CRAN Package Check Results for Package hdcuremodels

Last updated on 2025-11-20 01:48:34 CET.

Flavor	Version	T_install	T_check	T_total	Status
r-devel-linux-x86_64-debian-clang	0.0.5	19.35	614.86	634.21	ERROR
r-devel-linux-x86_64-debian-gcc	0.0.5	12.17	356.19	368.36	ERROR
r-devel-linux-x86_64-fedora-clang	0.0.5	50.00	971.26	1021.26	ERROR
r-devel-linux-x86_64-fedora-gcc	0.0.5	80.00	997.20	1077.20	ERROR
r-devel-windows-x86_64	0.0.5	25.00	562.00	587.00	OK
r-patched-linux-x86_64	0.0.5	18.41	592.48	610.89	ERROR
r-release-linux-x86_64	0.0.5	17.34	588.06	605.40	ERROR
r-release-macos-arm64	0.0.5				OK
r-release-macos-x86_64	0.0.5	15.00	342.00	357.00	OK
r-release-windows-x86_64	0.0.5	19.00	546.00	565.00	OK
r-oldrel-macos-arm64	0.0.5				OK
r-oldrel-macos-x86_64	0.0.5	14.00	371.00	385.00	OK
r-oldrel-windows-x86_64	0.0.5	27.00	758.00	785.00	OK

Check Details

Version: 0.0.5
Check: tests
Result: ERROR Running ‘testthat.R’ [339s/418s] 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: 0.724278947141186 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: 0.71552008323144 Fitting a final model... 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 Saving _problems/test-formula-11.R Saving _problems/test-logLik-11.R Saving _problems/test-nobs-11.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 lambda for incidence: 0.155 Selected lambda for latency: 0.155 Maximum C-statistic: 0.635331032110145 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: 0.724278947141186 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: 0.724278947141186 Fitting a final model... mixturecure object fit using Cox EM algorithm $b 0 0 0 0 0 0 6 more elements $beta 0 0 0.8829866 0 0.3677159 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 = -118.896956094066 at step = 51 AIC = 273.793912188132 at step = 51 mAIC = 490.199530466681 at step = 51 cAIC = 290.4768390174 at step = 51 BIC = 311.492114308129 at step = 51 mBIC = 453.037837086205 at step = 51 EBIC = 370.050413661889 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 = -118.896956094066 at step = 51 AIC = 273.793912188132 at step = 51 mAIC = 490.199530466681 at step = 51 cAIC = 290.4768390174 at step = 51 BIC = 311.492114308129 at step = 51 mBIC = 453.037837086205 at step = 51 EBIC = 370.050413661889 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: 0.703222725537594 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 3 | WARN 0 | SKIP 0 | PASS 552 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-formula.R:11:3'): formula function works correctly ───────────── Error in `expect(is.call(formula(fit)))`: `failure_message` must be a character vector, not absent. Backtrace: ▆ 1. └─testthat::expect(failure_message = <empty>) at test-formula.R:11:3 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-logLik.R:11:3'): logLik function works correctly ─────────────── Error in `expect(round(logLik(fit), 5), -9.22893)`: `ok` must be `TRUE` or `FALSE`, not a <logLik> object. Backtrace: ▆ 1. └─testthat::expect(ok = round(logLik(fit), 5)) at test-logLik.R:11:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-nobs.R:11:3'): nobs function works correctly ─────────────────── Error in `expect(nobs(fit), 60)`: `ok` must be `TRUE` or `FALSE`, not the number 60. Backtrace: ▆ 1. └─testthat::expect(ok = nobs(fit)) at test-nobs.R:11:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 3 | WARN 0 | SKIP 0 | PASS 552 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-debian-clang

