CRAN Package Check Results for Package FastRet

Last updated on 2025-12-06 05:48:42 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.1.4 16.46 179.88 196.34 OK
r-devel-linux-x86_64-debian-gcc 1.1.4 12.68 130.68 143.36 ERROR
r-devel-linux-x86_64-fedora-clang 1.1.4 81.00 251.45 332.45 ERROR
r-devel-linux-x86_64-fedora-gcc 1.1.4 134.00 195.94 329.94 ERROR
r-devel-windows-x86_64 1.1.4 18.00 151.00 169.00 OK
r-patched-linux-x86_64 1.1.4 14.02 173.22 187.24 OK
r-release-linux-x86_64 1.1.4 16.46 172.66 189.12 OK
r-release-macos-arm64 1.1.4 OK
r-release-macos-x86_64 1.1.4 16.00 149.00 165.00 OK
r-release-windows-x86_64 1.1.4 18.00 156.00 174.00 OK
r-oldrel-macos-arm64 1.1.4 OK
r-oldrel-macos-x86_64 1.1.4 14.00 148.00 162.00 OK
r-oldrel-windows-x86_64 1.1.4 23.00 205.00 228.00 ERROR

Check Details

Version: 1.1.4
Check: tests
Result: ERROR Running ‘testthat.R’ [28s/18s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(FastRet) > > test_check("FastRet") Starting 2 test processes. Saving _problems/test-train_frm-gbtree-11.R Saving _problems/test-fit_gbtree-8.R Saving _problems/test-fit_gbtree-16.R > test-plot_frm.R: <1b>[1;30m2025-12-05 19:13:05.87<1b>[0m Starting training of a lasso model > test-plot_frm.R: <1b>[1;30m2025-12-05 19:13:05.87<1b>[0m Mocking is enabled. Returning 'mockdata/lasso_model.rds' > test-plot_frm.R: <1b>[1;30m2025-12-05 19:13:05.99<1b>[0m Starting training of a lasso model > test-plot_frm.R: <1b>[1;30m2025-12-05 19:13:05.99<1b>[0m Mocking is enabled. Returning 'mockdata/lasso_model.rds' > test-plot_frm.R: <1b>[1;30m2025-12-05 19:13:06.00<1b>[0m Starting model Adjustment > test-plot_frm.R: <1b>[1;30m2025-12-05 19:13:06.00<1b>[0m dim(original_data): 442 x 126 > test-plot_frm.R: <1b>[1;30m2025-12-05 19:13:06.00<1b>[0m dim(new_data): 25 x 3 > test-plot_frm.R: <1b>[1;30m2025-12-05 19:13:06.06<1b>[0m predictors: 1, 2, 3, 4, 5, 6 > test-plot_frm.R: <1b>[1;30m2025-12-05 19:13:06.06<1b>[0m nfolds: 5 > test-plot_frm.R: <1b>[1;30m2025-12-05 19:13:06.06<1b>[0m Preprocessing data > test-plot_frm.R: <1b>[1;30m2025-12-05 19:13:06.07<1b>[0m Formula: RT_ADJ ~ RT + I(RT^2) + I(RT^3) + log(RT) + exp(RT) + sqrt(RT) > test-plot_frm.R: <1b>[1;30m2025-12-05 19:13:06.07<1b>[0m Estimating performance of adjusted model in CV > test-plot_frm.R: <1b>[1;30m2025-12-05 19:13:06.11<1b>[0m Fitting adjustment model on full new data set > test-plot_frm.R: <1b>[1;30m2025-12-05 19:13:06.11<1b>[0m Returning adjusted frm object > test-adjust_frm.R: <1b>[1;30m2025-12-05 19:13:06.27<1b>[0m Starting model Adjustment > test-adjust_frm.R: <1b>[1;30m2025-12-05 19:13:06.27<1b>[0m dim(original_data): 442 x 126 > test-adjust_frm.R: <1b>[1;30m2025-12-05 19:13:06.27<1b>[0m dim(new_data): 25 x 3 > test-adjust_frm.R: <1b>[1;30m2025-12-05 19:13:06.27<1b>[0m predictors: 1, 2 > test-adjust_frm.R: <1b>[1;30m2025-12-05 19:13:06.27<1b>[0m nfolds: 5 > test-adjust_frm.R: <1b>[1;30m2025-12-05 19:13:06.27<1b>[0m Preprocessing data > test-adjust_frm.R: <1b>[1;30m2025-12-05 19:13:06.27<1b>[0m Formula: RT_ADJ ~ RT + I(RT^2) > test-adjust_frm.R: <1b>[1;30m2025-12-05 