CRAN Package Check Results for Package hdcuremodels

Last updated on 2026-05-04 20:51:12 CEST.

Flavor	Version	T_install	T_check	T_total	Status
r-devel-linux-x86_64-debian-clang	0.0.6	16.71	609.83	626.54	OK
r-devel-linux-x86_64-debian-gcc	0.0.6	12.57	404.18	416.75	ERROR
r-devel-linux-x86_64-fedora-clang	0.0.6	33.00	1138.56	1171.56	ERROR
r-devel-linux-x86_64-fedora-gcc	0.0.6	30.00	960.94	990.94	OK
r-devel-windows-x86_64	0.0.6	19.00	547.00	566.00	OK
r-patched-linux-x86_64	0.0.6	16.93	573.45	590.38	OK
r-release-linux-x86_64	0.0.6	18.87	575.31	594.18	OK
r-release-macos-arm64	0.0.6	4.00	123.00	127.00	OK
r-release-macos-x86_64	0.0.6	12.00	522.00	534.00	OK
r-release-windows-x86_64	0.0.6	20.00	593.00	613.00	OK
r-oldrel-macos-arm64	0.0.6	4.00	116.00	120.00	OK
r-oldrel-macos-x86_64	0.0.6	13.00	367.00	380.00	OK
r-oldrel-windows-x86_64	0.0.6	29.00	762.00	791.00	OK

Check Details

Version: 0.0.6
Check: tests
Result: ERROR Running ‘testthat.R’ [233s/297s] 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(hdcuremodels) > > test_check("hdcuremodels") Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.083 Selected lambda for latency: 0.083 Maximum C-statistic: 0.724279198664601 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.062 Selected lambda for latency: 0.062 Maximum C-statistic: 0.756490658089751 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.062 Selected lambda for latency: 0.062 Maximum C-statistic: 0.756012758893876 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected step: 297 Maximum C-statistic: 0.770985658659945 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected step: 434 Maximum C-statistic: 0.734125636924605 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.09 Selected lambda for latency: 0.09 Maximum C-statistic: 0.691812421454834 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.105 Selected lambda for latency: 0.105 Maximum C-statistic: 0.715520083335048 Fitting a final model... Saving _problems/test-cv_cureem-285.R Fold 1 out of 2 training... Fold 2 out of 2 training... Selected step: 27 Maximum C-statistic: 0.674984053600791 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected step: 3 Maximum C-statistic: 0.697702519726813 Fitting a final model... Family: Cox Algorithm: EM Family: Cox Algorithm: EM Fold 1 out of 2 training... Fold 2 out of 2 training... Selected step: 27 Maximum C-statistic: 0.674984053600791 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.155 Selected lambda for latency: 0.155 Maximum C-statistic: 0.635331032110117 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.083 Selected lambda for latency: 0.083 Maximum C-statistic: 0.724279198664601 Fitting a final model... mixturecure object fit using Weibull GMIFS algorithm $b_path U1 U2 X1 X2 X3 X4 [1,] 0 0 0.01 0 0 0 [2,] 0 0 0.02 0 0 0 [3,] 0 0 0.03 0 0 0 [4,] 0 0 0.04 0 0 0 [5,] 0 0 0.05 0 0 0 [6,] 0 0 0.06 0 0 0 1274 more rows 6 more columns $beta_path U1 U2 X1 X2 X3 X4 [1,] 0 0 0.01 0 0 0.00 [2,] 0 0 0.02 0 0 0.00 [3,] 0 0 0.03 0 0 0.00 [4,] 0 0 0.03 0 0 0.01 [5,] 0 0 0.03 0 0 0.02 [6,] 0 0 0.03 0 0 0.03 1274 more rows 6 more columns $rate 1.74771 1.747557 1.745463 1.776432 1.806308 1.835547 1274 more elements $alpha 0.5120896 0.5130922 0.5131297 0.5149383 0.5167088 0.51841 1274 more elements Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.083 Selected lambda for latency: 0.083 Maximum C-statistic: 0.724279198664601 Fitting a final model... mixturecure object fit using Cox EM algorithm $b 0 0 0 0 0 0 6 more elements $beta 0 0 0.8833739 0 0.3678652 0 6 more elements Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.062 Selected lambda for latency: 0.062 Maximum C-statistic: 0.756490658089751 Fitting a final model... mixturecure object fit using Weibull EM algorithm $b 0 0 0.3534216 -0.1938579 0 -0.4778904 6 more elements $beta 0 0 0 0 0 0 6 more elements $rate 5.125488 $alpha 0.897226 Mixture cure model fit using the EM algorithm Number of non-zero incidence covariates at minimum AIC: 0 Number of non-zero latency covariates at minimum AIC: 17 Optimal step for selected information criterion: EM algorithm at step = 51 logLik = -118.896958515658 at step = 51 AIC = 273.793917031317 at step = 51 mAIC = 490.199535309867 at step = 51 cAIC = 290.476843860585 at step = 51 BIC = 311.492119151315 at step = 51 mBIC = 453.03784192939 at step = 51 EBIC = 370.050418505075 Mixture cure model fit using the EM algorithm Number of non-zero incidence covariates at minimum AIC: 0 Number of non-zero latency covariates at minimum AIC: 17 Optimal step for selected information criterion: EM algorithm at step = 51 logLik = -118.896958515658 at step = 51 AIC = 273.793917031317 at step = 51 mAIC = 490.199535309867 at step = 51 cAIC = 290.476843860585 at step = 51 BIC = 311.492119151315 at step = 51 mBIC = 453.03784192939 at step = 51 EBIC = 370.050418505075 Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.054 Selected lambda for latency: 0.054 Maximum C-statistic: 0.705464643379886 Fitting a final model... Mixture cure model fit using the EM algorithm using cross-validation Number of non-zero incidence covariates: 3 Number of non-zero latency covariates: 26 Mixture cure model fit using the EM algorithm using cross-validation Number of non-zero incidence covariates: 3 Number of non-zero latency covariates: 26 [ FAIL 1 | WARN 4848 | SKIP 0 | PASS 556 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-cv_cureem.R:285:3'): cv_cureem function works correctly ────── Expected `round(fit.cv$b0, 7)` to equal 0.3495074. Differences: `actual`: 0.34968 `expected`: 0.34951 [ FAIL 1 | WARN 4848 | SKIP 0 | PASS 556 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-debian-gcc

