CRAN Package Check Results for Package ngme2

Last updated on 2026-05-27 13:54:40 CEST.

Flavor	Version	T_install	T_check	T_total	Status
r-devel-linux-x86_64-debian-clang	0.9.8	478.33	364.43	842.76	ERROR
r-devel-linux-x86_64-debian-gcc	0.9.8	361.53	258.47	620.00	OK
r-devel-linux-x86_64-fedora-clang	0.9.8	660.00	5643.45	6303.45	ERROR
r-devel-linux-x86_64-fedora-gcc	0.9.8	1073.00	5691.16	6764.16	ERROR
r-devel-windows-x86_64	0.9.8	623.00	383.00	1006.00	OK
r-patched-linux-x86_64	0.9.7	488.54	348.31	836.85	OK
r-release-linux-x86_64	0.9.8	927.74	364.17	1291.91	OK
r-release-macos-arm64	0.9.8	97.00	24.00	121.00	OK
r-release-macos-x86_64	0.9.8	301.00	286.00	587.00	OK
r-release-windows-x86_64	0.9.8	647.00	388.00	1035.00	OK
r-oldrel-macos-arm64	0.9.8	87.00	30.00	117.00	OK
r-oldrel-macos-x86_64	0.9.8	295.00	211.00	506.00	OK
r-oldrel-windows-x86_64	0.9.8	705.00	511.00	1216.00	OK

Check Details

Version: 0.9.8
Check: tests
Result: ERROR Running ‘testthat.R’ [187s/234s] 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] -1.964 0.905 -1.802 $ sd : num [1:3] 0.872 0.927 0.933 $ 0.05q : num [1:3] -3.461 -0.651 -3.291 $ 0.95q : num [1:3] -0.589 2.387 -0.19 $ median: num [1:3] -1.925 0.883 -1.792 $ mode : num [1:3] -1.9 0.75 -1.7 - attr(*, "samples")= num [1:10, 1:500] -2.612 -2.688 -2.642 0.039 2.92 ... Starting estimation... iteration = : 1 grad.norm() = 503.748 pflug_sum = 0, max_pflug_sum = 0 --------------------------- iteration = : 2 grad.norm() = 487.779 pflug_sum = 244985, max_pflug_sum = 244985 --------------------------- iteration = : 3 grad.norm() = 474.997 pflug_sum = 475938, max_pflug_sum = 475938 --------------------------- iteration = : 4 grad.norm() = 443.87 pflug_sum = 686112, max_pflug_sum = 686112 --------------------------- iteration = : 5 grad.norm() = 425.383 pflug_sum = 874160, max_pflug_sum = 874160 --------------------------- iteration = : 6 grad.norm() = 394.672 pflug_sum = 1.04099e+06, max_pflug_sum = 1.04099e+06 --------------------------- iteration = : 7 grad.norm() = 354.62 pflug_sum = 1.18049e+06, max_pflug_sum = 1.18049e+06 --------------------------- iteration = : 8 grad.norm() = 334.368 pflug_sum = 1.29813e+06, max_pflug_sum = 1.29813e+06 --------------------------- iteration = : 9 grad.norm() = 301.911 pflug_sum = 1.3971e+06, max_pflug_sum = 1.3971e+06 --------------------------- iteration = : 10 grad.norm() = 268.661 pflug_sum = 1.47758e+06, max_pflug_sum = 1.47758e+06 --------------------------- Starting posterior sampling... Posterior sampling done. Average standard deviation of the posterior W: 0.209115349844821 Use ngme_post_samples() to access posterior samples and ngme_result() to access latent model parameters. Loading required package: Matrix This is rSPDE 2.5.2 - 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() = 0.033687 pflug_sum = 0, max_pflug_sum = 0 --------------------------- iteration = : 2 grad.norm() = 0.0448577 pflug_sum = 0.357712, max_pflug_sum = 0.357712 --------------------------- iteration = : 3 grad.norm() = 0.0436308 pflug_sum = 0.351297, max_pflug_sum = 0.357712 --------------------------- iteration = : 4 grad.norm() = 0.0692487 pflug_sum = 0.205166, max_pflug_sum = 0.357712 --------------------------- iteration = : 5 grad.norm() = 0.0261459 pflug_sum = 0.427534, max_pflug_sum = 0.427534 --------------------------- iteration = : 6 grad.norm() = 0.0675631 pflug_sum = 0.406541, max_pflug_sum = 0.427534 --------------------------- iteration = : 7 grad.norm() = 0.00499671 pflug_sum = 0.437338, max_pflug_sum = 0.437338 --------------------------- iteration = : 8 grad.norm() = 0.0309065 pflug_sum = 0.372797, max_pflug_sum = 0.437338 --------------------------- iteration = : 9 grad.norm() = 0.0615857 pflug_sum = 0.275958, max_pflug_sum = 0.437338 --------------------------- iteration = : 10 grad.norm() = 0.0911634 pflug_sum = 0.47777, max_pflug_sum = 0.47777 --------------------------- iteration = : 11 grad.norm() = 0.0178379 pflug_sum = 0.355596, max_pflug_sum = 0.47777 --------------------------- iteration = : 12 grad.norm() = 0.0446916 pflug_sum = 0.482034, max_pflug_sum = 0.482034 --------------------------- iteration = : 13 grad.norm() = 0.0233199 pflug_sum = 0.661732, max_pflug_sum = 0.661732 --------------------------- iteration = : 14 grad.norm() = 0.0235655 pflug_sum = 0.494999, max_pflug_sum = 0.661732 --------------------------- iteration = : 15 grad.norm() = 0.051559 pflug_sum = 0.49986, max_pflug_sum = 0.661732 --------------------------- iteration = : 16 grad.norm() = 0.00682711 pflug_sum = 0.53974, max_pflug_sum = 0.661732 --------------------------- iteration = : 17 grad.norm() = 0.104886 pflug_sum = 0.534622, max_pflug_sum = 0.661732 --------------------------- iteration = : 18 grad.norm() = 0.0330995 pflug_sum = 0.726885, max_pflug_sum = 0.726885 --------------------------- iteration = : 19 grad.norm() = 0.0294236 pflug_sum = 0.736227, max_pflug_sum = 0.736227 --------------------------- iteration = : 20 grad.norm() = 0.0336357 pflug_sum = 0.633364, max_pflug_sum = 0.736227 --------------------------- iteration = : 21 grad.norm() = 0.0272883 pflug_sum = 0.842784, max_pflug_sum = 0.842784 --------------------------- iteration = : 22 grad.norm() = 0.114779 pflug_sum = 0.869074, max_pflug_sum = 0.869074 --------------------------- iteration = : 23 grad.norm() = 0.0283572 pflug_sum = 0.659336, max_pflug_sum = 0.869074 --------------------------- iteration = : 24 grad.norm() = 0.118025 pflug_sum = 0.41296, max_pflug_sum = 0.869074 --------------------------- iteration = : 25 grad.norm() = 0.0292575 pflug_sum = 0.399437, max_pflug_sum = 0.869074 --------------------------- iteration = : 26 grad.norm() = 0.0998668 pflug_sum = -0.078802, max_pflug_sum = 0.869074 --------------------------- iteration = : 27 grad.norm() = 0.0242386 pflug_sum = -0.233533, max_pflug_sum = 0.869074 --------------------------- iteration = : 28 grad.norm() = 0.0197117 pflug_sum = -0.149607, max_pflug_sum = 0.869074 --------------------------- iteration = : 29 grad.norm() = 0.0306393 pflug_sum = 0.0631361, max_pflug_sum = 0.869074 --------------------------- iteration = : 30 grad.norm() = 0.0606735 pflug_sum = -0.198506, max_pflug_sum = 0.869074 --------------------------- iteration = : 31 grad.norm() = 0.0589794 pflug_sum = 0.145241, max_pflug_sum = 0.869074 --------------------------- iteration = : 32 grad.norm() = 0.0967469 pflug_sum = 0.100279, max_pflug_sum = 0.869074 --------------------------- iteration = : 33 grad.norm() = 0.0692006 pflug_sum = -0.064308, max_pflug_sum = 0.869074 --------------------------- iteration = : 34 grad.norm() = 0.102385 pflug_sum = -0.522148, max_pflug_sum = 0.869074 --------------------------- iteration = : 35 grad.norm() = 0.0972841 pflug_sum = 0.284504, max_pflug_sum = 0.869074 --------------------------- iteration = : 36 grad.norm() = 0.052664 pflug_sum = 0.141034, max_pflug_sum = 0.869074 --------------------------- iteration = : 37 grad.norm() = 0.00928754 pflug_sum = 0.387309, max_pflug_sum = 0.869074 --------------------------- iteration = : 38 grad.norm() = 0.049938 pflug_sum = 0.224268, max_pflug_sum = 0.869074 --------------------------- iteration = : 39 grad.norm() = 0.0271499 pflug_sum = -0.0176644, max_pflug_sum = 0.869074 --------------------------- iteration = : 40 grad.norm() = 0.0467727 pflug_sum = 0.177961, max_pflug_sum = 0.869074 --------------------------- iteration = : 41 grad.norm() = 0.0264556 pflug_sum = 0.0743614, max_pflug_sum = 0.869074 --------------------------- iteration = : 42 grad.norm() = 0.137035 pflug_sum = 0.0666992, max_pflug_sum = 0.869074 --------------------------- iteration = : 43 grad.norm() = 0.152733 pflug_sum = -0.580689, max_pflug_sum = 0.869074 --------------------------- iteration = : 44 grad.norm() = 0.0320561 pflug_sum = -0.469935, max_pflug_sum = 0.869074 --------------------------- iteration = : 45 grad.norm() = 0.0969837 pflug_sum = -0.576034, max_pflug_sum = 0.869074 --------------------------- iteration = : 46 