19:13:06.27<1b>[0m Estimating performance of adjusted model in CV > test-adjust_frm.R: <1b>[1;30m2025-12-05 19:13:06.31<1b>[0m Fitting adjustment model on full new data set > test-adjust_frm.R: <1b>[1;30m2025-12-05 19:13:06.31<1b>[0m Returning adjusted frm object > test-adjust_frm.R: <1b>[1;30m2025-12-05 19:13:06.31<1b>[0m Starting model Adjustment > test-adjust_frm.R: <1b>[1;30m2025-12-05 19:13:06.31<1b>[0m dim(original_data): 442 x 126 > test-adjust_frm.R: <1b>[1;30m2025-12-05 19:13:06.31<1b>[0m dim(new_data): 25 x 3 > test-adjust_frm.R: <1b>[1;30m2025-12-05 19:13:06.31<1b>[0m predictors: 1, 2, 3, 4, 5, 6 > test-adjust_frm.R: <1b>[1;30m2025-12-05 19:13:06.31<1b>[0m nfolds: 5 > test-adjust_frm.R: <1b>[1;30m2025-12-05 19:13:06.31<1b>[0m Preprocessing data > test-adjust_frm.R: <1b>[1;30m2025-12-05 19:13:06.32<1b>[0m Formula: RT_ADJ ~ RT + I(RT^2) + I(RT^3) + log(RT) + exp(RT) + sqrt(RT) > test-adjust_frm.R: <1b>[1;30m2025-12-05 19:13:06.32<1b>[0m Estimating performance of adjusted model in CV > test-adjust_frm.R: <1b>[1;30m2025-12-05 19:13:06.35<1b>[0m Fitting adjustment model on full new data set > test-adjust_frm.R: <1b>[1;30m2025-12-05 19:13:06.35<1b>[0m Returning adjusted frm object > test-selective_measuring.R: <1b>[1;30m2025-12-05 19:13:06.59<1b>[0m Starting Selective Measuring > test-selective_measuring.R: <1b>[1;30m2025-12-05 19:13:06.59<1b>[0m Preprocessing input data > test-selective_measuring.R: <1b>[1;30m2025-12-05 19:13:06.59<1b>[0m Mocking is enabled for 'preprocess_data'. Returning 'mockdata/RPCD_prepro.rds'. > test-selective_measuring.R: <1b>[1;30m2025-12-05 19:13:06.59<1b>[0m Standardizing features > test-selective_measuring.R: <1b>[1;30m2025-12-05 19:13:06.60<1b>[0m Training Ridge Regression model > test-selective_measuring.R: <1b>[1;30m2025-12-05 19:13:06.60<1b>[0m Fitting Ridge model > test-selective_measuring.R: <1b>[1;30m2025-12-05 19:13:07.02<1b>[0m End training > test-selective_measuring.R: <1b>[1;30m2025-12-05 19:13:07.02<1b>[0m Scaling features by coefficients of Ridge Regression model > test-selective_measuring.R: <1b>[1;30m2025-12-05 19:13:07.03<1b>[0m Applying PAM clustering > test-selective_measuring.R: <1b>[1;30m2025-12-05 19:13:07.30<1b>[0m Returning clustering results [ FAIL 3 | WARN 5 | SKIP 0 | PASS 19 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-train_frm-gbtree.R:5:5'): train_frm works if `method == "GBTree"` ── <subscriptOutOfBoundsError/error/condition> Error in `FUN(X[[i]], ...)`: subscript out of bounds Backtrace: ▆ 1. └─FastRet::train_frm(...) at test-train_frm-gbtree.R:5:5 2. └─base::lapply(tmp, "[[", 2) ── Error ('test-fit_gbtree.R:8:5'): fit.gbtrees works as expected ────────────── Error in `begin_iteration:end_iteration`: argument of length 0 Backtrace: ▆ 1. └─FastRet:::fit_gbtree(df, verbose = 0) at test-fit_gbtree.R:8:5 2. └─FastRet:::fit_gbtree_grid(...) 3. └─xgboost::xgb.train(...) ── Error ('test-fit_gbtree.R:16:5'): fit.gbtrees works for data from reverse phase column ── Error in `begin_iteration:end_iteration`: argument of length 0 Backtrace: ▆ 1. └─FastRet:::fit_gbtree(df, verbose = 0) at test-fit_gbtree.R:16:5 2. └─FastRet:::fit_gbtree_grid(...) 3. └─xgboost::xgb.train(...) [ FAIL 3 | WARN 5 | SKIP 0 | PASS 19 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-debian-gcc