Version: 0.0.5
Check: tests
Result: ERROR Running ‘testthat.R’ [185s/208s] 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: 0.724278947141186 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: 0.71552008323144 Fitting a final model... 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 Saving _problems/test-formula-11.R Saving _problems/test-logLik-11.R Saving _problems/test-nobs-11.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 lambda for incidence: 0.155 Selected lambda for latency: 0.155 Maximum C-statistic: 0.635331032113726 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: 0.724278947141186 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: 0.724278947141186 Fitting a final model... mixturecure object fit using Cox EM algorithm $b 0 0 0 0 0 0 6 more elements $beta 0 0 0.8829866 0 0.3677159 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 = -118.896956094066 at step = 51 AIC = 273.793912188132 at step = 51 mAIC = 490.199530466681 at step = 51 cAIC = 290.4768390174 at step = 51 BIC = 311.492114308129 at step = 51 mBIC = 453.037837086205 at step = 51 EBIC = 370.050413661889 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 = -118.896956094066 at step = 51 AIC = 273.793912188132 at step = 51 mAIC = 490.199530466681 at step = 51 cAIC = 290.4768390174 at step = 51 BIC = 311.492114308129 at step = 51 mBIC = 453.037837086205 at step = 51 EBIC = 370.050413661889 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: 0.703222725537594 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 3 | WARN 0 | SKIP 0 | PASS 552 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-formula.R:11:3'): formula function works correctly ───────────── Error in `expect(is.call(formula(fit)))`: `failure_message` must be a character vector, not absent. Backtrace: ▆ 1. └─testthat::expect(failure_message = <empty>) at test-formula.R:11:3 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-logLik.R:11:3'): logLik function works correctly ─────────────── Error in `expect(round(logLik(fit), 5), -9.22893)`: `ok` must be `TRUE` or `FALSE`, not a <logLik> object. Backtrace: ▆ 1. └─testthat::expect(ok = round(logLik(fit), 5)) at test-logLik.R:11:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-nobs.R:11:3'): nobs function works correctly ─────────────────── Error in `expect(nobs(fit), 60)`: `ok` must be `TRUE` or `FALSE`, not the number 60. Backtrace: ▆ 1. └─testthat::expect(ok = nobs(fit)) at test-nobs.R:11:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 3 | WARN 0 | SKIP 0 | PASS 552 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-debian-gcc

Version: 0.0.5
Check: tests
Result: ERROR Running ‘testthat.R’ [9m/16m] 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: 0.724278947141186 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: 0.715520083276069 Fitting a final model... 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 Saving _problems/test-formula-11.R Saving _problems/test-logLik-11.R Saving _problems/test-nobs-11.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 lambda for incidence: 0.155 Selected lambda for latency: 0.155 Maximum C-statistic: 0.63533103210919 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: 0.724278947141186 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: 0.724278947141186 Fitting a final model... mixturecure object fit using Cox EM algorithm $b 0 0 0 0 0 0 6 more elements $beta 0 0 0.8829866 0 0.3677159 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 = -118.896956094066 at step = 51 AIC = 273.793912188132 at step = 51 mAIC = 490.199530466681 at step = 51 cAIC = 290.4768390174 at step = 51 BIC = 311.492114308129 at step = 51 mBIC = 453.037837086205 at step = 51 EBIC = 370.050413661889 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 = -118.896956094066 at step = 51 AIC = 273.793912188132 at step = 51 mAIC = 490.199530466681 at step = 51 cAIC = 290.4768390174 at step = 51 BIC = 311.492114308129 at step = 51 mBIC = 453.037837086205 at step = 51 EBIC = 370.050413661889 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: 0.703222725537594 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 3 | WARN 0 | SKIP 0 | PASS 552 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-formula.R:11:3'): formula function works correctly ───────────── Error in `expect(is.call(formula(fit)))`: `failure_message` must be a character vector, not absent. Backtrace: ▆ 1. └─testthat::expect(failure_message = <empty>) at test-formula.R:11:3 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-logLik.R:11:3'): logLik function works correctly ─────────────── Error in `expect(round(logLik(fit), 5), -9.22893)`: `ok` must be `TRUE` or `FALSE`, not a <logLik> object. Backtrace: ▆ 1. └─testthat::expect(ok = round(logLik(fit), 5)) at test-logLik.R:11:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-nobs.R:11:3'): nobs function works correctly ─────────────────── Error in `expect(nobs(fit), 60)`: `ok` must be `TRUE` or `FALSE`, not the number 60. Backtrace: ▆ 1. └─testthat::expect(ok = nobs(fit)) at test-nobs.R:11:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 3 | WARN 0 | SKIP 0 | PASS 552 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-fedora-clang

