CRAN Package Check Results for Package BioMoR

Last updated on 2025-12-04 07:50:52 CET.

Flavor	Version	Tinstall	Tcheck	Ttotal	Status	Flags
r-devel-linux-x86_64-debian-clang	0.1.0	14.40	396.92	411.32	OK
r-devel-linux-x86_64-debian-gcc	0.1.0	11.11	331.43	342.54	OK
r-devel-linux-x86_64-fedora-clang	0.1.0	85.00	427.18	512.18	ERROR
r-devel-linux-x86_64-fedora-gcc	0.1.0	108.00	1029.38	1137.38	ERROR
r-devel-windows-x86_64	0.1.0	16.00	214.00	230.00	OK
r-patched-linux-x86_64	0.1.0	13.71	467.72	481.43	OK
r-release-linux-x86_64	0.1.0	13.43	489.43	502.86	OK
r-release-macos-arm64	0.1.0				OK
r-release-macos-x86_64	0.1.0	15.00	190.00	205.00	OK
r-release-windows-x86_64	0.1.0	17.00	210.00	227.00	OK
r-oldrel-macos-arm64	0.1.0				OK
r-oldrel-macos-x86_64	0.1.0	16.00	204.00	220.00	OK
r-oldrel-windows-x86_64	0.1.0	21.00	282.00	303.00	OK

Check Details

Version: 0.1.0
Check: tests
Result: ERROR Running ‘testthat.R’ [270s/571s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(BioMoR) > > test_check("BioMoR") Loading required namespace: randomForest Loading required package: ggplot2 Loading required package: lattice Loading required package: dplyr Attaching package: 'dplyr' The following objects are masked from 'package:stats': filter, lag The following objects are masked from 'package:base': intersect, setdiff, setequal, union Attaching package: 'recipes' The following object is masked from 'package:stats': step randomForest 4.7-1.2 Type rfNews() to see new features/changes/bug fixes. Attaching package: 'randomForest' The following object is masked from 'package:dplyr': combine The following object is masked from 'package:ggplot2': margin Setting direction: controls > cases note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 . ------------------------------------------------------------------------------ You have loaded plyr after dplyr - this is likely to cause problems. If you need functions from both plyr and dplyr, please load plyr first, then dplyr: library(plyr); library(dplyr) ------------------------------------------------------------------------------ Attaching package: 'plyr' The following objects are masked from 'package:dplyr': arrange, count, desc, failwith, id, mutate, rename, summarise, summarize Saving _problems/test_models-35.R [ FAIL 1 | WARN 600 | SKIP 0 | PASS 5 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_models.R:35:3'): XGB model trains and predicts ───────────────── Error in `{ if (!(length(ctrl$seeds) == 1L && is.na(ctrl$seeds))) set.seed(ctrl$seeds[[iter]][parm]) loadNamespace("caret") loadNamespace("recipes") if (ctrl$verboseIter) progress(printed[parm, , drop = FALSE], names(resampleIndex), iter) if (names(resampleIndex)[iter] != "AllData") { modelIndex <- resampleIndex[[iter]] holdoutIndex <- ctrl$indexOut[[iter]] } else { modelIndex <- 1:nrow(dat) holdoutIndex <- modelIndex } if (testing) cat("pre-model\n") if (!is.null(info$submodels[[parm]]) && nrow(info$submodels[[parm]]) > 0) { submod <- info$submodels[[parm]] } else submod <- NULL mod_rec <- try(rec_model(rec, subset_x(dat, modelIndex), method = method, tuneValue = info$loop[parm, , drop = FALSE], obsLevels = lev, classProbs = ctrl$classProbs, sampling = ctrl$sampling, ...), silent = TRUE) if (testing) print(mod_rec) if (!model_failed(mod_rec)) { predicted <- try(rec_pred(method = method, object = mod_rec, newdata = subset_x(dat, holdoutIndex), param = submod), silent = TRUE) if (pred_failed(predicted)) { fail_warning(settings = printed[parm, , drop = FALSE], msg = predicted, where = "predictions", iter = names(resampleIndex)[iter], verb = ctrl$verboseIter) predicted <- fill_failed_pred(index = holdoutIndex, lev = lev, submod) } } else { fail_warning(settings = printed[parm, , drop = FALSE], msg = mod_rec, iter = names(resampleIndex)[iter], verb = ctrl$verboseIter) predicted <- fill_failed_pred(index = holdoutIndex, lev = lev, submod) } if (testing) print(head(predicted)) if (ctrl$classProbs) { if (!model_failed(mod_rec)) { probValues <- rec_prob(method = method, object = mod_rec, newdata = subset_x(dat, holdoutIndex), param = submod) } else { probValues <- fill_failed_prob(holdoutIndex, lev, submod) } if (testing) print(head(probValues)) } predicted <- trim_values(predicted, ctrl, is.null(lev)) ho_data <- holdout_rec(mod_rec, dat, holdoutIndex) if (!is.null(submod)) { allParam <- expandParameters(info$loop[parm, , drop = FALSE], submod) allParam <- allParam[complete.cases(allParam), , drop = FALSE] predicted <- lapply(predicted, function(x, lv, dat) { x <- outcome_conversion(x, lv = lev) dat$pred <- x dat }, lv = lev, dat = ho_data) if (testing) print(head(predicted)) if (ctrl$classProbs) predicted <- mapply(cbind, predicted, probValues, SIMPLIFY = FALSE) if (keep_pred) { tmpPred <- predicted for (modIndex in seq(along.with = tmpPred)) { tmpPred[[modIndex]] <- merge(tmpPred[[modIndex]], allParam[modIndex, , drop = FALSE], all = TRUE) } tmpPred <- rbind.fill(tmpPred) tmpPred$Resample <- names(resampleIndex)[iter] } else tmpPred <- NULL thisResample <- lapply(predicted, ctrl$summaryFunction, lev = lev, model = method) if (testing) print(head(thisResample)) if (length(lev) > 1 && length(lev) <= 50) { cells <- lapply(predicted, function(x) flatTable(x$pred, x$obs)) for (ind in seq(along.with = cells)) thisResample[[ind]] <- c(thisResample[[ind]], cells[[ind]]) } thisResample <- do.call("rbind", thisResample) thisResample <- cbind(allParam, thisResample) } else { pred_val <- outcome_conversion(predicted, lv = lev) tmp <- ho_data tmp$pred <- pred_val if (ctrl$classProbs) tmp <- cbind(tmp, probValues) if (keep_pred) { tmpPred <- tmp tmpPred$rowIndex <- holdoutIndex tmpPred <- merge(tmpPred, info$loop[parm, , drop = FALSE], all = TRUE) tmpPred$Resample <- names(resampleIndex)[iter] } else tmpPred <- NULL thisResample <- ctrl$summaryFunction(tmp, lev = lev, model = method) if (length(lev) > 1 && length(lev) <= 50) thisResample <- c(thisResample, flatTable(tmp$pred, tmp$obs)) thisResample <- as.data.frame(t(thisResample), stringsAsFactors = FALSE) thisResample <- cbind(thisResample, info$loop[parm, , drop = FALSE]) } thisResample$Resample <- names(resampleIndex)[iter] thisResampleExtra <- optimism_rec(ctrl, dat, iter, lev, method, mod_rec, predicted, submod, info$loop[parm, , drop = FALSE]) if (ctrl$verboseIter) progress(printed[parm, , drop = FALSE], names(resampleIndex), iter, FALSE) if (testing) print(thisResample) list(resamples = thisResample, pred = tmpPred, resamplesExtra = thisResampleExtra) }`: task 1 failed - "$ operator is invalid for atomic vectors" Backtrace: ▆ 1. └─BioMoR::train_xgb_caret(df, "Label", ctrl) at test_models.R:35:3 2. ├─caret::train(...) 3. └─caret:::train.recipe(...) 4. └─caret:::train_rec(...) 5. └─... %op% ... 6. └─e$fun(obj, substitute(ex), parent.frame(), e$data) [ FAIL 1 | WARN 600 | SKIP 0 | PASS 5 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-fedora-clang