Version: 1.1.4
Check: tests
Result: ERROR Running ‘testthat.R’ [65s/112s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(FastRet) > > test_check("FastRet") Starting 2 test processes. Saving _problems/test-train_frm-gbtree-11.R Saving _problems/test-fit_gbtree-8.R Saving _problems/test-fit_gbtree-16.R > test-plot_frm.R: <1b>[1;30m2025-12-03 10:48:47.27<1b>[0m Starting training of a lasso model > test-plot_frm.R: <1b>[1;30m2025-12-03 10:48:47.27<1b>[0m Mocking is enabled. Returning 'mockdata/lasso_model.rds' > test-plot_frm.R: <1b>[1;30m2025-12-03 10:48:47.89<1b>[0m Starting training of a lasso model > test-plot_frm.R: <1b>[1;30m2025-12-03 10:48:47.89<1b>[0m Mocking is enabled. Returning 'mockdata/lasso_model.rds' > test-plot_frm.R: <1b>[1;30m2025-12-03 10:48:47.94<1b>[0m Starting model Adjustment > test-plot_frm.R: <1b>[1;30m2025-12-03 10:48:47.95<1b>[0m dim(original_data): 442 x 126 > test-adjust_frm.R: <1b>[1;30m2025-12-03 10:48:48.00<1b>[0m Starting model Adjustment > test-adjust_frm.R: <1b>[1;30m2025-12-03 10:48:48.00<1b>[0m dim(original_data): 442 x 126 > test-adjust_frm.R: <1b>[1;30m2025-12-03 10:48:48.00<1b>[0m dim(new_data): 25 x 3 > test-adjust_frm.R: <1b>[1;30m2025-12-03 10:48:48.00<1b>[0m predictors: 1, 2 > test-adjust_frm.R: <1b>[1;30m2025-12-03 10:48:48.00<1b>[0m nfolds: 5 > test-adjust_frm.R: <1b>[1;30m2025-12-03 10:48:48.00<1b>[0m Preprocessing data > test-adjust_frm.R: <1b>[1;30m2025-12-03 10:48:48.03<1b>[0m Formula: RT_ADJ ~ RT + I(RT^2) > test-adjust_frm.R: <1b>[1;30m2025-12-03 10:48:48.04<1b>[0m Estimating performance of adjusted model in CV > test-adjust_frm.R: <1b>[1;30m2025-12-03 10:48:48.21<1b>[0m Fitting adjustment model on full new data set > test-adjust_frm.R: <1b>[1;30m2025-12-03 10:48:48.22<1b>[0m Returning adjusted frm object > test-adjust_frm.R: <1b>[1;30m2025-12-03 10:48:48.22<1b>[0m Starting model Adjustment > test-adjust_frm.R: <1b>[1;30m2025-12-03 10:48:48.22<1b>[0m dim(original_data): 442 x 126 > test-adjust_frm.R: <1b>[1;30m2025-12-03 10:48:48.22<1b>[0m dim(new_data): 25 x 3 > test-adjust_frm.R: <1b>[1;30m2025-12-03 10:48:48.22<1b>[0m predictors: 1, 2, 3, 4, 5, 6 > test-adjust_frm.R: <1b>[1;30m2025-12-03 10:48:48.22<1b>[0m nfolds: 5 > test-adjust_frm.R: <1b>[1;30m2025-12-03 10:48:48.22<1b>[0m Preprocessing data > test-adjust_frm.R: <1b>[1;30m2025-12-03 10:48:48.25<1b>[0m Formula: RT_ADJ ~ RT + I(RT^2) + I(RT^3) + log(RT) + exp(RT) + sqrt(RT) > test-adjust_frm.R: <1b>[1;30m2025-12-03 10:48:48.26<1b>[0m Estimating performance of adjusted model in CV > test-adjust_frm.R: <1b>[1;30m2025-12-03 10:48:48.43<1b>[0m Fitting adjustment model on full new data set > test-adjust_frm.R: <1b>[1;30m2025-12-03 10:48:48.44<1b>[0m Returning adjusted frm object > test-selective_measuring.R: <1b>[1;30m2025-12-03 10:48:49.33<1b>[0m Starting Selective Measuring > test-selective_measuring.R: <1b>[1;30m2025-12-03 