CRAN Package Check Results for Package RRF

Last updated on 2015-08-28 06:47:33.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.6 2.33 15.63 17.96 NOTE
r-devel-linux-x86_64-debian-gcc 1.6 2.84 15.26 18.10 NOTE
r-devel-linux-x86_64-fedora-clang 1.6 32.90 NOTE
r-devel-linux-x86_64-fedora-gcc 1.6 31.67 NOTE
r-devel-osx-x86_64-clang 1.6 29.76 NOTE
r-devel-windows-ix86+x86_64 1.6 23.00 39.00 62.00 NOTE
r-patched-linux-x86_64 1.6 2.89 16.32 19.21 NOTE
r-patched-solaris-sparc 1.6 234.80 NOTE
r-patched-solaris-x86 1.6 52.70 NOTE
r-release-linux-x86_64 1.6 2.95 16.32 19.28 NOTE
r-release-osx-x86_64-mavericks 1.6 NOTE
r-release-windows-ix86+x86_64 1.6 16.00 55.00 71.00 NOTE
r-oldrel-windows-ix86+x86_64 1.6 16.00 58.00 74.00 NOTE

Check Details

Version: 1.6
Check: dependencies in R code
Result: NOTE
    'library' or 'require' call to ‘RColorBrewer’ in package code.
     Please use :: or requireNamespace() instead.
     See section 'Suggested packages' in the 'Writing R Extensions' manual.
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, r-release-linux-x86_64, r-release-osx-x86_64-mavericks, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64

Version: 1.6
Check: foreign function calls
Result: NOTE
    Calls with DUP:
     .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),
     coefReg = as.double(coefReg), flagReg = as.integer(flagReg),
     varUsedAll = as.integer(varUsedAll), DUP = FALSE, PACKAGE = "RRF")
     .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 = "RRF")
     .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 = "RRF")
     .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 = "RRF")
    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, r-patched-linux-x86_64, r-patched-solaris-sparc, r-patched-solaris-x86, r-release-linux-x86_64, r-release-osx-x86_64-mavericks, r-release-windows-ix86+x86_64

Version: 1.6
Check: R code for possible problems
Result: NOTE
    MDSplot: no visible global function definition for ‘par’
    MDSplot: no visible global function definition for ‘rainbow’
    MDSplot: no visible global function definition for ‘plot’
    MDSplot: no visible global function definition for ‘pairs’
    RRF.default: no visible global function definition for ‘var’
    RRF.formula: no visible binding for global variable ‘na.fail’
    RRF.formula: no visible global function definition for ‘model.response’
    RRF.formula: no visible global function definition for ‘model.frame’
    RRF.formula: no visible global function definition for ‘terms’
    RRF.formula: no visible global function definition for ‘reformulate’
    classCenter : <anonymous>: no visible binding for global variable
     ‘median’
    grow.RRF: no visible global function definition for ‘update’
    na.roughfix.data.frame : roughfix: no visible global function
     definition for ‘median’
    na.roughfix.default: no visible global function definition for ‘median’
    outlier.default: no visible global function definition for ‘median’
    outlier.default: no visible global function definition for ‘mad’
    partialPlot.RRF: no visible global function definition for ‘predict’
    partialPlot.RRF: no visible global function definition for
     ‘weighted.mean’
    partialPlot.RRF: no visible global function definition for ‘points’
    partialPlot.RRF: no visible global function definition for ‘barplot’
    partialPlot.RRF: no visible global function definition for ‘lines’
    partialPlot.RRF: no visible global function definition for ‘quantile’
    plot.RRF: no visible global function definition for ‘matplot’
    plot.margin: no visible global function definition for ‘rainbow’
    plot.margin: no visible global function definition for ‘plot.default’
    predict.RRF: no visible global function definition for
     ‘delete.response’
    predict.RRF: no visible global function definition for ‘model.frame’
    predict.RRF: no visible binding for global variable ‘na.omit’
    rrfImpute.formula: no visible global function definition for
     ‘model.response’
    rrfcv: no visible global function definition for ‘quantile’
    tuneRRF: no visible global function definition for ‘axis’
    varImpPlot: no visible global function definition for ‘par’
    varImpPlot: no visible global function definition for ‘dotchart’
    varImpPlot: no visible global function definition for ‘mtext’
    Undefined global functions or variables:
     axis barplot delete.response dotchart lines mad matplot median
     model.frame model.response mtext na.fail na.omit pairs par plot
     plot.default points predict quantile rainbow reformulate terms update
     var weighted.mean
    Consider adding
     importFrom("grDevices", "rainbow")
     importFrom("graphics", "axis", "barplot", "dotchart", "lines",
     "matplot", "mtext", "pairs", "par", "plot", "plot.default",
     "points")
     importFrom("stats", "delete.response", "mad", "median", "model.frame",
     "model.response", "na.fail", "na.omit", "predict",
     "quantile", "reformulate", "terms", "update", "var",
     "weighted.mean")
    to your NAMESPACE.
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-windows-ix86+x86_64

Version: 1.6
Check: foreign function calls
Result: NOTE
    Calls with DUP != TRUE:
     .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),
     coefReg = as.double(coefReg), flagReg = as.integer(flagReg),
     varUsedAll = as.integer(varUsedAll), DUP = FALSE, PACKAGE = "RRF")
     .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 = "RRF")
     .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 = "RRF")
     .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 = "RRF")
    DUP = FALSE is deprecated and will be disabled in future versions of R.
Flavor: r-oldrel-windows-ix86+x86_64