grad.norm() = 0.0202461 pflug_sum = -1.01754, max_pflug_sum = 0.869074 --------------------------- iteration = : 47 grad.norm() = 0.0619695 pflug_sum = -0.945518, max_pflug_sum = 0.869074 --------------------------- iteration = : 48 grad.norm() = 0.0532465 pflug_sum = -1.09207, max_pflug_sum = 0.869074 --------------------------- iteration = : 49 grad.norm() = 0.0367268 pflug_sum = -0.767379, max_pflug_sum = 0.869074 --------------------------- iteration = : 50 grad.norm() = 0.0330081 pflug_sum = -0.872155, max_pflug_sum = 0.869074 --------------------------- iteration = : 51 grad.norm() = 0.0432963 pflug_sum = -1.35804, max_pflug_sum = 0.869074 --------------------------- iteration = : 52 grad.norm() = 0.0413313 pflug_sum = -1.6755, max_pflug_sum = 0.869074 --------------------------- iteration = : 53 grad.norm() = 0.0236745 pflug_sum = -1.72769, max_pflug_sum = 0.869074 --------------------------- iteration = : 54 grad.norm() = 0.115194 pflug_sum = -1.48102, max_pflug_sum = 0.869074 --------------------------- iteration = : 55 grad.norm() = 0.0374376 pflug_sum = -1.41817, max_pflug_sum = 0.869074 --------------------------- iteration = : 56 grad.norm() = 0.0548784 pflug_sum = -1.47194, max_pflug_sum = 0.869074 --------------------------- iteration = : 57 grad.norm() = 0.154563 pflug_sum = -1.42094, max_pflug_sum = 0.869074 --------------------------- iteration = : 58 grad.norm() = 0.0756581 pflug_sum = -0.667886, max_pflug_sum = 0.869074 --------------------------- iteration = : 59 grad.norm() = 0.0670823 pflug_sum = -0.797343, max_pflug_sum = 0.869074 --------------------------- iteration = : 60 grad.norm() = 0.0191174 pflug_sum = -0.892624, max_pflug_sum = 0.869074 --------------------------- iteration = : 61 grad.norm() = 0.142708 pflug_sum = -0.896331, max_pflug_sum = 0.869074 --------------------------- iteration = : 62 grad.norm() = 0.0756593 pflug_sum = -1.52016, max_pflug_sum = 0.869074 --------------------------- iteration = : 63 grad.norm() = 0.0831958 pflug_sum = -1.76325, max_pflug_sum = 0.869074 --------------------------- iteration = : 64 grad.norm() = 0.044145 pflug_sum = -2.12683, max_pflug_sum = 0.869074 --------------------------- iteration = : 65 grad.norm() = 0.039741 pflug_sum = -1.8384, max_pflug_sum = 0.869074 --------------------------- iteration = : 66 grad.norm() = 0.0639826 pflug_sum = -1.89261, max_pflug_sum = 0.869074 --------------------------- iteration = : 67 grad.norm() = 0.0924966 pflug_sum = -1.73583, max_pflug_sum = 0.869074 --------------------------- iteration = : 68 grad.norm() = 0.0676307 pflug_sum = -1.50462, max_pflug_sum = 0.869074 --------------------------- iteration = : 69 grad.norm() = 0.0664996 pflug_sum = -1.35009, max_pflug_sum = 0.869074 --------------------------- iteration = : 70 grad.norm() = 0.0989253 pflug_sum = -1.28263, max_pflug_sum = 0.869074 --------------------------- iteration = : 71 grad.norm() = 0.0302758 pflug_sum = -0.836072, max_pflug_sum = 0.869074 --------------------------- iteration = : 72 grad.norm() = 0.0623475 pflug_sum = -0.50048, max_pflug_sum = 0.869074 --------------------------- iteration = : 73 grad.norm() = 0.0403199 pflug_sum = -0.701895, max_pflug_sum = 0.869074 --------------------------- iteration = : 74 grad.norm() = 0.0710187 pflug_sum = -0.728296, max_pflug_sum = 0.869074 --------------------------- iteration = : 75 grad.norm() = 0.0503872 pflug_sum = -0.751935, max_pflug_sum = 0.869074 --------------------------- iteration = : 76 grad.norm() = 0.107617 pflug_sum = -1.25155, max_pflug_sum = 0.869074 --------------------------- iteration = : 77 grad.norm() = 0.030489 pflug_sum = -1.30831, max_pflug_sum = 0.869074 --------------------------- iteration = : 78 grad.norm() = 0.0600846 pflug_sum = -1.22007, max_pflug_sum = 0.869074 --------------------------- iteration = : 79 grad.norm() = 0.0895742 pflug_sum = -1.10132, max_pflug_sum = 0.869074 --------------------------- iteration = : 80 grad.norm() = 0.0226872 pflug_sum = -0.652502, max_pflug_sum = 0.869074 --------------------------- iteration = : 81 grad.norm() = 0.0490271 pflug_sum = -1.02201, max_pflug_sum = 0.869074 --------------------------- iteration = : 82 grad.norm() = 0.0328312 pflug_sum = -1.09878, max_pflug_sum = 0.869074 --------------------------- iteration = : 83 grad.norm() = 0.0616144 pflug_sum = -1.1989, max_pflug_sum = 0.869074 --------------------------- iteration = : 84 grad.norm() = 0.063719 pflug_sum = -1.49478, max_pflug_sum = 0.869074 --------------------------- iteration = : 85 grad.norm() = 0.0222299 pflug_sum = -1.44666, max_pflug_sum = 0.869074 --------------------------- iteration = : 86 grad.norm() = 0.0619331 pflug_sum = -1.54761, max_pflug_sum = 0.869074 --------------------------- iteration = : 87 grad.norm() = 0.0754766 pflug_sum = -1.33842, max_pflug_sum = 0.869074 --------------------------- iteration = : 88 grad.norm() = 0.0529381 pflug_sum = -1.53888, max_pflug_sum = 0.869074 --------------------------- iteration = : 89 grad.norm() = 0.0108828 pflug_sum = -1.44961, max_pflug_sum = 0.869074 --------------------------- iteration = : 90 grad.norm() = 0.070556 pflug_sum = -1.83578, max_pflug_sum = 0.869074 --------------------------- iteration = : 91 grad.norm() = 0.0346722 pflug_sum = -1.86977, max_pflug_sum = 0.869074 --------------------------- iteration = : 92 grad.norm() = 0.0496992 pflug_sum = -1.80019, max_pflug_sum = 0.869074 --------------------------- iteration = : 93 grad.norm() = 0.0866998 pflug_sum = -1.47604, max_pflug_sum = 0.869074 --------------------------- iteration = : 94 grad.norm() = 0.0617506 pflug_sum = -1.81954, max_pflug_sum = 0.869074 --------------------------- iteration = : 95 grad.norm() = 0.0462483 pflug_sum = -1.90309, max_pflug_sum = 0.869074 --------------------------- iteration = : 96 grad.norm() = 0.0145283 pflug_sum = -1.97972, max_pflug_sum = 0.869074 --------------------------- iteration = : 97 grad.norm() = 0.0909327 pflug_sum = -2.02052, max_pflug_sum = 0.869074 --------------------------- iteration = : 98 grad.norm() = 0.049256 pflug_sum = -2.41148, max_pflug_sum = 0.869074 --------------------------- iteration = : 99 grad.norm() = 0.037749 pflug_sum = -2.4757, max_pflug_sum = 0.869074 --------------------------- iteration = : 100 grad.norm() = 0.0532104 pflug_sum = -2.33087, max_pflug_sum = 0.869074 --------------------------- iteration = : 101 grad.norm() = 0.0135627 pflug_sum = -2.1439, max_pflug_sum = 0.869074 --------------------------- iteration = : 102 grad.norm() = 0.0641813 pflug_sum = -2.45092, max_pflug_sum = 0.869074 --------------------------- iteration = : 103 grad.norm() = 0.00727037 pflug_sum = -2.72525, max_pflug_sum = 0.869074 --------------------------- iteration = : 104 grad.norm() = 0.0354745 pflug_sum = -2.94468, max_pflug_sum = 0.869074 --------------------------- iteration = : 105 grad.norm() = 0.0583461 pflug_sum = -3.20851, max_pflug_sum = 0.869074 --------------------------- iteration = : 106 grad.norm() = 0.0595233 pflug_sum = -3.01393, max_pflug_sum = 0.869074 --------------------------- iteration = : 107 grad.norm() = 0.196239 pflug_sum = -2.9086, max_pflug_sum = 0.869074 --------------------------- iteration = : 108 grad.norm() = 0.0156092 pflug_sum = -3.06981, max_pflug_sum = 0.869074 --------------------------- iteration = : 109 grad.norm() = 0.0815671 pflug_sum = -3.34383, max_pflug_sum = 0.869074 --------------------------- iteration = : 110 grad.norm() = 0.0172926 pflug_sum = -3.85565, max_pflug_sum = 0.869074 --------------------------- iteration = : 111 grad.norm() = 0.0190409 pflug_sum = -3.66434, max_pflug_sum = 0.869074 --------------------------- iteration = : 112 grad.norm() = 0.0191043 pflug_sum = -3.76739, max_pflug_sum = 0.869074 --------------------------- iteration = : 113 grad.norm() = 0.0603456 pflug_sum = -3.70182, max_pflug_sum = 0.869074 --------------------------- iteration = : 114 grad.norm() = 0.125454 pflug_sum = -3.94118, max_pflug_sum = 0.869074 --------------------------- iteration = : 115 grad.norm() = 0.0330033 pflug_sum = -3.95577, max_pflug_sum = 0.869074 --------------------------- iteration = : 116 grad.norm() = 0.0527977 pflug_sum = -3.87961, max_pflug_sum = 0.869074 --------------------------- iteration = : 117 grad.norm() = 0.0839402 pflug_sum = -4.26422, max_pflug_sum = 0.869074 --------------------------- iteration = : 118 grad.norm() = 0.055376 pflug_sum = -3.74731, max_pflug_sum = 0.869074 --------------------------- iteration = : 119 grad.norm() = 0.0847294 pflug_sum = -4.38384, max_pflug_sum = 0.869074 --------------------------- iteration = : 120 grad.norm() = 0.0279504 pflug_sum = -4.2824, max_pflug_sum = 0.869074 --------------------------- iteration = : 121 grad.norm() = 0.0214413 pflug_sum = -4.3351, max_pflug_sum = 0.869074 --------------------------- iteration = : 122 grad.norm() = 0.0513782 pflug_sum = -4.17003, max_pflug_sum = 0.869074 --------------------------- iteration = : 123 grad.norm() = 0.0580797 pflug_sum = -4.06255, max_pflug_sum = 0.869074 --------------------------- iteration = : 124 grad.norm() = 0.0246871 pflug_sum = -4.05474, max_pflug_sum = 0.869074 --------------------------- iteration = : 125 grad.norm() = 0.0221298 pflug_sum = -4.8808, max_pflug_sum = 0.869074 --------------------------- iteration = : 126 grad.norm() = 0.0715647 pflug_sum = -4.39536, max_pflug_sum = 0.869074 --------------------------- iteration = : 127 grad.norm() = 0.0186252 pflug_sum = -4.13575, max_pflug_sum = 0.869074 --------------------------- iteration = : 128 grad.norm() = 0.0231255 pflug_sum = -4.23195, max_pflug_sum = 0.869074 --------------------------- iteration = : 129 grad.norm() = 0.0257311 pflug_sum = -4.19586, max_pflug_sum = 0.869074 --------------------------- iteration = : 130 grad.norm() = 0.100603 pflug_sum = -3.93589, max_pflug_sum = 0.869074 --------------------------- iteration = : 131 grad.norm() = 0.0878142 pflug_sum = -4.17302, max_pflug_sum = 0.869074 --------------------------- iteration = : 132 grad.norm() = 0.136762 pflug_sum = -4.52416, max_pflug_sum = 0.869074 --------------------------- iteration = : 133 grad.norm() = 0.0258945 pflug_sum = -5.10125, max_pflug_sum = 0.869074 --------------------------- iteration = : 134 grad.norm() = 0.0694979 pflug_sum = -5.63424, max_pflug_sum = 0.869074 --------------------------- iteration = : 135 grad.norm() = 0.103102 pflug_sum = -6.02637, max_pflug_sum = 0.869074 --------------------------- iteration = : 136 grad.norm() = 0.0880952 pflug_sum = -6.27777, max_pflug_sum = 0.869074 --------------------------- iteration = : 137 grad.norm() = 0.0667027 pflug_sum = -6.50947, max_pflug_sum = 0.869074 --------------------------- iteration = : 138 grad.norm() = 0.0568441 pflug_sum = -6.77095, max_pflug_sum = 0.869074 --------------------------- iteration = : 139 grad.norm() = 0.0681556 pflug_sum = -7.10361, max_pflug_sum = 0.869074 --------------------------- iteration = : 140 grad.norm() = 0.0383865 pflug_sum = -7.33061, max_pflug_sum = 0.869074 --------------------------- iteration = : 141 grad.norm() = 0.0458047 pflug_sum = -7.52526, max_pflug_sum = 0.869074 --------------------------- iteration = : 142 grad.norm() = 0.0389108 pflug_sum = -7.7035, max_pflug_sum = 0.869074 --------------------------- iteration = : 143 grad.norm() = 0.0247602 pflug_sum = -7.68518, max_pflug_sum = 0.869074 --------------------------- iteration = : 144 grad.norm() = 0.0284392 pflug_sum = -7.67022, max_pflug_sum = 0.869074 --------------------------- iteration = : 145 grad.norm() = 0.038673 pflug_sum = -8.264, max_pflug_sum = 0.869074 --------------------------- iteration = : 146 grad.norm() = 0.0104257 pflug_sum = -8.22173, max_pflug_sum = 0.869074 --------------------------- iteration = : 147 grad.norm() = 0.0165508 pflug_sum = -8.25837, max_pflug_sum = 0.869074 --------------------------- iteration = : 148 grad.norm() = 0.0225249 pflug_sum = -8.29137, max_pflug_sum = 0.869074 --------------------------- iteration = : 149 