + b <- LECV(Xfactor, y, ...)
    + d <- LinStatExpCov(X = integer(0), ix = ix .... [TRUNCATED]
    
    > stopifnot(testit() && testit(weights = weights) &&
    + testit(subset = subset) && testit(weights = weights, subset = subset) &&
    + testit(blo .... [TRUNCATED]
    
    > LinStatExpCov(X = iX2d, ix = ix, Y = iY2d, iy = iy,
    + weights = weights, subset = subset, nresample = 10)$PermutedLinearStatistic
    Segmentation fault
    
    ... incomplete output. Crash?
    
     ‘libcoin.Rnw’ using ‘UTF-8’... failed to complete the test
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 1.0-5
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
     ...
    --- re-building ‘libcoin.Rnw’ using Sweave
    Segmentation fault
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 1.0-5
Check: running R code from vignettes
Result: ERROR
    Errors in running code in vignettes:
    when running code in 'libcoin.Rnw'
    
    > isequal <- function(a, b) {
    + attributes(a) <- NULL
    + attributes(b) <- NULL
    + if (!isTRUE(all.equal(a, b))) {
    + print(a, digits .... [TRUNCATED]
    
    > library("libcoin")
    
    > set.seed(290875)
    
    > x <- gl(5, 20)
    
    > y <- round(runif(length(x)), 1)
    
    > ls1 <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(y,
    + ncol = 1))
    
    > ls1$LinearStatistic
    [1] 8.8 9.5 10.3 9.8 10.5
    
    > tapply(y, x, sum)
     1 2 3 4 5
     8.8 9.5 10.3 9.8 10.5
    
    > ls2 <- LinStatExpCov(X = x, Y = matrix(y, ncol = 1))
    
    > all.equal(ls1[-grep("Xfactor", names(ls1))], ls2[-grep("Xfactor",
    + names(ls2))])
    [1] TRUE
    
    > X <- rbind(0, diag(nlevels(x)))
    
    > ix <- unclass(x)
    
    > ylev <- sort(unique(y))
    
    > Y <- rbind(0, matrix(ylev, ncol = 1))
    
    > iy <- .bincode(y, breaks = c(-Inf, ylev, Inf))
    
    > ls3 <- LinStatExpCov(X = X, ix = ix, Y = Y, iy = iy)
    
    > all.equal(ls1[-grep("Table", names(ls1))], ls3[-grep("Table",
    + names(ls3))])
    [1] TRUE
    
    > ls3 <- LinStatExpCov(X = X, ix = factor(ix), Y = Y,
    + iy = factor(iy))
    
    > all.equal(ls1[-grep("Table", names(ls1))], ls3[-grep("Table",
    + names(ls3))])
    [1] TRUE
    
    > ls4 <- LinStatExpCov(ix = ix, Y = Y, iy = iy)
    
    > all.equal(ls3[-grep("Xfactor", names(ls3))], ls4[-grep("Xfactor",
    + names(ls4))])
    [1] TRUE
    
    > ls3$Table
    , , 1
    
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
    [1,] 0 0 0 0 0 0 0 0 0 0 0 0
    [2,] 0 0 4 4 1 2 3 0 1 2 3 0
    [3,] 0 2 2 1 2 2 5 0 1 1 3 1
    [4,] 0 1 1 4 0 1 5 2 0 2 3 1
    [5,] 0 0 2 2 4 2 2 1 3 2 1 1
    [6,] 0 1 3 1 1 1 2 2 2 6 1 0
    
    
    > xtabs(~ix + iy)
     iy
    ix 1 2 3 4 5 6 7 8 9 10 11
     1 0 4 4 1 2 3 0 1 2 3 0
     2 2 2 1 2 2 5 0 1 1 3 1
     3 1 1 4 0 1 5 2 0 2 3 1
     4 0 2 2 4 2 2 1 3 2 1 1
     5 1 3 1 1 1 2 2 2 6 1 0
    
    > ls1$Covariance
     [1] 1.3572364 -0.3393091 -0.3393091 -0.3393091 -0.3393091 1.3572364
     [7] -0.3393091 -0.3393091 -0.3393091 1.3572364 -0.3393091 -0.3393091
    [13] 1.3572364 -0.3393091 1.3572364
    
    > vcov(ls1)
     [,1] [,2] [,3] [,4] [,5]
    [1,] 1.3572364 -0.3393091 -0.3393091 -0.3393091 -0.3393091
    [2,] -0.3393091 1.3572364 -0.3393091 -0.3393091 -0.3393091
    [3,] -0.3393091 -0.3393091 1.3572364 -0.3393091 -0.3393091
    [4,] -0.3393091 -0.3393091 -0.3393091 1.3572364 -0.3393091
    [5,] -0.3393091 -0.3393091 -0.3393091 -0.3393091 1.3572364
    
    > doTest(ls1, teststat = "maximum", pvalue = FALSE)
    $TestStatistic
    [1] 0.8411982
    
    $p.value
    [1] NA
    
    
    > doTest(ls1, teststat = "maximum")
    $TestStatistic
    [1] 0.8411982
    
    $p.value
    [1] 0.8852087
    
    
    > doTest(ls1, teststat = "maximum", log = TRUE)
    $TestStatistic
    [1] 0.8411982
    