10:48:49.34<1b>[0m Preprocessing input data > test-selective_measuring.R: <1b>[1;30m2025-12-03 10:48:49.34<1b>[0m Mocking is enabled for 'preprocess_data'. Returning 'mockdata/RPCD_prepro.rds'. > test-selective_measuring.R: <1b>[1;30m2025-12-03 10:48:49.39<1b>[0m Standardizing features > test-selective_measuring.R: <1b>[1;30m2025-12-03 10:48:49.43<1b>[0m Training Ridge Regression model > test-selective_measuring.R: <1b>[1;30m2025-12-03 10:48:49.44<1b>[0m Fitting Ridge model > test-selective_measuring.R: <1b>[1;30m2025-12-03 10:48:51.27<1b>[0m End training > test-selective_measuring.R: <1b>[1;30m2025-12-03 10:48:51.27<1b>[0m Scaling features by coefficients of Ridge Regression model > test-selective_measuring.R: <1b>[1;30m2025-12-03 10:48:51.29<1b>[0m Applying PAM clustering > test-selective_measuring.R: <1b>[1;30m2025-12-03 10:48:52.16<1b>[0m Returning clustering results > test-plot_frm.R: <1b>[1;30m2025-12-03 10:48:47.95<1b>[0m dim(new_data): 25 x 3 > test-plot_frm.R: <1b>[1;30m2025-12-03 10:48:53.23<1b>[0m predictors: 1, 2, 3, 4, 5, 6 > test-plot_frm.R: <1b>[1;30m2025-12-03 10:48:53.23<1b>[0m nfolds: 5 > test-plot_frm.R: <1b>[1;30m2025-12-03 10:48:53.23<1b>[0m Preprocessing data > test-plot_frm.R: <1b>[1;30m2025-12-03 10:48:53.28<1b>[0m Formula: RT_ADJ ~ RT + I(RT^2) + I(RT^3) + log(RT) + exp(RT) + sqrt(RT) > test-plot_frm.R: <1b>[1;30m2025-12-03 10:48:53.29<1b>[0m Estimating performance of adjusted model in CV > test-plot_frm.R: <1b>[1;30m2025-12-03 10:48:53.69<1b>[0m Fitting adjustment model on full new data set > test-plot_frm.R: <1b>[1;30m2025-12-03 10:48:53.72<1b>[0m Returning adjusted frm object [ FAIL 3 | WARN 5 | SKIP 0 | PASS 19 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-train_frm-gbtree.R:5:5'): train_frm works if `method == "GBTree"` ── <subscriptOutOfBoundsError/error/condition> Error in `FUN(X[[i]], ...)`: subscript out of bounds Backtrace: ▆ 1. └─FastRet::train_frm(...) at test-train_frm-gbtree.R:5:5 2. └─base::lapply(tmp, "[[", 2) ── Error ('test-fit_gbtree.R:8:5'): fit.gbtrees works as expected ────────────── Error in `begin_iteration:end_iteration`: argument of length 0 Backtrace: ▆ 1. └─FastRet:::fit_gbtree(df, verbose = 0) at test-fit_gbtree.R:8:5 2. └─FastRet:::fit_gbtree_grid(...) 3. └─xgboost::xgb.train(...) ── Error ('test-fit_gbtree.R:16:5'): fit.gbtrees works for data from reverse phase column ── Error in `begin_iteration:end_iteration`: argument of length 0 Backtrace: ▆ 1. └─FastRet:::fit_gbtree(df, verbose = 0) at test-fit_gbtree.R:16:5 2. └─FastRet:::fit_gbtree_grid(...) 3. └─xgboost::xgb.train(...) [ FAIL 3 | WARN 5 | SKIP 0 | PASS 19 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-fedora-clang

Version: 1.1.4
Check: tests