Version: 0.0.5
Check: tests
Result: ERROR Running ‘testthat.R’ [10m/27m] 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: 0.724278947141186 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: 0.691812421420305 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: 0.715520083276069 Fitting a final model... 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 Saving _problems/test-formula-11.R Saving _problems/test-logLik-11.R Saving _problems/test-nobs-11.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 lambda for incidence: 0.155 Selected lambda for latency: 0.155 Maximum C-statistic: 0.635331032165219 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: 0.724278947141186 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: 0.724278947141186 Fitting a final model... mixturecure object fit using Cox EM algorithm $b 0 0 0 0 0 0 6 more elements $beta 0 0 0.8829866 0 0.3677159 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 = -118.896956094066 at step = 51 AIC = 273.793912188132 at step = 51 mAIC = 490.199530466681 at step = 51 cAIC = 290.4768390174 at step = 51 BIC = 311.492114308129 at step = 51 mBIC = 453.037837086205 at step = 51 EBIC = 370.050413661889 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 = -118.896956094066 at step = 51 AIC = 273.793912188132 at step = 51 mAIC = 490.199530466681 at step = 51 cAIC = 290.4768390174 at step = 51 BIC = 311.492114308129 at step = 51 mBIC = 453.037837086205 at step = 51 EBIC = 370.050413661889 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: 0.703222725537594 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 3 | WARN 0 | SKIP 0 | PASS 552 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-formula.R:11:3'): formula function works correctly ───────────── Error in `expect(is.call(formula(fit)))`: `failure_message` must be a character vector, not absent. Backtrace: ▆ 1. └─testthat::expect(failure_message = <empty>) at test-formula.R:11:3 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-logLik.R:11:3'): logLik function works correctly ─────────────── Error in `expect(round(logLik(fit), 5), -9.22893)`: `ok` must be `TRUE` or `FALSE`, not a <logLik> object. Backtrace: ▆ 1. └─testthat::expect(ok = round(logLik(fit), 5)) at test-logLik.R:11:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-nobs.R:11:3'): nobs function works correctly ─────────────────── Error in `expect(nobs(fit), 60)`: `ok` must be `TRUE` or `FALSE`, not the number 60. Backtrace: ▆ 1. └─testthat::expect(ok = nobs(fit)) at test-nobs.R:11:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 3 | WARN 0 | SKIP 0 | PASS 552 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 0.0.5
Check: tests
Result: ERROR Running ‘testthat.R’ [329s/401s] 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: 0.724278947141186 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: 0.71552008323144 Fitting a final model... 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 Saving _problems/test-formula-11.R Saving _problems/test-logLik-11.R Saving _problems/test-nobs-11.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 lambda for incidence: 0.155 Selected lambda for latency: 0.155 Maximum C-statistic: 0.635331032110145 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: 0.724278947141186 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: 0.724278947141186 Fitting a final model... mixturecure object fit using Cox EM algorithm $b 0 0 0 0 0 0 6 more elements $beta 0 0 0.8829866 0 0.3677159 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 = -118.896956094066 at step = 51 AIC = 273.793912188132 at step = 51 mAIC = 490.199530466681 at step = 51 cAIC = 290.4768390174 at step = 51 BIC = 311.492114308129 at step = 51 mBIC = 453.037837086205 at step = 51 EBIC = 370.050413661889 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 = -118.896956094066 at step = 51 AIC = 273.793912188132 at step = 51 mAIC = 490.199530466681 at step = 51 cAIC = 290.4768390174 at step = 51 BIC = 311.492114308129 at step = 51 mBIC = 453.037837086205 at step = 51 EBIC = 370.050413661889 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: 0.703222725537594 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 3 | WARN 0 | SKIP 0 | PASS 552 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-formula.R:11:3'): formula function works correctly ───────────── Error in `expect(is.call(formula(fit)))`: `failure_message` must be a character vector, not absent. Backtrace: ▆ 1. └─testthat::expect(failure_message = <empty>) at test-formula.R:11:3 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-logLik.R:11:3'): logLik function works correctly ─────────────── Error in `expect(round(logLik(fit), 5), -9.22893)`: `ok` must be `TRUE` or `FALSE`, not a <logLik> object. Backtrace: ▆ 1. └─testthat::expect(ok = round(logLik(fit), 5)) at test-logLik.R:11:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-nobs.R:11:3'): nobs function works correctly ─────────────────── Error in `expect(nobs(fit), 60)`: `ok` must be `TRUE` or `FALSE`, not the number 60. Backtrace: ▆ 1. └─testthat::expect(ok = nobs(fit)) at test-nobs.R:11:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 3 | WARN 0 | SKIP 0 | PASS 552 ] Error: ! Test failures. Execution halted Flavor: r-patched-linux-x86_64