grad.norm() = 0.0756559 pflug_sum = -8.35161, max_pflug_sum = 0.869074 --------------------------- iteration = : 150 grad.norm() = 0.0754312 pflug_sum = -8.34791, max_pflug_sum = 0.869074 --------------------------- iteration = : 151 grad.norm() = 0.150869 pflug_sum = -9.0858, max_pflug_sum = 0.869074 --------------------------- iteration = : 152 grad.norm() = 0.0127474 pflug_sum = -8.91762, max_pflug_sum = 0.869074 --------------------------- iteration = : 153 grad.norm() = 0.0351009 pflug_sum = -9.11596, max_pflug_sum = 0.869074 --------------------------- iteration = : 154 grad.norm() = 0.0166626 pflug_sum = -9.50734, max_pflug_sum = 0.869074 --------------------------- iteration = : 155 grad.norm() = 0.0678907 pflug_sum = -9.51986, max_pflug_sum = 0.869074 --------------------------- iteration = : 156 grad.norm() = 0.0652667 pflug_sum = -9.59088, max_pflug_sum = 0.869074 --------------------------- iteration = : 157 grad.norm() = 0.0387739 pflug_sum = -9.82017, max_pflug_sum = 0.869074 --------------------------- iteration = : 158 grad.norm() = 0.0955924 pflug_sum = -9.47151, max_pflug_sum = 0.869074 --------------------------- iteration = : 159 grad.norm() = 0.096038 pflug_sum = -10.0582, max_pflug_sum = 0.869074 --------------------------- iteration = : 160 grad.norm() = 0.0294542 pflug_sum = -10.1699, max_pflug_sum = 0.869074 --------------------------- iteration = : 161 grad.norm() = 0.0109726 pflug_sum = -10.0917, max_pflug_sum = 0.869074 --------------------------- iteration = : 162 grad.norm() = 0.0782881 pflug_sum = -10.2812, max_pflug_sum = 0.869074 --------------------------- iteration = : 163 grad.norm() = 0.0387253 pflug_sum = -10.6485, max_pflug_sum = 0.869074 --------------------------- iteration = : 164 grad.norm() = 0.0516625 pflug_sum = -10.5357, max_pflug_sum = 0.869074 --------------------------- iteration = : 165 grad.norm() = 0.0200451 pflug_sum = -10.5933, max_pflug_sum = 0.869074 --------------------------- iteration = : 166 grad.norm() = 0.00509654 pflug_sum = -11.1168, max_pflug_sum = 0.869074 --------------------------- iteration = : 167 grad.norm() = 0.0630701 pflug_sum = -11.3081, max_pflug_sum = 0.869074 --------------------------- iteration = : 168 grad.norm() = 0.0146911 pflug_sum = -11.4854, max_pflug_sum = 0.869074 --------------------------- iteration = : 169 grad.norm() = 0.0905737 pflug_sum = -11.4528, max_pflug_sum = 0.869074 --------------------------- iteration = : 170 grad.norm() = 0.0886336 pflug_sum = -11.7959, max_pflug_sum = 0.869074 --------------------------- iteration = : 171 grad.norm() = 0.0401049 pflug_sum = -12.1545, max_pflug_sum = 0.869074 --------------------------- iteration = : 172 grad.norm() = 0.0110225 pflug_sum = -12.1227, max_pflug_sum = 0.869074 --------------------------- iteration = : 173 grad.norm() = 0.0288623 pflug_sum = -12.1271, max_pflug_sum = 0.869074 --------------------------- iteration = : 174 grad.norm() = 0.0190451 pflug_sum = -12.0605, max_pflug_sum = 0.869074 --------------------------- iteration = : 175 grad.norm() = 0.0207563 pflug_sum = -12.0694, max_pflug_sum = 0.869074 --------------------------- iteration = : 176 grad.norm() = 0.0210178 pflug_sum = -12.0402, max_pflug_sum = 0.869074 --------------------------- iteration = : 177 grad.norm() = 0.0958455 pflug_sum = -12.0386, max_pflug_sum = 0.869074 --------------------------- iteration = : 178 grad.norm() = 0.0457676 pflug_sum = -11.9236, max_pflug_sum = 0.869074 --------------------------- iteration = : 179 grad.norm() = 0.0561522 pflug_sum = -12.1573, max_pflug_sum = 0.869074 --------------------------- iteration = : 180 grad.norm() = 0.0588364 pflug_sum = -12.3329, max_pflug_sum = 0.869074 --------------------------- iteration = : 181 grad.norm() = 0.0817607 pflug_sum = -12.7958, max_pflug_sum = 0.869074 --------------------------- iteration = : 182 grad.norm() = 0.0532944 pflug_sum = -12.7554, max_pflug_sum = 0.869074 --------------------------- iteration = : 183 grad.norm() = 0.172878 