Result: ERROR Running ‘testthat.R’ [65s/142s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(FastRet) > > test_check("FastRet") Starting 2 test processes. Saving _problems/test-train_frm-gbtree-11.R Saving _problems/test-fit_gbtree-8.R Saving _problems/test-fit_gbtree-16.R > test-plot_frm.R: <1b>[1;30m2025-12-03 11:34:54.34<1b>[0m Starting training of a lasso model > test-plot_frm.R: <1b>[1;30m2025-12-03 11:34:54.35<1b>[0m Mocking is enabled. Returning 'mockdata/lasso_model.rds' > test-plot_frm.R: <1b>[1;30m2025-12-03 11:34:56.18<1b>[0m Starting training of a lasso model > test-plot_frm.R: <1b>[1;30m2025-12-03 11:34:56.18<1b>[0m Mocking is enabled. Returning 'mockdata/lasso_model.rds' > test-plot_frm.R: <1b>[1;30m2025-12-03 11:34:56.26<1b>[0m Starting model Adjustment > test-plot_frm.R: <1b>[1;30m2025-12-03 11:34:56.26<1b>[0m dim(original_data): 442 x 126 > test-plot_frm.R: <1b>[1;30m2025-12-03 11:34:56.26<1b>[0m dim(new_data): 25 x 3 > test-plot_frm.R: <1b>[1;30m2025-12-03 11:34:57.05<1b>[0m predictors: 1, 2, 3, 4, 5, 6 > test-plot_frm.R: <1b>[1;30m2025-12-03 11:34:57.05<1b>[0m nfolds: 5 > test-plot_frm.R: <1b>[1;30m2025-12-03 11:34:57.05<1b>[0m Preprocessing data > test-plot_frm.R: <1b>[1;30m2025-12-03 11:34:57.14<1b>[0m Formula: RT_ADJ ~ RT + I(RT^2) + I(RT^3) + log(RT) + exp(RT) + sqrt(RT) > test-plot_frm.R: <1b>[1;30m2025-12-03 11:34:57.16<1b>[0m Estimating performance of adjusted model in CV > test-plot_frm.R: <1b>[1;30m2025-12-03 11:34:57.66<1b>[0m Fitting adjustment model on full new data set > test-plot_frm.R: <1b>[1;30m2025-12-03 11:34:57.68<1b>[0m Returning adjusted frm object > test-adjust_frm.R: <1b>[1;30m2025-12-03 11:34:58.80<1b>[0m Starting model Adjustment > test-adjust_frm.R: <1b>[1;30m2025-12-03 11:34:58.82<1b>[0m dim(original_data): 442 x 126 > test-adjust_frm.R: <1b>[1;30m2025-12-03 11:34:58.82<1b>[0m dim(new_data): 25 x 3 > test-adjust_frm.R: <1b>[1;30m2025-12-03 11:34:58.82<1b>[0m predictors: 1, 2 > test-adjust_frm.R: <1b>[1;30m2025-12-03 11:34:58.82<1b>[0m nfolds: 5 > test-adjust_frm.R: <1b>[1;30m2025-12-03 11:34:58.82<1b>[0m Preprocessing data > test-adjust_frm.R: <1b>[1;30m2025-12-03 11:34:58.87<1b>[0m Formula: RT_ADJ ~ RT + I(RT^2) > test-adjust_frm.R: <1b>[1;30m2025-12-03 11:34:58.89<1b>[0m Estimating performance of adjusted model in CV > test-adjust_frm.R: <1b>[1;30m2025-12-03 11:34:59.24<1b>[0m Fitting adjustment model on full new data set > test-adjust_frm.R: <1b>[1;30m2025-12-03 11:34:59.27<1b>[0m Returning adjusted frm object > test-adjust_frm.R: <1b>[1;30m2025-12-03 11:34:59.27<1b>[0m Starting model Adjustment > test-adjust_frm.R: <1b>[1;30m2025-12-03 11:34:59.27<1b>[0m dim(original_data): 442 x 126 > test-adjust_frm.R: <1b>[1;30m2025-12-03 11:34:59.27<1b>[0m dim(new_data): 25 x 3 > test-adjust_frm.R: <1b>[1;30m2025-12-03 11:34:59.27<1b>[0m predictors: 1, 2, 3, 4, 5, 6 > test-adjust_frm.R: <1b>[1;30m2025-12-03 11:34:59.27<1b>[0m nfolds: 5 > test-adjust_frm.R: <1b>[1;30m2025-12-03 11:34:59.27<1b>[0m Preprocessing data > test-adjust_frm.R: <1b>[1;30m2025-12-03 11:34:59.35<1b>[0m Formula: RT_ADJ ~ RT + I(RT^2) + I(RT^3) + log(RT) + exp(RT) + sqrt(RT) > test-adjust_frm.R: <1b>[1;30m2025-12-03 11:34:59.37<1b>[0m Estimating performance of adjusted model in CV > test-adjust_frm.R: <1b>[1;30m2025-12-03 11:35:00.33<1b>[0m Fitting adjustment model on full new data set > test-adjust_frm.R: <1b>[1;30m2025-12-03 11:35:00.37<1b>[0m Returning adjusted frm object > test-selective_measuring.R: <1b>[1;30m2025-12-03 11:35:01.45<1b>[0m Starting Selective Measuring > test-selective_measuring.R: <1b>[1;30m2025-12-03 11:35:01.45<1b>[0m Preprocessing input data > test-selective_measuring.R: <1b>[1;30m2025-12-03 11:35:01.45<1b>[0m Mocking is enabled for 'preprocess_data'. Returning 'mockdata/RPCD_prepro.rds'. > test-selective_measuring.R: <1b>[1;30m2025-12-03 11:35:01.48<1b>[0m Standardizing features > test-selective_measuring.R: <1b>[1;30m2025-12-03 11:35:01.52<1b>[0m Training Ridge Regression model > test-selective_measuring.R: <1b>[1;30m2025-12-03 11:35:01.53<1b>[0m Fitting Ridge model > test-selective_measuring.R: <1b>[1;30m2025-12-03 11:35:04.75<1b>[0m End training > test-selective_measuring.R: <1b>[1;30m2025-12-03 11:35:04.76<1b>[0m Scaling features by coefficients of Ridge Regression model > test-selective_measuring.R: <1b>[1;30m2025-12-03 11:35:05.04<1b>[0m Applying PAM clustering > test-selective_measuring.R: <1b>[1;30m2025-12-03 11:35:10.56<1b>[0m Returning clustering results [ FAIL 3 | WARN 5 | SKIP 0 | PASS 19 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-train_frm-gbtree.R:5:5'): train_frm works if `method == "GBTree"` ── <subscriptOutOfBoundsError/error/condition> Error in `FUN(X[[i]], ...)`: subscript out of bounds Backtrace: ▆ 1. └─FastRet::train_frm(...) at test-train_frm-gbtree.R:5:5 2. └─base::lapply(tmp, "[[", 2) ── Error ('test-fit_gbtree.R:8:5'): fit.gbtrees works as expected ────────────── Error in `begin_iteration:end_iteration`: argument of length 0 Backtrace: ▆ 1. └─FastRet:::fit_gbtree(df, verbose = 0) at test-fit_gbtree.R:8:5 2. └─FastRet:::fit_gbtree_grid(...) 3. └─xgboost::xgb.train(...) ── Error ('test-fit_gbtree.R:16:5'): fit.gbtrees works for data from reverse phase column ── Error in `begin_iteration:end_iteration`: argument of length 0 Backtrace: ▆ 1. └─FastRet:::fit_gbtree(df, verbose = 0) at test-fit_gbtree.R:16:5 2. └─FastRet:::fit_gbtree_grid(...) 3. └─xgboost::xgb.train(...) [ FAIL 3 | WARN 5 | SKIP 0 | PASS 19 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 1.1.4
Check: tests
Result: ERROR Running 'testthat.R' [25s] Running the tests in 'tests/testthat.R' failed. Complete output: > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(FastRet) > > test_check("FastRet") Starting 2 test processes. Saving _problems/test-train_frm-gbtree-11.R > test-read_rp_xlsx.R: WARNING: An illegal reflective access operation has occurred > test-read_rp_xlsx.R: WARNING: Illegal reflective access by org.apache.poi.openxml4j.util.ZipSecureFile (file:/D:/RCompile/CRANpkg/lib/4.4/xlsxjars/java/poi-ooxml-3.13-20150929.jar) to field java.io.FilterInputStream.in > test-read_rp_xlsx.R: WARNING: Please consider reporting this to the maintainers of org.apache.poi.openxml4j.util.ZipSecureFile > test-read_rp_xlsx.R: WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations > test-read_rp_xlsx.R: WARNING: All illegal access operations will be denied in a future release > test-read_rp_xlsx.R: > test-plot_frm.R: <1b>[1;30m2025-12-05 07:38:19.12<1b>[0m Starting training