CRAN Package Check Results for Package randomForest

Last updated on 2014-11-01 11:49:10.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 4.6-10 2.40 19.25 21.65 NOTE
r-devel-linux-x86_64-debian-gcc 4.6-10 2.96 18.30 21.26 NOTE
r-devel-linux-x86_64-fedora-clang 4.6-10 41.69 NOTE
r-devel-linux-x86_64-fedora-gcc 4.6-10 40.16 NOTE
r-devel-osx-x86_64-clang 4.6-10 35.77 NOTE
r-devel-windows-ix86+x86_64 4.6-10 13.00 39.00 52.00 NOTE
r-patched-linux-x86_64 4.6-10 2.97 18.60 21.57 NOTE
r-patched-solaris-sparc 4.6-10 242.40 NOTE
r-patched-solaris-x86 4.6-10 60.20 NOTE
r-release-linux-ix86 4.6-10 3.57 26.04 29.61 NOTE
r-release-linux-x86_64 4.6-10 2.93 19.06 21.99 NOTE
r-release-osx-x86_64-mavericks 4.6-10 NOTE
r-release-osx-x86_64-snowleopard 4.6-10 NOTE
r-release-windows-ix86+x86_64 4.6-10 12.00 41.00 53.00 NOTE
r-oldrel-windows-ix86+x86_64 4.6-10 14.00 44.00 58.00 OK

Check Details

Version: 4.6-10
Check: foreign function calls
Result: NOTE
    Calls with DUP:
     .C("regForest", as.double(x), ypred = double(ntest), as.integer(mdim),
     as.integer(ntest), as.integer(ntree), object$forest$leftDaughter,
     object$forest$rightDaughter, object$forest$nodestatus, nrnodes,
     object$forest$xbestsplit, object$forest$nodepred, object$forest$bestvar,
     object$forest$ndbigtree, object$forest$ncat, as.integer(maxcat),
     as.integer(predict.all), treepred = as.double(treepred),
     as.integer(proximity), proximity = as.double(proxmatrix),
     nodes = as.integer(nodes), nodexts = as.integer(nodexts),
     DUP = FALSE, PACKAGE = "randomForest")
     .C("classForest", mdim = as.integer(mdim), ntest = as.integer(ntest),
     nclass = as.integer(object$forest$nclass), maxcat = as.integer(maxcat),
     nrnodes = as.integer(nrnodes), jbt = as.integer(ntree), xts = as.double(x),
     xbestsplit = as.double(object$forest$xbestsplit), pid = object$forest$pid,
     cutoff = as.double(cutoff), countts = as.double(countts),
     treemap = as.integer(aperm(object$forest$treemap, c(2, 1,
     3))), nodestatus = as.integer(object$forest$nodestatus),
     cat = as.integer(object$forest$ncat), nodepred = as.integer(object$forest$nodepred),
     treepred = as.integer(treepred), jet = as.integer(numeric(ntest)),
     bestvar = as.integer(object$forest$bestvar), nodexts = as.integer(nodexts),
     ndbigtree = as.integer(object$forest$ndbigtree), predict.all = as.integer(predict.all),
     prox = as.integer(proximity), proxmatrix = as.double(proxmatrix),
     nodes = as.integer(nodes), DUP = FALSE, PACKAGE = "randomForest")
     .C("classRF", x = x, xdim = as.integer(c(p, n)), y = as.integer(y),
     nclass = as.integer(nclass), ncat = as.integer(ncat), maxcat = as.integer(maxcat),
     sampsize = as.integer(sampsize), strata = if (Stratify) as.integer(strata) else integer(1),
     Options = as.integer(c(addclass, importance, localImp, proximity,
     oob.prox, do.trace, keep.forest, replace, Stratify, keep.inbag)),
     ntree = as.integer(ntree), mtry = as.integer(mtry), ipi = as.integer(ipi),
     classwt = as.double(cwt), cutoff = as.double(threshold),
     nodesize = as.integer(nodesize), outcl = integer(nsample),
     counttr = integer(nclass * nsample), prox = prox, impout = impout,
     impSD = impSD, impmat = impmat, nrnodes = as.integer(nrnodes),
     ndbigtree = integer(ntree), nodestatus = integer(nt * nrnodes),
     bestvar = integer(nt * nrnodes), treemap = integer(nt * 2 *