pflug_sum = -13.1288, max_pflug_sum = 0.869074 --------------------------- iteration = : 184 grad.norm() = 0.0117178 pflug_sum = -13.1397, max_pflug_sum = 0.869074 --------------------------- iteration = : 185 grad.norm() = 0.0526353 pflug_sum = -13.26, max_pflug_sum = 0.869074 --------------------------- iteration = : 186 grad.norm() = 0.033221 pflug_sum = -13.0998, max_pflug_sum = 0.869074 --------------------------- iteration = : 187 grad.norm() = 0.0131843 pflug_sum = -13.5171, max_pflug_sum = 0.869074 --------------------------- iteration = : 188 grad.norm() = 0.0615938 pflug_sum = -13.6031, max_pflug_sum = 0.869074 --------------------------- iteration = : 189 grad.norm() = 0.0332645 pflug_sum = -13.5179, max_pflug_sum = 0.869074 --------------------------- iteration = : 190 grad.norm() = 0.0831637 pflug_sum = -13.8653, max_pflug_sum = 0.869074 --------------------------- iteration = : 191 grad.norm() = 0.0197154 pflug_sum = -13.7606, max_pflug_sum = 0.869074 --------------------------- iteration = : 192 grad.norm() = 0.0184798 pflug_sum = -13.8031, max_pflug_sum = 0.869074 --------------------------- iteration = : 193 grad.norm() = 0.00753577 pflug_sum = -13.6695, max_pflug_sum = 0.869074 --------------------------- iteration = : 194 grad.norm() = 0.0990474 pflug_sum = -13.5023, max_pflug_sum = 0.869074 --------------------------- iteration = : 195 grad.norm() = 0.0542527 pflug_sum = -13.86, max_pflug_sum = 0.869074 --------------------------- iteration = : 196 grad.norm() = 0.0323332 pflug_sum = -14.0909, max_pflug_sum = 0.869074 --------------------------- iteration = : 197 grad.norm() = 0.0501201 pflug_sum = -14.3234, max_pflug_sum = 0.869074 --------------------------- iteration = : 198 grad.norm() = 0.1408 pflug_sum = -14.6964, max_pflug_sum = 0.869074 --------------------------- iteration = : 199 grad.norm() = 0.0485816 pflug_sum = -14.6869, max_pflug_sum = 0.869074 --------------------------- iteration = : 200 grad.norm() = 0.0941041 pflug_sum = -15.2938, max_pflug_sum = 0.869074 --------------------------- Starting posterior sampling... Posterior sampling done. Average standard deviation of the posterior W: 2.34978365233785 Use ngme_post_samples() to access posterior samples and ngme_result() to access latent model parameters. [1] 0.319746 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] 0.319746 [QQ SPD fail] iter=51 min_diag=1.81405e+08 asym_norm=4.10364e+14 Saving _problems/test-core-generic-ns-401.R [1] 0.200067 [1] 0.200067 [1] "rho" "c1" "c2" "rho (1st)" "rho (2nd)" "sigma_1" [7] "sigma_2" "sigma_1" Starting estimation... iteration = : 1 grad.norm() = 74.7968 pflug_sum = 0, max_pflug_sum = 0 --------------------------- iteration = : 2 grad.norm() = 72.1429 pflug_sum = 5396.06, max_pflug_sum = 5396.06 --------------------------- iteration = : 3 grad.norm() = 69.1991 pflug_sum = 10377.5, max_pflug_sum = 10377.5 --------------------------- iteration = : 4 grad.norm() = 65.9925 pflug_sum = 14937.3, max_pflug_sum = 14937.3 --------------------------- iteration = : 5 grad.norm() = 62.4577 pflug_sum = 19048.7, max_pflug_sum = 19048.7 --------------------------- iteration = : 6 grad.norm() = 58.5884 pflug_sum = 22707.4, max_pflug_sum = 22707.4 --------------------------- iteration = : 7 grad.norm() = 54.2824 pflug_sum = 25886.1, max_pflug_sum = 25886.1 --------------------------- iteration = : 8 grad.norm() = 49.5932 pflug_sum = 28571.5, max_pflug_sum = 28571.5 --------------------------- iteration = : 9 grad.norm() = 44.6231 pflug_sum = 30778.9, max_pflug_sum = 30778.9 --------------------------- iteration = : 10 grad.norm() = 39.1702 pflug_sum = 32526.5, max_pflug_sum = 32526.5 --------------------------- 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 ════════════════════════════════════════════════════════════════ ── Error ('test-core-generic-ns.R:389:3'): generic model == Matern model (alpha == 2 or 4) ── Error: C++ estimation failed: C++ exception in parallel SGD step: Measurement precision QQ is not SPD Backtrace: ▆ 1. └─ngme2::ngme(...) at test-core-generic-ns.R:389:3 2. └─base::tryCatch(...) 3. └─base (local) tryCatchList(expr, classes, parentenv, handlers) 4. └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]]) 5. └─value[[3L]](cond) [ FAIL 1 | WARN 0 | SKIP 10 | PASS 395 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-debian-clang