of a lasso model > test-plot_frm.R: <1b>[1;30m2025-12-05 07:38:19.12<1b>[0m Mocking is enabled. Returning 'mockdata/lasso_model.rds' Saving _problems/test-fit_gbtree-8.R Saving _problems/test-fit_gbtree-16.R > test-plot_frm.R: <1b>[1;30m2025-12-05 07:38:19.39<1b>[0m Starting training of a lasso model > test-plot_frm.R: <1b>[1;30m2025-12-05 07:38:19.39<1b>[0m Parallel processing is not supported on Windows. Setting `nw` to 1. > test-plot_frm.R: <1b>[1;30m2025-12-05 07:38:19.39<1b>[0m Mocking is enabled. Returning 'mockdata/lasso_model.rds' > test-plot_frm.R: <1b>[1;30m2025-12-05 07:38:19.40<1b>[0m Starting model Adjustment > test-plot_frm.R: <1b>[1;30m2025-12-05 07:38:19.40<1b>[0m dim(original_data): 442 x 126 > test-plot_frm.R: <1b>[1;30m2025-12-05 07:38:19.40<1b>[0m dim(new_data): 25 x 3 > test-plot_frm.R: <1b>[1;30m2025-12-05 07:38:19.53<1b>[0m predictors: 1, 2, 3, 4, 5, 6 > test-plot_frm.R: <1b>[1;30m2025-12-05 07:38:19.53<1b>[0m nfolds: 5 > test-plot_frm.R: <1b>[1;30m2025-12-05 07:38:19.53<1b>[0m Preprocessing data > test-plot_frm.R: <1b>[1;30m2025-12-05 07:38:19.56<1b>[0m Formula: RT_ADJ ~ RT + I(RT^2) + I(RT^3) + log(RT) + exp(RT) + sqrt(RT) > test-plot_frm.R: <1b>[1;30m2025-12-05 07:38:19.57<1b>[0m Estimating performance of adjusted model in CV > test-plot_frm.R: <1b>[1;30m2025-12-05 07:38:19.65<1b>[0m Fitting adjustment model on full new data set > test-plot_frm.R: <1b>[1;30m2025-12-05 07:38:19.66<1b>[0m Returning adjusted frm object > test-adjust_frm.R: WARNING: An illegal reflective access operation has occurred > test-adjust_frm.R: WARNING: Illegal reflective access by org.apache.poi.openxml4j.util.ZipSecureFile (file:/D:/RCompile/CRANpkg/lib/4.4/xlsxjars/java/poi-ooxml-3.13-20150929.jar) to field java.io.FilterInputStream.in > test-adjust_frm.R: WARNING: Please consider reporting this to the maintainers of org.apache.poi.openxml4j.util.ZipSecureFile > test-adjust_frm.R: WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations > test-adjust_frm.R: WARNING: All illegal access operations will be denied in a future release > test-adjust_frm.R: > test-selective_measuring.R: <1b>[1;30m2025-12-05 07:38:20.35<1b>[0m Starting Selective Measuring > test-selective_measuring.R: <1b>[1;30m2025-12-05 07:38:20.35<1b>[0m Preprocessing input data > test-selective_measuring.R: <1b>[1;30m2025-12-05 07:38:20.35<1b>[0m Mocking is enabled for 'preprocess_data'. Returning 'mockdata/RPCD_prepro.rds'. > test-selective_measuring.R: <1b>[1;30m2025-12-05 07:38:20.35<1b>[0m Standardizing features > test-selective_measuring.R: <1b>[1;30m2025-12-05 07:38:20.36<1b>[0m Training Ridge Regression model > test-selective_measuring.R: <1b>[1;30m2025-12-05 07:38:20.36<1b>[0m Fitting Ridge model > test-adjust_frm.R: <1b>[1;30m2025-12-05 07:38:20.73<1b>[0m Starting model Adjustment > test-adjust_frm.R: <1b>[1;30m2025-12-05 07:38:20.73<1b>[0m dim(original_data): 442 x 126 > test-adjust_frm.R: <1b>[1;30m2025-12-05 07:38:20.73<1b>[0m dim(new_data): 25 x 3 > test-adjust_frm.R: <1b>[1;30m2025-12-05 07:38:20.73<1b>[0m predictors: 1, 2 > test-adjust_frm.R: <1b>[1;30m2025-12-05 07:38:20.73<1b>[0m nfolds: 5 > test-adjust_frm.R: <1b>[1;30m2025-12-05 07:38:20.73<1b>[0m Preprocessing data > test-adjust_frm.R: <1b>[1;30m2025-12-05 07:38:20.74<1b>[0m Formula: RT_ADJ ~ RT + I(RT^2) > test-adjust_frm.R: <1b>[1;30m2025-12-05 07:38:20.74<1b>[0m Estimating performance of adjusted model in CV > test-adjust_frm.R: <1b>[1;30m2025-12-05 07:38:20.83<1b>[0m Fitting adjustment model on full new data set > test-adjust_frm.R: <1b>[1;30m2025-12-05 07:38:20.83<1b>[0m Returning adjusted frm object > test-adjust_frm.R: <1b>[1;30m2025-12-05 07:38:20.83<1b>[0m Starting model Adjustment > test-adjust_frm.R: <1b>[1;30m2025-12-05 07:38:20.83<1b>[0m dim(original_data): 442 x 126 > test-adjust_frm.R: <1b>[1;30m2025-12-05 07:38:20.83<1b>[0m dim(new_data): 25 x 3 > test-adjust_frm.R: <1b>[1;30m2025-12-05 07:38:20.83<1b>[0m predictors: 1, 2, 3, 4, 5, 6 > test-adjust_frm.R: <1b>[1;30m2025-12-05 07:38:20.83<1b>[0m nfolds: 5 > test-adjust_frm.R: <1b>[1;30m2025-12-05 07:38:20.83<1b>[0m Preprocessing data > test-adjust_frm.R: <1b>[1;30m2025-12-05 07:38:20.84<1b>[0m Formula: RT_ADJ ~ RT + I(RT^2) + I(RT^3) + log(RT) + exp(RT) + sqrt(RT) > test-adjust_frm.R: <1b>[1;30m2025-12-05 07:38:20.84<1b>[0m Estimating performance of adjusted model in CV > test-adjust_frm.R: <1b>[1;30m2025-12-05 07:38:20.91<1b>[0m Fitting adjustment model on full new data set > test-adjust_frm.R: <1b>[1;30m2025-12-05 07:38:20.91<1b>[0m Returning adjusted frm object > test-selective_measuring.R: <1b>[1;30m2025-12-05 07:38:21.00<1b>[0m End training > test-selective_measuring.R: <1b>[1;30m2025-12-05 07:38:21.00<1b>[0m Scaling features by coefficients of Ridge Regression model > test-selective_measuring.R: <1b>[1;30m2025-12-05 07:38:21.01<1b>[0m Applying PAM clustering > test-selective_measuring.R: <1b>[1;30m2025-12-05 07:38:21.62<1b>[0m Returning clustering results [ FAIL 3 | WARN 3 | SKIP 0 | PASS 19 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-train_frm-gbtree.R:5:5'): train_frm works if `method == "GBTree"` ── Error in `begin_iteration:end_iteration`: argument of length 0 Backtrace: ▆ 1. └─FastRet::train_frm(...) at test-train_frm-gbtree.R:5:5 2. └─parallel::mclapply(...) 3. └─base::lapply(X, FUN, ...) 4. └─FastRet (local) FUN(X[[i]], ...) 5. └─FastRet (local) fit(df[train, ], verbose = 0) 6. └─FastRet:::fit_gbtree_grid(...) 7. └─xgboost::xgb.train(...) ── Error ('test-fit_gbtree.R:8:5'): fit.gbtrees works as expected ────────────── Error in `begin_iteration:end_iteration`: argument of length 0 Backtrace: ▆ 1. └─FastRet:::fit_gbtree(df, verbose = 0) at test-fit_gbtree.R:8:5 2. └─FastRet:::fit_gbtree_grid(...) 3. └─xgboost::xgb.train(...) ── Error ('test-fit_gbtree.R:16:5'): fit.gbtrees works for data from reverse phase column ── Error in `begin_iteration:end_iteration`: argument of length 0 Backtrace: ▆ 1. └─FastRet:::fit_gbtree(df, verbose = 0) at test-fit_gbtree.R:16:5 2. └─FastRet:::fit_gbtree_grid(...) 3. └─xgboost::xgb.train(...) [ FAIL 3 | WARN 3 | SKIP 0 | PASS 19 ] Error: ! Test failures. Execution halted Flavor: r-oldrel-windows-x86_64