     nrnodes), nodepred = integer(nt * nrnodes), xbestsplit = double(nt *
     nrnodes), errtr = double((nclass + 1) * ntree), testdat = as.integer(testdat),
     xts = as.double(xtest), clts = as.integer(ytest), nts = as.integer(ntest),
     countts = double(nclass * ntest), outclts = as.integer(numeric(ntest)),
     labelts = as.integer(labelts), proxts = proxts, errts = error.test,
     inbag = if (keep.inbag) matrix(integer(n * ntree), n) else integer(n),
     DUP = FALSE, PACKAGE = "randomForest")
     .C("regRF", x, as.double(y), as.integer(c(n, p)), as.integer(sampsize),
     as.integer(nodesize), as.integer(nrnodes), as.integer(ntree),
     as.integer(mtry), as.integer(c(importance, localImp, nPerm)),
     as.integer(ncat), as.integer(maxcat), as.integer(do.trace),
     as.integer(proximity), as.integer(oob.prox), as.integer(corr.bias),
     ypred = double(n), impout = impout, impmat = impmat, impSD = impSD,
     prox = prox, ndbigtree = integer(ntree), nodestatus = matrix(integer(nrnodes *
     nt), ncol = nt), leftDaughter = matrix(integer(nrnodes *
     nt), ncol = nt), rightDaughter = matrix(integer(nrnodes *
     nt), ncol = nt), nodepred = matrix(double(nrnodes * nt),
     ncol = nt), bestvar = matrix(integer(nrnodes * nt), ncol = nt),
     xbestsplit = matrix(double(nrnodes * nt), ncol = nt), mse = double(ntree),
     keep = as.integer(c(keep.forest, keep.inbag)), replace = as.integer(replace),
     testdat = as.integer(testdat), xts = xtest, ntest = as.integer(ntest),
     yts = as.double(ytest), labelts = as.integer(labelts), ytestpred = double(ntest),
     proxts = proxts, msets = double(if (labelts) ntree else 1),
     coef = double(2), oob.times = integer(n), inbag = if (keep.inbag) matrix(integer(n *
     ntree), n) else integer(1), DUP = FALSE, PACKAGE = "randomForest")
    DUP is no longer supported and will be ignored.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-osx-x86_64-clang, r-devel-windows-ix86+x86_64

Version: 4.6-10
Check: R code for possible problems
Result: NOTE
    MDSplot: no visible global function definition for ‘brewer.pal’
    plot.margin: no visible global function definition for ‘brewer.pal’
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-osx-x86_64-clang, r-devel-windows-ix86+x86_64, r-patched-linux-x86_64, r-patched-solaris-sparc, r-patched-solaris-x86

Version: 4.6-10
Check: foreign function calls
Result: NOTE
    Calls with DUP != TRUE:
     .C("regForest", as.double(x), ypred = double(ntest), as.integer(mdim),
     as.integer(ntest), as.integer(ntree), object$forest$leftDaughter,
     object$forest$rightDaughter, object$forest$nodestatus, nrnodes,
     object$forest$xbestsplit, object$forest$nodepred, object$forest$bestvar,
     object$forest$ndbigtree, object$forest$ncat, as.integer(maxcat),
     as.integer(predict.all), treepred = as.double(treepred),
     as.integer(proximity), proximity = as.double(proxmatrix),
     nodes = as.integer(nodes), nodexts = as.integer(nodexts),
     DUP = FALSE, PACKAGE = "randomForest")
     .C("classForest", mdim = as.integer(mdim), ntest = as.integer(ntest),
     nclass = as.integer(object$forest$nclass), maxcat = as.integer(maxcat),
     nrnodes = as.integer(nrnodes), jbt = as.integer(ntree), xts = as.double(x),
     xbestsplit = as.double(object$forest$xbestsplit), pid = object$forest$pid,
     cutoff = as.double(cutoff), countts = as.double(countts),