Version: 0.0.5
Check: tests
Result: ERROR Running ‘testthat.R’ [329s/422s] 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: 0.724278947141186 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: 0.71552008323144 Fitting a final model... 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 Saving _problems/test-formula-11.R Saving _problems/test-logLik-11.R Saving _problems/test-nobs-11.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 lambda for incidence: 0.155 Selected lambda for latency: 0.155 Maximum C-statistic: 0.635331032110145 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: 0.724278947141186 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: 0.724278947141186 Fitting a final model... mixturecure object fit using Cox EM algorithm $b 0 0 0 0 0 0 6 more elements $beta 0 0 0.8829866 0 0.3677159 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 = -118.896956094066 at step = 51 AIC = 273.793912188132 at step = 51 mAIC = 490.199530466681 at step = 51 cAIC = 290.4768390174 at step = 51 BIC = 311.492114308129 at step = 51 mBIC = 453.037837086205 at step = 51 EBIC = 370.050413661889 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 = -118.896956094066 at step = 51 AIC = 273.793912188132 at step = 51 mAIC = 490.199530466681 at step = 51 cAIC = 290.4768390174 at step = 51 BIC = 311.492114308129 at step = 51 mBIC = 453.037837086205 at step = 51 EBIC = 370.050413661889 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: 0.703222725537594 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 3 | WARN 0 | SKIP 0 | PASS 552 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-formula.R:11:3'): formula function works correctly ───────────── Error in `expect(is.call(formula(fit)))`: `failure_message` must be a character vector, not absent. Backtrace: ▆ 1. └─testthat::expect(failure_message = <empty>) at test-formula.R:11:3 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-logLik.R:11:3'): logLik function works correctly ─────────────── Error in `expect(round(logLik(fit), 5), -9.22893)`: `ok` must be `TRUE` or `FALSE`, not a <logLik> object. Backtrace: ▆ 1. └─testthat::expect(ok = round(logLik(fit), 5)) at test-logLik.R:11:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-nobs.R:11:3'): nobs function works correctly ─────────────────── Error in `expect(nobs(fit), 60)`: `ok` must be `TRUE` or `FALSE`, not the number 60. Backtrace: ▆ 1. └─testthat::expect(ok = nobs(fit)) at test-nobs.R:11:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 3 | WARN 0 | SKIP 0 | PASS 552 ] Error: ! Test failures. Execution halted Flavor: r-release-linux-x86_64

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