    $p.value
    [1] 0.108822
    
    
    > doTest(ls1, teststat = "maximum", lower = TRUE)
    $TestStatistic
    [1] 0.8411982
    
    $p.value
    [1] 0.1150168
    
    
    > doTest(ls1, teststat = "maximum", lower = TRUE, log = TRUE)
    $TestStatistic
    [1] 0.8411982
    
    $p.value
    [1] -2.164164
    
    
    > doTest(ls1, teststat = "quadratic")
    $TestStatistic
    [1] 1.077484
    
    $p.value
    [1] 0.897828
    
    
    > set.seed(29)
    
    > ls1d <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(y,
    + ncol = 1), nresample = 10, standardise = TRUE)
    
    > set.seed(29)
    
    > ls1s <- LinStatExpCov(X = as.double(1:5)[x], Y = matrix(y,
    + ncol = 1), nresample = 10, standardise = TRUE)
    
    > ls1c <- lmult(c(1:5), ls1d)
    
    > stopifnot(isequal(ls1c, ls1s))
    
    > set.seed(29)
    
    > ls1d <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(c(y,
    + y), ncol = 2), nresample = 10, standardise = TRUE)
    
    > set.seed(29)
    
    > ls1s <- LinStatExpCov(X = as.double(1:5)[x], Y = matrix(c(y,
    + y), ncol = 2), nresample = 10, standardise = TRUE)
    
    > ls1c <- lmult(c(1:5), ls1d)
    
    > stopifnot(isequal(ls1c, ls1s))
    
    > t1 <- ctabs(ix = ix, iy = iy)
    
    > t2 <- xtabs(~ix + iy)
    
    > max(abs(t1[-1, -1] - t2))
    [1] 0
    
    > N <- 20
    
    > P <- 3
    
    > Lx <- 10
    
    > Ly <- 5
    
    > Q <- 4
    
    > B <- 2
    
    > iX2d <- rbind(0, matrix(runif(Lx * P), nrow = Lx))
    
    > ix <- sample(1:Lx, size = N, replace = TRUE)
    
    > levels(ix) <- 1:Lx
    
    > ixf <- factor(ix, levels = 1:Lx, labels = 1:Lx)
    
    > x <- iX2d[ix + 1, ]
    
    > Xfactor <- diag(Lx)[ix, ]
    
    > iY2d <- rbind(0, matrix(runif(Ly * Q), nrow = Ly))
    
    > iy <- sample(1:Ly, size = N, replace = TRUE)
    
    > levels(iy) <- 1:Ly
    
    > iyf <- factor(iy, levels = 1:Ly, labels = 1:Ly)
    
    > y <- iY2d[iy + 1, ]
    
    > weights <- sample(0:5, size = N, replace = TRUE)
    
    > block <- sample(gl(B, ceiling(N/B))[1:N])
    
    > subset <- sort(sample(1:N, floor(N * 1.5), replace = TRUE))
    
    > subsety <- sample(1:N, floor(N * 1.5), replace = TRUE)
    
    > r1 <- rep(1:ncol(x), ncol(y))
    
    > r1Xfactor <- rep(1:ncol(Xfactor), ncol(y))
    
    > r2 <- rep(1:ncol(y), each = ncol(x))
    
    > r2Xfactor <- rep(1:ncol(y), each = ncol(Xfactor))
    
    > LECV <- function(X, Y, weights = integer(0), subset = integer(0),
    + block = integer(0)) {
    + if (length(weights) == 0)
    + weights <- .... [TRUNCATED]
    
    > cmpr <- function(ret1, ret2) {
    + if (inherits(ret1, "LinStatExpCov")) {
    + if (!ret1$varonly)
    + ret1$Covariance <- vcov(ret1 .... [TRUNCATED]
    
    > LECVxyws <- LinStatExpCov(x, y, weights = weights,
    + subset = subset)
    
    > LEVxyws <- LinStatExpCov(x, y, weights = weights,
    + subset = subset, varonly = TRUE)
    
    > testit <- function(...) {
    + a <- LinStatExpCov(x, y, ...)
    + b <- LECV(x, y, ...)
    + d <- LinStatExpCov(X = iX2d, ix = ix, Y = iY2d, iy = .... [TRUNCATED]
    
    > stopifnot(testit() && testit(weights = weights) &&
    + testit(subset = subset) && testit(weights = weights, subset = subset) &&
    + testit(blo .... [TRUNCATED]
    
    > testit <- function(...) {
    + a <- LinStatExpCov(X = ix, y, ...)
    + b <- LECV(Xfactor, y, ...)
    + d <- LinStatExpCov(X = integer(0), ix = ix .... [TRUNCATED]
    
    > stopifnot(testit() && testit(weights = weights) &&
    + testit(subset = subset) && testit(weights = weights, subset = subset) &&
    + testit(blo .... [TRUNCATED]
    
    > LinStatExpCov(X = iX2d, ix = ix, Y = iY2d, iy = iy,
    + weights = weights, subset = subset, nresample = 10)$PermutedLinearStatistic
    
    ... incomplete output. Crash?
    
     'libcoin.Rnw' using 'UTF-8'... failed to complete the test
Flavor: r-devel-windows-ix86+x86_64

Version: 1.0-5
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
    --- re-building 'libcoin.Rnw' using Sweave
Flavor: r-devel-windows-ix86+x86_64