     treemap = as.integer(aperm(object$forest$treemap, c(2, 1,
     3))), nodestatus = as.integer(object$forest$nodestatus),
     cat = as.integer(object$forest$ncat), nodepred = as.integer(object$forest$nodepred),
     treepred = as.integer(treepred), jet = as.integer(numeric(ntest)),
     bestvar = as.integer(object$forest$bestvar), nodexts = as.integer(nodexts),
     ndbigtree = as.integer(object$forest$ndbigtree), predict.all = as.integer(predict.all),
     prox = as.integer(proximity), proxmatrix = as.double(proxmatrix),
     nodes = as.integer(nodes), DUP = FALSE, PACKAGE = "randomForest")
     .C("classRF", x = x, xdim = as.integer(c(p, n)), y = as.integer(y),
     nclass = as.integer(nclass), ncat = as.integer(ncat), maxcat = as.integer(maxcat),
     sampsize = as.integer(sampsize), strata = if (Stratify) as.integer(strata) else integer(1),
     Options = as.integer(c(addclass, importance, localImp, proximity,
     oob.prox, do.trace, keep.forest, replace, Stratify, keep.inbag)),
     ntree = as.integer(ntree), mtry = as.integer(mtry), ipi = as.integer(ipi),
     classwt = as.double(cwt), cutoff = as.double(threshold),
     nodesize = as.integer(nodesize), outcl = integer(nsample),
     counttr = integer(nclass * nsample), prox = prox, impout = impout,
     impSD = impSD, impmat = impmat, nrnodes = as.integer(nrnodes),
     ndbigtree = integer(ntree), nodestatus = integer(nt * nrnodes),
     bestvar = integer(nt * nrnodes), treemap = integer(nt * 2 *
     nrnodes), nodepred = integer(nt * nrnodes), xbestsplit = double(nt *
     nrnodes), errtr = double((nclass + 1) * ntree), testdat = as.integer(testdat),
     xts = as.double(xtest), clts = as.integer(ytest), nts = as.integer(ntest),
     countts = double(nclass * ntest), outclts = as.integer(numeric(ntest)),
     labelts = as.integer(labelts), proxts = proxts, errts = error.test,
     inbag = if (keep.inbag) matrix(integer(n * ntree), n) else integer(n),
     DUP = FALSE, PACKAGE = "randomForest")
     .C("regRF", x, as.double(y), as.integer(c(n, p)), as.integer(sampsize),
     as.integer(nodesize), as.integer(nrnodes), as.integer(ntree),
     as.integer(mtry), as.integer(c(importance, localImp, nPerm)),
     as.integer(ncat), as.integer(maxcat), as.integer(do.trace),
     as.integer(proximity), as.integer(oob.prox), as.integer(corr.bias),
     ypred = double(n), impout = impout, impmat = impmat, impSD = impSD,
     prox = prox, ndbigtree = integer(ntree), nodestatus = matrix(integer(nrnodes *
     nt), ncol = nt), leftDaughter = matrix(integer(nrnodes *
     nt), ncol = nt), rightDaughter = matrix(integer(nrnodes *
     nt), ncol = nt), nodepred = matrix(double(nrnodes * nt),
     ncol = nt), bestvar = matrix(integer(nrnodes * nt), ncol = nt),
     xbestsplit = matrix(double(nrnodes * nt), ncol = nt), mse = double(ntree),
     keep = as.integer(c(keep.forest, keep.inbag)), replace = as.integer(replace),
     testdat = as.integer(testdat), xts = xtest, ntest = as.integer(ntest),
     yts = as.double(ytest), labelts = as.integer(labelts), ytestpred = double(ntest),
     proxts = proxts, msets = double(if (labelts) ntree else 1),
     coef = double(2), oob.times = integer(n), inbag = if (keep.inbag) matrix(integer(n *
     ntree), n) else integer(1), DUP = FALSE, PACKAGE = "randomForest")
    DUP = FALSE is deprecated and will be disabled in future versions of R.