Version: 0.9.8
Check: tests
Result: ERROR Running ‘testthat.R’ [90m/76m] 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] -1.699 0.639 -2.324 $ sd : num [1:3] 0.673 0.718 0.733 $ 0.05q : num [1:3] -2.703 -0.491 -3.592 $ 0.95q : num [1:3] -0.594 1.865 -1.2 $ median: num [1:3] -1.714 0.582 -2.25 $ mode : num [1:3] -2.1 0.3 -2.1 - attr(*, "samples")= num [1:10, 1:500] -1.94 -1.45 -2.62 0.81 4.11 ... Starting estimation... iteration = : 1 grad.norm() = 491.121 pflug_sum = 0, max_pflug_sum = 0 --------------------------- iteration = : 2 grad.norm() = 480.944 pflug_sum = 234461, max_pflug_sum = 234461 --------------------------- iteration = : 3 grad.norm() = 457.077 pflug_sum = 453864, max_pflug_sum = 453864 --------------------------- iteration = : 4 grad.norm() = 434.859 pflug_sum = 651947, max_pflug_sum = 651947 --------------------------- iteration = : 5 grad.norm() = 409.252 pflug_sum = 827675, max_pflug_sum = 827675 --------------------------- iteration = : 6 grad.norm() = 384.046 pflug_sum = 984204, max_pflug_sum = 984204 --------------------------- iteration = : 7 grad.norm() = 361.471 pflug_sum = 1.12235e+06, max_pflug_sum = 1.12235e+06 --------------------------- iteration = : 8 grad.norm() = 320.812 pflug_sum = 1.23785e+06, max_pflug_sum = 1.23785e+06 --------------------------- iteration = : 9 grad.norm() = 285.185 pflug_sum = 1.32852e+06, max_pflug_sum = 1.32852e+06 --------------------------- iteration = : 10 grad.norm() = 252.194 pflug_sum = 1.39989e+06, max_pflug_sum = 1.39989e+06 --------------------------- Starting posterior sampling... Posterior sampling done. Average standard deviation of the posterior W: 0.200848062936174 Use ngme_post_samples() to access posterior samples and ngme_result() to access latent model parameters. OMP: Warning #96: Cannot form a team with 24 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). 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] -2.007 0.891 -1.826 $ sd : num [1:3] 0.891 0.927 0.91 $ 0.05q : num [1:3] -3.655 -0.579 -3.277 $ 0.95q : num [1:3] -0.593 2.381 -0.325 $ median: num [1:3] -1.971 0.857 -1.866 $ mode : num [1:3] -2.3 0.7 -2.1 - attr(*, "samples")= num [1:10, 1:500] -1.925 -2.667 -2.831 -0.361 2.473 ... Starting estimation... iteration = : 1 grad.norm() = 517.258 pflug_sum = 0, max_pflug_sum = 0 --------------------------- iteration = : 2 grad.norm() = 495.112 pflug_sum = 255267, max_pflug_sum = 255267 --------------------------- iteration = : 3 grad.norm() = 477.5 pflug_sum = 491019, max_pflug_sum = 491019 --------------------------- iteration = : 4 grad.norm() = 452.704 pflug_sum = 706503, max_pflug_sum = 706503 --------------------------- iteration = : 5 grad.norm() = 430.94 pflug_sum = 900523, max_pflug_sum = 900523 --------------------------- iteration = : 6 grad.norm() = 403.432 pflug_sum = 1.07405e+06, max_pflug_sum = 1.07405e+06 --------------------------- iteration = : 7 grad.norm() = 385.462 pflug_sum = 1.229e+06, max_pflug_sum = 1.229e+06 --------------------------- iteration = : 8 grad.norm() = 345.276 pflug_sum = 1.36167e+06, max_pflug_sum = 1.36167e+06 --------------------------- iteration = : 9 grad.norm() = 316.852 pflug_sum = 1.47014e+06, max_pflug_sum = 1.47014e+06 --------------------------- iteration = : 10 grad.norm() = 283.834 pflug_sum = 1.55948e+06, max_pflug_sum = 1.55948e+06 --------------------------- Starting posterior sampling... Posterior sampling done. Average standard deviation of the posterior W: 0.242753017741848 Use ngme_post_samples() to access posterior samples and ngme_result() to access latent model parameters. Flavor: r-devel-linux-x86_64-fedora-gcc