Version: 0.1.0
Check: tests
Result: ERROR Running ‘testthat.R’ [15m/36m] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(BioMoR) > > test_check("BioMoR") Loading required namespace: randomForest Loading required package: ggplot2 Loading required package: lattice Loading required package: dplyr Attaching package: 'dplyr' The following objects are masked from 'package:stats': filter, lag The following objects are masked from 'package:base': intersect, setdiff, setequal, union Attaching package: 'recipes' The following object is masked from 'package:stats': step randomForest 4.7-1.2 Type rfNews() to see new features/changes/bug fixes. Attaching package: 'randomForest' The following object is masked from 'package:dplyr': combine The following object is masked from 'package:ggplot2': margin Setting direction: controls > cases note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 . ------------------------------------------------------------------------------ You have loaded plyr after dplyr - this is likely to cause problems. If you need functions from both plyr and dplyr, please load plyr first, then dplyr: library(plyr); library(dplyr) ------------------------------------------------------------------------------ Attaching package: 'plyr' The following objects are masked from 'package:dplyr': arrange, count, desc, failwith, id, mutate, rename, summarise, summarize Saving _problems/test_models-35.R [ FAIL 1 | WARN 600 | SKIP 0 | PASS 5 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_models.R:35:3'): XGB model trains and predicts ───────────────── Error in `{ if (!(length(ctrl$seeds) == 1L && is.na(ctrl$seeds))) set.seed(ctrl$seeds[[iter]][parm]) loadNamespace("caret") loadNamespace("recipes") if (ctrl$verboseIter) progress(printed[parm, , drop = FALSE], names(resampleIndex), iter) if (names(resampleIndex)[iter] != "AllData") { modelIndex <- resampleIndex[[iter]] holdoutIndex <- ctrl$indexOut[[iter]] } else { modelIndex <- 1:nrow(dat) holdoutIndex <- modelIndex } if (testing) cat("pre-model\n") if (!is.null(info$submodels[[parm]]) && nrow(info$submodels[[parm]]) > 0) { submod <- info$submodels[[parm]] } else submod <- NULL mod_rec <- try(rec_model(rec, subset_x(dat, modelIndex), method = method, tuneValue = info$loop[parm, , drop = FALSE], obsLevels = lev, classProbs = ctrl$classProbs, sampling = ctrl$sampling, ...), silent = TRUE) if (testing) print(mod_rec) if (!model_failed(mod_rec)) { predicted <- try(rec_pred(method = method, object = mod_rec, newdata = subset_x(dat, holdoutIndex), param = submod), silent = TRUE) if (pred_failed(predicted)) { fail_warning(settings = printed[parm, , drop = FALSE], msg = predicted, where = "predictions", iter = names(resampleIndex)[iter], verb = ctrl$verboseIter) predicted <- fill_failed_pred(index = holdoutIndex, lev = lev, submod) } } else { fail_warning(settings = printed[parm, , drop = FALSE], msg = mod_rec, iter = names(resampleIndex)[iter], verb = ctrl$verboseIter) predicted <- fill_failed_pred(index = holdoutIndex, lev = lev, submod) } if (testing) print(head(predicted)) if (ctrl$classProbs) { if (!model_failed(mod_rec)) { probValues <- rec_prob(method = method, object = mod_rec, newdata = subset_x(dat, holdoutIndex), param = submod) } else { probValues <- fill_failed_prob(holdoutIndex, lev, submod) } if (testing) print(head(probValues)) } predicted <- trim_values(predicted, ctrl, is.null(lev)) ho_data <- holdout_rec(mod_rec, dat, holdoutIndex) if (!is.null(submod)) { allParam <- expandParameters(info$loop[parm, , drop = FALSE], submod) allParam <- allParam[complete.cases(allParam), , drop = FALSE] predicted <- lapply(predicted, function(x, lv, dat) { x <- outcome_conversion(x, lv = lev) dat$pred <- x dat }, lv = lev, dat = ho_data) if (testing) print(head(predicted)) if (ctrl$classProbs) predicted <- mapply(cbind, predicted, probValues, SIMPLIFY = FALSE) if (keep_pred) { tmpPred <- predicted for (modIndex in seq(along.with = tmpPred)) { tmpPred[[modIndex]] <- merge(tmpPred[[modIndex]], allParam[modIndex, , drop = FALSE], all = TRUE) } tmpPred <- rbind.fill(tmpPred) tmpPred$Resample <- names(resampleIndex)[iter] } else tmpPred <- NULL thisResample <- lapply(predicted, ctrl$summaryFunction, lev = lev, model = method) if (testing) print(head(thisResample)) if (length(lev) > 1 && length(lev) <= 50) { cells <- lapply(predicted, function(x) flatTable(x$pred, x$obs)) for (ind in seq(along.with = cells)) thisResample[[ind]] <- c(thisResample[[ind]], cells[[ind]]) } thisResample <- do.call("rbind", thisResample) thisResample <- cbind(allParam, thisResample) } else { pred_val <- outcome_conversion(predicted, lv = lev) tmp <- ho_data tmp$pred <- pred_val if (ctrl$classProbs) tmp <- cbind(tmp, probValues) if (keep_pred) { tmpPred <- tmp tmpPred$rowIndex <- holdoutIndex tmpPred <- merge(tmpPred, info$loop[parm, , drop = FALSE], all = TRUE) tmpPred$Resample <- names(resampleIndex)[iter] } else tmpPred <- NULL thisResample <- ctrl$summaryFunction(tmp, lev = lev, model = method) if (length(lev) > 1 && length(lev) <= 50) thisResample <- c(thisResample, flatTable(tmp$pred, tmp$obs)) thisResample <- as.data.frame(t(thisResample), stringsAsFactors = FALSE) thisResample <- cbind(thisResample, info$loop[parm, , drop = FALSE]) } thisResample$Resample <- names(resampleIndex)[iter] thisResampleExtra <- optimism_rec(ctrl, dat, iter, lev, method, mod_rec, predicted, submod, info$loop[parm, , drop = FALSE]) if (ctrl$verboseIter) progress(printed[parm, , drop = FALSE], names(resampleIndex), iter, FALSE) if (testing) print(thisResample) list(resamples = thisResample, pred = tmpPred, resamplesExtra = thisResampleExtra) }`: task 1 failed - "$ operator is invalid for atomic vectors" Backtrace: ▆ 1. └─BioMoR::train_xgb_caret(df, "Label", ctrl) at test_models.R:35:3 2. ├─caret::train(...) 3. └─caret:::train.recipe(...) 4. └─caret:::train_rec(...) 5. └─... %op% ... 6. └─e$fun(obj, substitute(ex), parent.frame(), e$data) [ FAIL 1 | WARN 600 | SKIP 0 | PASS 5 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-fedora-gcc