Flavors: r-patched-linux-x86_64, r-patched-solaris-sparc, r-patched-solaris-x86

Version: 4.6-10
Check: foreign function calls
Result: NOTE
    Calls with DUP = FALSE:
     .C("regForest", as.double(x), ypred = double(ntest), as.integer(mdim),
     as.integer(ntest), as.integer(ntree), object$forest$leftDaughter,
     object$forest$rightDaughter, object$forest$nodestatus, nrnodes,
     object$forest$xbestsplit, object$forest$nodepred, object$forest$bestvar,
     object$forest$ndbigtree, object$forest$ncat, as.integer(maxcat),
     as.integer(predict.all), treepred = as.double(treepred),
     as.integer(proximity), proximity = as.double(proxmatrix),
     nodes = as.integer(nodes), nodexts = as.integer(nodexts),
     DUP = FALSE, PACKAGE = "randomForest")
     .C("classForest", mdim = as.integer(mdim), ntest = as.integer(ntest),
     nclass = as.integer(object$forest$nclass), maxcat = as.integer(maxcat),
     nrnodes = as.integer(nrnodes), jbt = as.integer(ntree), xts = as.double(x),
     xbestsplit = as.double(object$forest$xbestsplit), pid = object$forest$pid,
     cutoff = as.double(cutoff), countts = as.double(countts),
     treemap = as.integer(aperm(object$forest$treemap, c(2, 1,
     3))), nodestatus = as.integer(object$forest$nodestatus),
     cat = as.integer(object$forest$ncat), nodepred = as.integer(object$forest$nodepred),
     treepred = as.integer(treepred), jet = as.integer(numeric(ntest)),
     bestvar = as.integer(object$forest$bestvar), nodexts = as.integer(nodexts),
     ndbigtree = as.integer(object$forest$ndbigtree), predict.all = as.integer(predict.all),
     prox = as.integer(proximity), proxmatrix = as.double(proxmatrix),
     nodes = as.integer(nodes), DUP = FALSE, PACKAGE = "randomForest")
     .C("classRF", x = x, xdim = as.integer(c(p, n)), y = as.integer(y),
     nclass = as.integer(nclass), ncat = as.integer(ncat), maxcat = as.integer(maxcat),
     sampsize = as.integer(sampsize), strata = if (Stratify) as.integer(strata) else integer(1),
     Options = as.integer(c(addclass, importance, localImp, proximity,
     oob.prox, do.trace, keep.forest, replace, Stratify, keep.inbag)),
     ntree = as.integer(ntree), mtry = as.integer(mtry), ipi = as.integer(ipi),
     classwt = as.double(cwt), cutoff = as.double(threshold),
     nodesize = as.integer(nodesize), outcl = integer(nsample),
     counttr = integer(nclass * nsample), prox = prox, impout = impout,
     impSD = impSD, impmat = impmat, nrnodes = as.integer(nrnodes),
     ndbigtree = integer(ntree), nodestatus = integer(nt * nrnodes),
     bestvar = integer(nt * nrnodes), treemap = integer(nt * 2 *
     nrnodes), nodepred = integer(nt * nrnodes), xbestsplit = double(nt *
     nrnodes), errtr = double((nclass + 1) * ntree), testdat = as.integer(testdat),
     xts = as.double(xtest), clts = as.integer(ytest), nts = as.integer(ntest),
     countts = double(nclass * ntest), outclts = as.integer(numeric(ntest)),
     labelts = as.integer(labelts), proxts = proxts, errts = error.test,
     inbag = if (keep.inbag) matrix(integer(n * ntree), n) else integer(n),
     DUP = FALSE, PACKAGE = "randomForest")
     .C("regRF", x, as.double(y), as.integer(c(n, p)), as.integer(sampsize),
     as.integer(nodesize), as.integer(nrnodes), as.integer(ntree),
     as.integer(mtry), as.integer(c(importance, localImp, nPerm)),
     as.integer(ncat), as.integer(maxcat), as.integer(do.trace),
     as.integer(proximity), as.integer(oob.prox), as.integer(corr.bias),
     ypred = double(n), impout = impout, impmat = impmat, impSD = impSD,
     prox = prox, ndbigtree = integer(ntree), nodestatus = matrix(integer(nrnodes *
     nt), ncol = nt), leftDaughter = matrix(integer(nrnodes *
     nt), ncol = nt), rightDaughter = matrix(integer(nrnodes *
     nt), ncol = nt), nodepred = matrix(double(nrnodes * nt),
     ncol = nt), bestvar = matrix(integer(nrnodes * nt), ncol = nt),
     xbestsplit = matrix(double(nrnodes * nt), ncol = nt), mse = double(ntree),
     keep = as.integer(c(keep.forest, keep.inbag)), replace = as.integer(replace),
     testdat = as.integer(testdat), xts = xtest, ntest = as.integer(ntest),
     yts = as.double(ytest), labelts = as.integer(labelts), ytestpred = double(ntest),
     proxts = proxts, msets = double(if (labelts) ntree else 1),
     coef = double(2), oob.times = integer(n), inbag = if (keep.inbag) matrix(integer(n *
     ntree), n) else integer(1), DUP = FALSE, PACKAGE = "randomForest")
    DUP = FALSE is deprecated and may be disabled in future versions of R.
Flavors: r-release-linux-ix86, r-release-linux-x86_64, r-release-osx-x86_64-mavericks, r-release-osx-x86_64-snowleopard, r-release-windows-ix86+x86_64