Version: 0.0.6
Check: tests
Result: ERROR Running ‘testthat.R’ [11m/33m] 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(hdcuremodels) > > test_check("hdcuremodels") Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.083 Selected lambda for latency: 0.083 Maximum C-statistic: 0.724279198664601 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.062 Selected lambda for latency: 0.062 Maximum C-statistic: 0.756490658082704 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.062 Selected lambda for latency: 0.062 Maximum C-statistic: 0.756012758916735 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected step: 297 Maximum C-statistic: 0.770985658659945 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected step: 434 Maximum C-statistic: 0.734125636924359 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.09 Selected lambda for latency: 0.09 Maximum C-statistic: 0.691812421454245 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.105 Selected lambda for latency: 0.105 Maximum C-statistic: 0.71552008335686 Fitting a final model... Saving _problems/test-cv_cureem-285.R Fold 1 out of 2 training... Fold 2 out of 2 training... Selected step: 27 Maximum C-statistic: 0.674984053587649 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected step: 3 Maximum C-statistic: 0.697702519726813 Fitting a final model... Family: Cox Algorithm: EM Family: Cox Algorithm: EM Fold 1 out of 2 training... Fold 2 out of 2 training... Selected step: 27 Maximum C-statistic: 0.674984053587649 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.155 Selected lambda for latency: 0.155 Maximum C-statistic: 0.635331032115188 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.083 Selected lambda for latency: 0.083 Maximum C-statistic: 0.724279198664601 Fitting a final model... mixturecure object fit using Weibull GMIFS algorithm $b_path U1 U2 X1 X2 X3 X4 [1,] 0 0 0.01 0 0 0 [2,] 0 0 0.02 0 0 0 [3,] 0 0 0.03 0 0 0 [4,] 0 0 0.04 0 0 0 [5,] 0 0 0.05 0 0 0 [6,] 0 0 0.06 0 0 0 1274 more rows 6 more columns $beta_path U1 U2 X1 X2 X3 X4 [1,] 0 0 0.01 0 0 0.00 [2,] 0 0 0.02 0 0 0.00 [3,] 0 0 0.03 0 0 0.00 [4,] 0 0 0.03 0 0 0.01 [5,] 0 0 0.03 0 0 0.02 [6,] 0 0 0.03 0 0 0.03 1274 more rows 6 more columns $rate 1.74771 1.747557 1.745463 1.776432 1.806308 1.835547 1274 more elements $alpha 0.5120896 0.5130922 0.5131297 0.5149383 0.5167088 0.51841 1274 more elements Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.083 Selected lambda for latency: 0.083 Maximum C-statistic: 0.724279198664601 Fitting a final model... mixturecure object fit using Cox EM algorithm $b 0 0 0 0 0 0 6 more elements $beta 0 0 0.8833739 0 0.3678652 0 6 more elements Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.062 Selected lambda for latency: 0.062 Maximum C-statistic: 0.756490658082704 Fitting a final model... mixturecure object fit using Weibull EM algorithm $b 0 0 0.3534216 -0.1938579 0 -0.4778904 6 more elements $beta 0 0 0 0 0 0 6 more elements $rate 5.125488 $alpha 0.897226 Mixture cure model fit using the EM algorithm Number of non-zero incidence covariates at minimum AIC: 0 Number of non-zero latency covariates at minimum AIC: 17 Optimal step for selected information criterion: EM algorithm at step = 51 logLik = -118.896958515658 at step = 51 AIC = 273.793917031317 at step = 51 mAIC = 490.199535309867 at step = 51 cAIC = 290.476843860585 at step = 51 BIC = 311.492119151315 at step = 51 mBIC = 453.03784192939 at step = 51 EBIC = 370.050418505075 Mixture cure model fit using the EM algorithm Number of non-zero incidence covariates at minimum AIC: 0 Number of non-zero latency covariates at minimum AIC: 17 Optimal step for selected information criterion: EM algorithm at step = 51 logLik = -118.896958515658 at step = 51 AIC = 273.793917031317 at step = 51 mAIC = 490.199535309867 at step = 51 cAIC = 290.476843860585 at step = 51 BIC = 311.492119151315 at step = 51 mBIC = 453.03784192939 at step = 51 EBIC = 370.050418505075 Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.054 Selected lambda for latency: 0.054 Maximum C-statistic: 0.705464643379886 Fitting a final model... Mixture cure model fit using the EM algorithm using cross-validation Number of non-zero incidence covariates: 3 Number of non-zero latency covariates: 26 Mixture cure model fit using the EM algorithm using cross-validation Number of non-zero incidence covariates: 3 Number of non-zero latency covariates: 26 [ FAIL 1 | WARN 4848 | SKIP 0 | PASS 556 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-cv_cureem.R:285:3'): cv_cureem function works correctly ────── Expected `round(fit.cv$b0, 7)` to equal 0.3495074. Differences: `actual`: 0.34968 `expected`: 0.34951 [ FAIL 1 | WARN 4848 | SKIP 0 | PASS 556 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-fedora-clang