CRAN Package Check Results for Package ngme2

Last updated on 2026-09-28 04:51:44 CEST.

Flavor	Version	T_install	T_check	T_total	Status
r-devel-linux-x86_64-debian-clang	0.9.8	458.87	341.23	800.10	OK
r-devel-linux-x86_64-debian-gcc	0.9.8	383.11	261.69	644.80	ERROR
r-devel-linux-x86_64-fedora-clang	1.0.0	480.00	5481.57	5961.57	ERROR
r-devel-linux-x86_64-fedora-gcc	0.9.8	465.00	5546.13	6011.13	ERROR
r-devel-windows-x86_64	0.9.8	612.00	465.00	1077.00	OK
r-patched-linux-x86_64	0.9.8	512.28	361.76	874.04	OK
r-release-linux-x86_64	0.9.8	511.24	357.43	868.67	OK
r-release-macos-arm64	1.0.0	144.00	-14.00	130.00	OK
r-release-macos-x86_64	1.0.0	468.00	331.00	799.00	OK
r-release-windows-x86_64	0.9.8	583.00	383.00	966.00	OK
r-oldrel-macos-arm64	1.0.0	186.00	-19.00	167.00	OK
r-oldrel-macos-x86_64	1.0.0	454.00	268.00	722.00	OK
r-oldrel-windows-x86_64	0.9.8	710.00	464.00	1174.00	OK

Check Details

Version: 0.9.8
Check: tests
Result: ERROR Running ‘testthat.R’ [111s/122s] 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/tests.html > # * https://testthat.r-lib.org/reference/test_package.html#special-files > > library(testthat) > > test_check("ngme2") Loading required package: ngme2 This is ngme2 of version 0.9.8 - See our homepage: https://davidbolin.github.io/ngme2 for more details. Attaching package: 'ngme2' The following object is masked from 'package:stats': ar List of 6 $ mean : num [1:3] -1.896 0.814 -1.781 $ sd : num [1:3] 0.832 0.918 0.922 $ 0.05q : num [1:3] -3.246 -0.674 -3.242 $ 0.95q : num [1:3] -0.479 2.332 -0.273 $ median: num [1:3] -1.911 0.814 -1.808 $ mode : num [1:3] -1.9 0.5 -1.9 - attr(*, "samples")= num [1:10, 1:500] -1.789 -1.145 -0.871 -0.585 0.957 ... Starting estimation... iteration = : 1 grad.norm() = 510.828 pflug_sum = 0, max_pflug_sum = 0 --------------------------- iteration = : 2 grad.norm() = 495.019 pflug_sum = 252562, max_pflug_sum = 252562 --------------------------- iteration = : 3 grad.norm() = 472.119 pflug_sum = 485657, max_pflug_sum = 485657 --------------------------- iteration = : 4 grad.norm() = 451.127 pflug_sum = 697530, max_pflug_sum = 697530 --------------------------- iteration = : 5 grad.norm() = 429.492 pflug_sum = 890730, max_pflug_sum = 890730 --------------------------- iteration = : 6 grad.norm() = 405.165 pflug_sum = 1.06456e+06, max_pflug_sum = 1.06456e+06 --------------------------- iteration = : 7 grad.norm() = 382.065 pflug_sum = 1.21892e+06, max_pflug_sum = 1.21892e+06 --------------------------- iteration = : 8 grad.norm() = 347.92 pflug_sum = 1.35139e+06, max_pflug_sum = 1.35139e+06 --------------------------- iteration = : 9 grad.norm() = 317.9 pflug_sum = 1.46059e+06, max_pflug_sum = 1.46059e+06 --------------------------- iteration = : 10 grad.norm() = 285.748 pflug_sum = 1.55016e+06, max_pflug_sum = 1.55016e+06 --------------------------- Starting posterior sampling... Posterior sampling done. Average standard deviation of the posterior W: 0.240730749817106 Use ngme_post_samples() to access posterior samples and ngme_result() to access latent model parameters. Saving _problems/test-compose-tp-bv-matern-87.R Loading required package: Matrix This is rSPDE 2.6.0 - See https://davidbolin.github.io/rSPDE for vignettes and manuals. Attaching package: 'rSPDE' The following object is masked from 'package:ngme2': cross_validation Starting estimation... iteration = : 1 grad.norm() = 0.039387 pflug_sum = 0, max_pflug_sum = 0 --------------------------- iteration = : 2 grad.norm() = 0.118299 pflug_sum = 1.7063, max_pflug_sum = 1.7063 --------------------------- iteration = : 3 grad.norm() = 0.0234002 pflug_sum = 2.3075, max_pflug_sum = 2.3075 --------------------------- iteration = : 4 grad.norm() = 0.0306022 pflug_sum = 2.34689, max_pflug_sum = 2.34689 --------------------------- iteration = : 5 grad.norm() = 0.0263656 pflug_sum = 2.15722, max_pflug_sum = 2.34689 --------------------------- iteration = : 6 grad.norm() = 0.0812295 pflug_sum = 2.21092, max_pflug_sum = 2.34689 --------------------------- iteration = : 7 grad.norm() = 0.0167089 pflug_sum = 2.3456, max_pflug_sum = 2.34689 --------------------------- iteration = : 8 grad.norm() = 0.0330382 pflug_sum = 2.29782, max_pflug_sum = 2.34689 --------------------------- iteration = : 9 grad.norm() = 0.0190493 pflug_sum = 2.35773, max_pflug_sum = 2.35773 --------------------------- iteration = : 10 grad.norm() = 0.0474104 pflug_sum = 2.33861, max_pflug_sum = 2.35773 --------------------------- iteration = : 11 grad.norm() = 0.00813236 pflug_sum = 2.3734, max_pflug_sum = 2.3734 --------------------------- iteration = : 12 grad.norm() = 0.0629826 pflug_sum = 2.38114, max_pflug_sum = 2.38114 --------------------------- iteration = : 13 grad.norm() = 0.0772953 pflug_sum = 2.27958, max_pflug_sum = 2.38114 --------------------------- iteration = : 14 grad.norm() = 0.0515648 pflug_sum = 2.05178, max_pflug_sum = 2.38114 --------------------------- iteration = : 15 grad.norm() = 0.0692457 pflug_sum = 1.68547, max_pflug_sum = 2.38114 --------------------------- iteration = : 16 grad.norm() = 0.0788014 pflug_sum = 1.49325, max_pflug_sum = 2.38114 --------------------------- iteration = : 17 grad.norm() = 0.0069869 pflug_sum = 1.44585, max_pflug_sum = 2.38114 --------------------------- iteration = : 18 grad.norm() = 0.0766556 pflug_sum = 1.82163, max_pflug_sum = 2.38114 --------------------------- iteration = : 19 grad.norm() = 0.0287867 pflug_sum = 1.87912, max_pflug_sum = 2.38114 --------------------------- iteration = : 20 grad.norm() = 0.0269602 pflug_sum = 1.86921, max_pflug_sum = 2.38114 --------------------------- iteration = : 21 grad.norm() = 0.046419 pflug_sum = 1.76009, max_pflug_sum = 2.38114 --------------------------- iteration = : 22 grad.norm() = 0.0474175 pflug_sum = 2.26167, max_pflug_sum = 2.38114 --------------------------- iteration = : 23 grad.norm() = 0.0335417 pflug_sum = 2.05299, max_pflug_sum = 2.38114 --------------------------- iteration = : 24 grad.norm() = 0.131644 pflug_sum = 1.96903, max_pflug_sum = 2.38114 --------------------------- iteration = : 25 grad.norm() = 0.077738 pflug_sum = 1.3377, max_pflug_sum = 2.38114 --------------------------- iteration = : 26 grad.norm() = 0.0213774 pflug_sum = 1.22623, max_pflug_sum = 2.38114 --------------------------- iteration = : 27 grad.norm() = 0.0829836 pflug_sum = 1.31489, max_pflug_sum = 2.38114 --------------------------- iteration = : 28 grad.norm() = 0.0498857 pflug_sum = 1.11832, max_pflug_sum = 2.38114 --------------------------- iteration = : 29 grad.norm() = 0.0312983 pflug_sum = 0.910949, max_pflug_sum = 2.38114 --------------------------- iteration = : 30 grad.norm() = 0.0158661 pflug_sum = 0.631714, max_pflug_sum = 2.38114 --------------------------- iteration = : 31 grad.norm() = 0.0180384 pflug_sum = 0.871779, max_pflug_sum = 2.38114 --------------------------- iteration = : 32 grad.norm() = 0.0361046 pflug_sum = 1.01951, max_pflug_sum = 2.38114 --------------------------- iteration = : 33 grad.norm() = 0.0437238 pflug_sum = 1.05186, max_pflug_sum = 2.38114 --------------------------- iteration = : 34 grad.norm() = 0.0641619 pflug_sum = 0.913809, max_pflug_sum = 2.38114 --------------------------- iteration = : 35 grad.norm() = 0.0594323 pflug_sum = 0.893458, max_pflug_sum = 2.38114 --------------------------- iteration = : 36 grad.norm() = 0.145093 pflug_sum = 0.931935, max_pflug_sum = 2.38114 --------------------------- iteration = : 37 grad.norm() = 0.107809 pflug_sum = 0.222869, max_pflug_sum = 2.38114 --------------------------- iteration = : 38 grad.norm() = 0.0129799 pflug_sum = 0.259316, max_pflug_sum = 2.38114 --------------------------- iteration = : 39 grad.norm() = 0.0728965 pflug_sum = 0.455058, max_pflug_sum = 2.38114 --------------------------- iteration = : 40 grad.norm() = 0.115613 pflug_sum = 0.87038, max_pflug_sum = 2.38114 --------------------------- iteration = : 41 grad.norm() = 0.098147 pflug_sum = 0.356254, max_pflug_sum = 2.38114 --------------------------- iteration = : 42 grad.norm() = 0.0668247 pflug_sum = 0.755583, max_pflug_sum = 2.38114 --------------------------- iteration = : 43 grad.norm() = 0.0372625 pflug_sum = 0.853734, max_pflug_sum = 2.38114 --------------------------- iteration = : 44 grad.norm() = 0.0407081 pflug_sum = 0.804407, max_pflug_sum = 2.38114 --------------------------- iteration = : 45 grad.norm() = 0.0942283 pflug_sum = 0.760912, max_pflug_sum = 2.38114 --------------------------- iteration = : 46 grad.norm() = 0.0149191 pflug_sum = 0.714733, max_pflug_sum = 2.38114 --------------------------- iteration = : 47 grad.norm() = 0.0359073 pflug_sum = 0.702006, max_pflug_sum = 2.38114 --------------------------- iteration = : 48 grad.norm() = 0.0191373 pflug_sum = 1.01059, max_pflug_sum = 2.38114 --------------------------- iteration = : 49 grad.norm() = 0.0152963 pflug_sum = 1.92279, max_pflug_sum = 2.38114 --------------------------- iteration = : 50 grad.norm() = 0.0217406 pflug_sum = 1.6314, max_pflug_sum = 2.38114 --------------------------- iteration = : 51 grad.norm() = 0.0862144 pflug_sum = 1.55125, max_pflug_sum = 2.38114 --------------------------- iteration = : 52 grad.norm() = 0.00928228 pflug_sum = 1.73334, max_pflug_sum = 2.38114 --------------------------- iteration = : 53 grad.norm() = 0.161511 pflug_sum = 1.52085, max_pflug_sum = 2.38114 --------------------------- iteration = : 54 grad.norm() = 0.0528296 pflug_sum = 1.02017, max_pflug_sum = 2.38114 --------------------------- iteration = : 55 grad.norm() = 0.0145021 pflug_sum = 0.385458, max_pflug_sum = 2.38114 --------------------------- iteration = : 56 grad.norm() = 0.00731658 pflug_sum = 0.380611, max_pflug_sum = 2.38114 --------------------------- iteration = : 57 grad.norm() = 0.0690521 pflug_sum = 0.337781, max_pflug_sum = 2.38114 --------------------------- iteration = : 58 grad.norm() = 0.00808464 pflug_sum = 0.334586, max_pflug_sum = 2.38114 --------------------------- iteration = : 59 grad.norm() = 0.0474425 pflug_sum = 0.667341, max_pflug_sum = 2.38114 --------------------------- iteration = : 60 grad.norm() = 0.102719 pflug_sum = 0.41409, max_pflug_sum = 2.38114 --------------------------- Pflug diagnostic satisfied: pflug_sum < 0.9 * max_pflug_sum for all chains. Starting posterior sampling... Posterior sampling done. Average standard deviation of the posterior W: 2.12810570532415 Use ngme_post_samples() to access posterior samples and ngme_result() to access latent model parameters. [1] 0.238408 5 x 5 sparse Matrix of class "dgCMatrix" [1,] 0.8660254 . . . . [2,] -0.5000000 1.0 . . . [3,] . -0.5 1.0 . . [4,] . . -0.5 1.0 . [5,] . . . -0.5 1 [1] 0.238408 [1] 0.1623737 [1] 0.1623737 [1] "rho" "c1" "c2" "rho (1st)" "rho (2nd)" "sigma_1" [7] "sigma_2" "sigma_1" Starting estimation... iteration = : 1 grad.norm() = 74.2717 pflug_sum = 0, max_pflug_sum = 0 --------------------------- iteration = : 2 grad.norm() = 71.5626 pflug_sum = 5315.07, max_pflug_sum = 5315.07 --------------------------- iteration = : 3 grad.norm() = 68.5594 pflug_sum = 10210.6, max_pflug_sum = 10210.6 --------------------------- iteration = : 4 grad.norm() = 65.2844 pflug_sum = 14679.6, max_pflug_sum = 14679.6 --------------------------- iteration = : 5 grad.norm() = 61.6779 pflug_sum = 18696, max_pflug_sum = 18696 --------------------------- iteration = : 6 grad.norm() = 57.73 pflug_sum = 22256, max_pflug_sum = 22256 --------------------------- iteration = : 7 grad.norm() = 53.3367 pflug_sum = 25333.5, max_pflug_sum = 25333.5 --------------------------- iteration = : 8 grad.norm() = 48.5564 pflug_sum = 27916.7, max_pflug_sum = 27916.7 --------------------------- iteration = : 9 grad.norm() = 43.4953 pflug_sum = 30023.1, max_pflug_sum = 30023.1 --------------------------- iteration = : 10 grad.norm() = 37.9443 pflug_sum = 31673.2, max_pflug_sum = 31673.2 --------------------------- Starting posterior sampling... Posterior sampling done. Average standard deviation of the posterior W: NA Use ngme_post_samples() to access posterior samples and ngme_result() to access latent model parameters. [ FAIL 1 | WARN 0 | SKIP 10 | PASS 395 ] ══ Skipped tests (10) ══════════════════════════════════════════════════════════ • On CRAN (2): 'test-compose-sum-ar1-matern.R:2:3', 'test-regression-fe-rank-check.R:25:3' • empty test (7): 'test-compose-bv.R:1:1', 'test-compose-bv.R:35:1', 'test-compose-bv.R:110:1', 'test-compose-bv.R:175:1', 'test-core-model-defs.R:20:1', 'test-core-model-defs.R:54:1', 'test-core-model-defs.R:77:1' • {INLA} is not installed. (1): 'test-core-fractional-model.R:77:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-compose-tp-bv-matern.R:87:3'): tp-bv-matern operator structure and simulation ── Expected `rho_hat > 0.35 && rho_hat < 0.9` to be TRUE. Differences: `actual`: FALSE `expected`: TRUE [ FAIL 1 | WARN 0 | SKIP 10 | PASS 395 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-debian-gcc

Version: 1.0.0
Check: whether package can be installed
Result: WARN Found the following significant warnings: block.cpp:2418:11: warning: ignoring return value of function declared with 'nodiscard' attribute [-Wunused-result] See ‘/data/localhost/ripley/R/packages/tests-clang/ngme2.Rcheck/00install.out’ for details. * used C++ compiler: ‘clang version 23.1.2’ Flavor: r-devel-linux-x86_64-fedora-clang

Version: 1.0.0
Check: tests
Result: ERROR Running ‘testthat.R’ [89m/77m] 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/tests.html > # * https://testthat.r-lib.org/reference/test_package.html#special-files > > library(testthat) > > test_check("ngme2") Loading required package: ngme2 This is ngme2 of version 1.0.0 - See our homepage: https://davidbolin.github.io/ngme2 for more details. Attaching package: 'ngme2' The following object is masked from 'package:stats': ar OMP: Warning #96: Cannot form a team with 4 threads, using 2 instead. OMP: Hint Consider unsetting KMP_DEVICE_THREAD_LIMIT (KMP_ALL_THREADS), KMP_TEAMS_THREAD_LIMIT, and OMP_THREAD_LIMIT (if any are set). stop 1: ------------------------------------------------------------------------------------------------------------------------------------ Param: theta rho rho (1st) rho (2nd) mu_1 mu_2 sigma_1 sigma_2 nu_1 nu_2 meas_sigma_1 ------------------------------------------------------------------------------------------------------------------------------------ R_hat: 1.610 2.109 2.418 4.942 2.662 3.310 2.546 2.135 3.053 2.875 1.637 ------------------------------------------------------------------------------------------------------------------------------------ List of 6 $ mean : num [1:3] -1.225 0.652 -3.262 $ sd : num [1:3] 0.539 0.763 0.96 $ 0.05q : num [1:3] -2.106 -0.435 -4.826 $ 0.95q : num [1:3] -0.344 2.012 -1.679 $ median: num [1:3] -1.244 0.602 -3.213 $ mode : num [1:3] -1.3 0.5 -3.1 - attr(*, "samples")= num [1:10, 1:500] -2.799 -2.62 -2.631 -0.233 2.22 ... Starting estimation... iteration = : 1 grad.norm() = 1.4118 --------------------------- iteration = : 2 grad.norm() = 0.408099 --------------------------- iteration = : 3 grad.norm() = 0.249287 --------------------------- iteration = : 4 grad.norm() = 0.110781 --------------------------- iteration = : 5 grad.norm() = 0.0911561 --------------------------- iteration = : 6 grad.norm() = 0.105111 --------------------------- iteration = : 7 grad.norm() = 0.133394 --------------------------- iteration = : 8 grad.norm() = 0.065317 --------------------------- iteration = : 9 grad.norm() = 0.051223 --------------------------- iteration = : 10 grad.norm() = 0.0793906 --------------------------- [iter 10] 0/11 converged worst: R_hat 9.202 (nu_1), drift/100 0.00% theta: theta=0.0686 rho=0.7018 kappa (1st)=0.0120 kappa (2nd)=0.5105 mu_1=0.2150 mu_2=0.2130 sigma_1=-0.1870 sigma_2=0.1264 nu_1=0.0336 nu_2=0.1899 meas_sigma_1=-0.6818 Starting posterior sampling... Posterior sampling done. Average standard deviation of the posterior W: 0.239583621615535 Use ngme_post_samples() to access posterior samples and ngme_result() to access latent model parameters. Flavor: r-devel-linux-x86_64-fedora-clang

Version: 0.9.8
Check: tests
Result: ERROR Running ‘testthat.R’ [90m/49m] 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/tests.html > # * https://testthat.r-lib.org/reference/test_package.html#special-files > > library(testthat) > > test_check("ngme2") Loading required package: ngme2 This is ngme2 of version 0.9.8 - See our homepage: https://davidbolin.github.io/ngme2 for more details. Attaching package: 'ngme2' The following object is masked from 'package:stats': ar List of 6 $ mean : num [1:3] -1.872 0.836 -1.725 $ sd : num [1:3] 0.86 0.921 0.907 $ 0.05q : num [1:3] -3.285 -0.719 -3.227 $ 0.95q : num [1:3] -0.452 2.426 -0.287 $ median: num [1:3] -1.85 0.865 -1.682 $ mode : num [1:3] -2.1 0.9 -1.3 - attr(*, "samples")= num [1:10, 1:500] -1.19 -1.265 -0.521 -0.154 1.072 ... Starting estimation... iteration = : 1 grad.norm() = 492.545 pflug_sum = 0, max_pflug_sum = 0 --------------------------- iteration = : 2 grad.norm() = 471.892 pflug_sum = 231193, max_pflug_sum = 231193 --------------------------- iteration = : 3 grad.norm() = 448.473 pflug_sum = 442264, max_pflug_sum = 442264 --------------------------- iteration = : 4 grad.norm() = 430.687 pflug_sum = 634965, max_pflug_sum = 634965 --------------------------- iteration = : 5 grad.norm() = 401.221 pflug_sum = 805904, max_pflug_sum = 805904 --------------------------- iteration = : 6 grad.norm() = 365.07 pflug_sum = 951221, max_pflug_sum = 951221 --------------------------- iteration = : 7 grad.norm() = 342.534 pflug_sum = 1.07546e+06, max_pflug_sum = 1.07546e+06 --------------------------- iteration = : 8 grad.norm() = 305.929 pflug_sum = 1.17938e+06, max_pflug_sum = 1.17938e+06 --------------------------- iteration = : 9 grad.norm() = 271.637 pflug_sum = 1.26182e+06, max_pflug_sum = 1.26182e+06 --------------------------- iteration = : 10 grad.norm() = 231.634 pflug_sum = 1.32447e+06, max_pflug_sum = 1.32447e+06 --------------------------- Starting posterior sampling... Posterior sampling done. Average standard deviation of the posterior W: 0.271198078235015 Use ngme_post_samples() to access posterior samples and ngme_result() to access latent model parameters. Flavor: r-devel-linux-x86_64-fedora-gcc