R version 4.6.0 alpha (2026-04-07 r89797 ucrt) Copyright (C) 2026 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > # 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 0.9.5 - See our homepage: https://davidbolin.github.io/ngme2 for more details. Attaching package: 'ngme2' The following object is masked from 'package:stats': ar Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 1.842465 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Last estimates: $theta [1] 1.337028 $rho [1] 3.21434 $`rho (1st)` [1] 0.9695728 $`rho (2nd)` [1] -0.3339929 $`mu (1st)` [1] 0.4412892 $`mu (2nd)` [1] -0.3501611 $`sigma (1st)` [1] 1.416803 $`sigma (2nd)` [1] 1.456078 $`nu (1st)` [1] 2.908246 $`nu (2nd)` [1] 2.615109 List of 6 $ mean : num [1:3] -1.89 0.85 -1.72 $ sd : num [1:3] 0.831 0.941 0.93 $ 0.05q : num [1:3] -3.319 -0.588 -3.214 $ 0.95q : num [1:3] -0.585 2.294 -0.263 $ median: num [1:3] -1.813 0.868 -1.72 $ mode : num [1:3] -1.7 1.1 -1.7 - attr(*, "samples")= num [1:10, 1:500] -1.225 -2.001 -2.107 0.987 1.393 ... Starting estimation... iteration = : 1 grad.norm() = 500.866 pflug_sum = 0, max_pflug_sum = 0 --------------------------- iteration = : 2 grad.norm() = 485.711 pflug_sum = 242426, max_pflug_sum = 242426 --------------------------- iteration = : 3 grad.norm() = 462.777 pflug_sum = 466216, max_pflug_sum = 466216 --------------------------- iteration = : 4 grad.norm() = 439.385 pflug_sum = 668704, max_pflug_sum = 668704 --------------------------- iteration = : 5 grad.norm() = 411.405 pflug_sum = 849165, max_pflug_sum = 849165 --------------------------- iteration = : 6 grad.norm() = 384.457 pflug_sum = 1.00695e+06, max_pflug_sum = 1.00695e+06 --------------------------- iteration = : 7 grad.norm() = 359.909 pflug_sum = 1.14468e+06, max_pflug_sum = 1.14468e+06 --------------------------- iteration = : 8 grad.norm() = 325.824 pflug_sum = 1.2615e+06, max_pflug_sum = 1.2615e+06 --------------------------- iteration = : 9 grad.norm() = 284.077 pflug_sum = 1.35317e+06, max_pflug_sum = 1.35317e+06 --------------------------- iteration = : 10 grad.norm() = 242.227 pflug_sum = 1.42057e+06, max_pflug_sum = 1.42057e+06 --------------------------- Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 0.3132772 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Last estimates: $theta [1] -0.02364329 $rho [1] -0.6155372 $`kappa (1st)` [1] 1.038029 $`kappa (2nd)` [1] 0.8457631 $`mu (1st)` [1] 0.385946 $`mu (2nd)` [1] -0.1161752 $`sigma (1st)` [1] 0.9384433 $`sigma (2nd)` [1] 0.8308083 $`nu (1st)` [1] 1.43144 $`nu (2nd)` [1] 0.9067967 Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 1.226819 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Last estimates: $rho [1] 0.6192866 $sd1 [1] 0.9638948 $sd2 [1] 0.7563881 $`kappa (1st)` [1] 1.077157 $`kappa (2nd)` [1] 0.8521327 $`sigma (1st)` [1] 1.793271 $`sigma (2nd)` [1] 0.6534214 Last estimates: $`rho (bv)` [1] 0.6192866 $`sd1 (bv)` [1] 0.9638948 $`sd2 (bv)` [1] 0.7563881 $`kappa (1st) (bv)` [1] 1.077157 $`kappa (2nd) (bv)` [1] 0.8521327 $`sigma (1st) (bv)` [1] 1.793271 $`sigma (2nd) (bv)` [1] 0.6534214 $`theta_sigma 1` [1] -0.3374415 $`theta_sigma 2` [1] -0.01750384 $`rho(measurement)` [1] 0.5923125 $x1 [1] -2.980352 $x2 [1] 1.501855 Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 1.254774 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Last estimates: $rho [1] 1.046673 $sd1 [1] 1.291596 $sd2 [1] 0.6400522 $`kappa (1st)` [1] 0.8702909 $`kappa (2nd)` [1] 1.21506 $`mu (1st)` [1] -0.004786977 $`mu (2nd)` [1] 0.6006764 $`sigma (1st)` [1] 1.272619 $`sigma (2nd)` [1] 1.019185 $`nu (1st)` [1] 0.8369405 $`nu (2nd)` [1] 0.7776807 Last estimates: $`rho (bv)` [1] 1.046673 $`sd1 (bv)` [1] 1.291596 $`sd2 (bv)` [1] 0.6400522 $`kappa (1st) (bv)` [1] 0.8702909 $`kappa (2nd) (bv)` [1] 1.21506 $`mu (1st) (bv)` [1] -0.004786977 $`mu (2nd) (bv)` [1] 0.6006764 $`sigma (1st) (bv)` [1] 1.272619 $`sigma (2nd) (bv)` [1] 1.019185 $`nu (1st) (bv)` [1] 0.8369405 $`nu (2nd) (bv)` [1] 0.7776807 $`theta_sigma 1` [1] -0.4310094 $`theta_sigma 2` [1] -0.03336109 $`rho(measurement)` [1] 0.818426 $x1 [1] -3.001411 $x2 [1] 1.496069 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() = 0.053628 pflug_sum = 0, max_pflug_sum = 0 --------------------------- iteration = : 2 grad.norm() = 0.053859 pflug_sum = 0.353246, max_pflug_sum = 0.353246 --------------------------- iteration = : 3 grad.norm() = 0.0174338 pflug_sum = 0.621279, max_pflug_sum = 0.621279 --------------------------- iteration = : 4 grad.norm() = 0.0693151 pflug_sum = 0.650845, max_pflug_sum = 0.650845 --------------------------- iteration = : 5 grad.norm() = 0.126933 pflug_sum = 1.15089, max_pflug_sum = 1.15089 --------------------------- iteration = : 6 grad.norm() = 0.019261 pflug_sum = 1.22913, max_pflug_sum = 1.22913 --------------------------- iteration = : 7 grad.norm() = 0.0334378 pflug_sum = 1.23643, max_pflug_sum = 1.23643 --------------------------- iteration = : 8 grad.norm() = 0.00688835 pflug_sum = 1.05636, max_pflug_sum = 1.23643 --------------------------- iteration = : 9 grad.norm() = 0.0150222 pflug_sum = 1.18433, max_pflug_sum = 1.23643 --------------------------- iteration = : 10 grad.norm() = 0.00563666 pflug_sum = 1.11662, max_pflug_sum = 1.23643 --------------------------- iteration = : 11 grad.norm() = 0.0338845 pflug_sum = 1.08753, max_pflug_sum = 1.23643 --------------------------- iteration = : 12 grad.norm() = 0.0395847 pflug_sum = 0.912958, max_pflug_sum = 1.23643 --------------------------- iteration = : 13 grad.norm() = 0.0413451 pflug_sum = 0.683432, max_pflug_sum = 1.23643 --------------------------- iteration = : 14 grad.norm() = 0.0453819 pflug_sum = 0.646216, max_pflug_sum = 1.23643 --------------------------- iteration = : 15 grad.norm() = 0.0621322 pflug_sum = 0.54176, max_pflug_sum = 1.23643 --------------------------- iteration = : 16 grad.norm() = 0.0409825 pflug_sum = 0.529479, max_pflug_sum = 1.23643 --------------------------- iteration = : 17 grad.norm() = 0.054065 pflug_sum = 0.604259, max_pflug_sum = 1.23643 --------------------------- iteration = : 18 grad.norm() = 0.0316887 pflug_sum = 0.364167, max_pflug_sum = 1.23643 --------------------------- iteration = : 19 grad.norm() = 0.0736581 pflug_sum = 0.225747, max_pflug_sum = 1.23643 --------------------------- iteration = : 20 grad.norm() = 0.0103635 pflug_sum = 0.278421, max_pflug_sum = 1.23643 --------------------------- iteration = : 21 grad.norm() = 0.113818 pflug_sum = 0.197842, max_pflug_sum = 1.23643 --------------------------- iteration = : 22 grad.norm() = 0.0100405 pflug_sum = 0.15457, max_pflug_sum = 1.23643 --------------------------- iteration = : 23 grad.norm() = 0.0429181 pflug_sum = 0.115525, max_pflug_sum = 1.23643 --------------------------- iteration = : 24 grad.norm() = 0.0273456 pflug_sum = -0.177558, max_pflug_sum = 1.23643 --------------------------- iteration = : 25 grad.norm() = 0.0472198 pflug_sum = -0.58622, max_pflug_sum = 1.23643 --------------------------- iteration = : 26 grad.norm() = 0.192202 pflug_sum = -0.342868, max_pflug_sum = 1.23643 --------------------------- iteration = : 27 grad.norm() = 0.098686 pflug_sum = -2.06643, max_pflug_sum = 1.23643 --------------------------- iteration = : 28 grad.norm() = 0.0456353 pflug_sum = -2.53032, max_pflug_sum = 1.23643 --------------------------- iteration = : 29 grad.norm() = 0.0193946 pflug_sum = -2.5219, max_pflug_sum = 1.23643 --------------------------- iteration = : 30 grad.norm() = 0.0548994 pflug_sum = -2.60896, max_pflug_sum = 1.23643 --------------------------- iteration = : 31 grad.norm() = 0.0669461 pflug_sum = -3.10041, max_pflug_sum = 1.23643 --------------------------- iteration = : 32 grad.norm() = 0.0178033 pflug_sum = -3.13536, max_pflug_sum = 1.23643 --------------------------- iteration = : 33 grad.norm() = 0.0276455 pflug_sum = -3.11126, max_pflug_sum = 1.23643 --------------------------- iteration = : 34 grad.norm() = 0.0408134 pflug_sum = -3.17426, max_pflug_sum = 1.23643 --------------------------- iteration = : 35 grad.norm() = 0.0641654 pflug_sum = -3.25022, max_pflug_sum = 1.23643 --------------------------- iteration = : 36 grad.norm() = 0.108787 pflug_sum = -3.66793, max_pflug_sum = 1.23643 --------------------------- iteration = : 37 grad.norm() = 0.0427635 pflug_sum = -3.26374, max_pflug_sum = 1.23643 --------------------------- iteration = : 38 grad.norm() = 0.0333375 pflug_sum = -3.55662, max_pflug_sum = 1.23643 --------------------------- iteration = : 39 grad.norm() = 0.013211 pflug_sum = -3.43945, max_pflug_sum = 1.23643 --------------------------- iteration = : 40 grad.norm() = 0.0530349 pflug_sum = -3.42979, max_pflug_sum = 1.23643 --------------------------- iteration = : 41 grad.norm() = 0.0211477 pflug_sum = -3.41049, max_pflug_sum = 1.23643 --------------------------- iteration = : 42 grad.norm() = 0.0200428 pflug_sum = -3.10626, max_pflug_sum = 1.23643 --------------------------- iteration = : 43 grad.norm() = 0.0228237 pflug_sum = -3.01929, max_pflug_sum = 1.23643 --------------------------- iteration = : 44 grad.norm() = 0.0323514 pflug_sum = -3.06998, max_pflug_sum = 1.23643 --------------------------- iteration = : 45 grad.norm() = 0.0301736 pflug_sum = -3.13451, max_pflug_sum = 1.23643 --------------------------- iteration = : 46 grad.norm() = 0.0213218 pflug_sum = -3.2393, max_pflug_sum = 1.23643 --------------------------- iteration = : 47 grad.norm() = 0.0434609 pflug_sum = -3.21437, max_pflug_sum = 1.23643 --------------------------- iteration = : 48 grad.norm() = 0.0656108 pflug_sum = -3.4, max_pflug_sum = 1.23643 --------------------------- iteration = : 49 grad.norm() = 0.0243049 pflug_sum = -3.49426, max_pflug_sum = 1.23643 --------------------------- iteration = : 50 grad.norm() = 0.0444243 pflug_sum = -3.98343, max_pflug_sum = 1.23643 --------------------------- iteration = : 51 grad.norm() = 0.0477331 pflug_sum = -4.2245, max_pflug_sum = 1.23643 --------------------------- iteration = : 52 grad.norm() = 0.0330455 pflug_sum = -4.70921, max_pflug_sum = 1.23643 --------------------------- iteration = : 53 grad.norm() = 0.0508476 pflug_sum = -4.69146, max_pflug_sum = 1.23643 --------------------------- iteration = : 54 grad.norm() = 0.0370746 pflug_sum = -4.68376, max_pflug_sum = 1.23643 --------------------------- iteration = : 55 grad.norm() = 0.0271525 pflug_sum = -4.17695, max_pflug_sum = 1.23643 --------------------------- iteration = : 56 grad.norm() = 0.0179614 pflug_sum = -4.19869, max_pflug_sum = 1.23643 --------------------------- iteration = : 57 grad.norm() = 0.0558418 pflug_sum = -4.27151, max_pflug_sum = 1.23643 --------------------------- iteration = : 58 grad.norm() = 0.0331868 pflug_sum = -4.58252, max_pflug_sum = 1.23643 --------------------------- iteration = : 59 grad.norm() = 0.066065 pflug_sum = -4.82954, max_pflug_sum = 1.23643 --------------------------- iteration = : 60 grad.norm() = 0.0590695 pflug_sum = -4.55083, max_pflug_sum = 1.23643 --------------------------- iteration = : 61 grad.norm() = 0.088906 pflug_sum = -4.82663, max_pflug_sum = 1.23643 --------------------------- iteration = : 62 grad.norm() = 0.0506875 pflug_sum = -4.67832, max_pflug_sum = 1.23643 --------------------------- iteration = : 63 grad.norm() = 0.0507807 pflug_sum = -5.18906, max_pflug_sum = 1.23643 --------------------------- iteration = : 64 grad.norm() = 0.028182 pflug_sum = -5.32114, max_pflug_sum = 1.23643 --------------------------- iteration = : 65 grad.norm() = 0.0137459 pflug_sum = -5.35305, max_pflug_sum = 1.23643 --------------------------- iteration = : 66 grad.norm() = 0.0436795 pflug_sum = -5.33209, max_pflug_sum = 1.23643 --------------------------- iteration = : 67 grad.norm() = 0.00573736 pflug_sum = -5.33578, max_pflug_sum = 1.23643 --------------------------- iteration = : 68 grad.norm() = 0.0721825 pflug_sum = -5.59207, max_pflug_sum = 1.23643 --------------------------- iteration = : 69 grad.norm() = 0.113558 pflug_sum = -6.50765, max_pflug_sum = 1.23643 --------------------------- iteration = : 70 grad.norm() = 0.01867 pflug_sum = -6.32557, max_pflug_sum = 1.23643 --------------------------- iteration = : 71 grad.norm() = 0.00777032 pflug_sum = -6.32998, max_pflug_sum = 1.23643 --------------------------- iteration = : 72 grad.norm() = 0.0402547 pflug_sum = -6.34122, max_pflug_sum = 1.23643 --------------------------- iteration = : 73 grad.norm() = 0.0560957 pflug_sum = -6.4049, max_pflug_sum = 1.23643 --------------------------- iteration = : 74 grad.norm() = 0.0127848 pflug_sum = -6.35777, max_pflug_sum = 1.23643 --------------------------- iteration = : 75 grad.norm() = 0.101335 pflug_sum = -6.32785, max_pflug_sum = 1.23643 --------------------------- iteration = : 76 grad.norm() = 0.0235774 pflug_sum = -6.506, max_pflug_sum = 1.23643 --------------------------- iteration = : 77 grad.norm() = 0.0352637 pflug_sum = -6.6262, max_pflug_sum = 1.23643 --------------------------- iteration = : 78 grad.norm() = 0.0114285 pflug_sum = -6.94706, max_pflug_sum = 1.23643 --------------------------- iteration = : 79 grad.norm() = 0.0958356 pflug_sum = -6.7743, max_pflug_sum = 1.23643 --------------------------- iteration = : 80 grad.norm() = 0.0245033 pflug_sum = -7.05011, max_pflug_sum = 1.23643 --------------------------- iteration = : 81 grad.norm() = 0.0644326 pflug_sum = -7.59552, max_pflug_sum = 1.23643 --------------------------- iteration = : 82 grad.norm() = 0.12903 pflug_sum = -8.12957, max_pflug_sum = 1.23643 --------------------------- iteration = : 83 grad.norm() = 0.0515044 pflug_sum = -7.78449, max_pflug_sum = 1.23643 --------------------------- iteration = : 84 grad.norm() = 0.0458372 pflug_sum = -8.05038, max_pflug_sum = 1.23643 --------------------------- iteration = : 85 grad.norm() = 0.111972 pflug_sum = -8.1796, max_pflug_sum = 1.23643 --------------------------- iteration = : 86 grad.norm() = 0.128539 pflug_sum = -9.58758, max_pflug_sum = 1.23643 --------------------------- iteration = : 87 grad.norm() = 0.00755163 pflug_sum = -9.77423, max_pflug_sum = 1.23643 --------------------------- iteration = : 88 grad.norm() = 0.0428957 pflug_sum = -9.75512, max_pflug_sum = 1.23643 --------------------------- iteration = : 89 grad.norm() = 0.0745325 pflug_sum = -9.92984, max_pflug_sum = 1.23643 --------------------------- iteration = : 90 grad.norm() = 0.0385175 pflug_sum = -9.69752, max_pflug_sum = 1.23643 --------------------------- iteration = : 91 grad.norm() = 0.0479934 pflug_sum = -10.1249, max_pflug_sum = 1.23643 --------------------------- iteration = : 92 grad.norm() = 0.130298 pflug_sum = -9.98066, max_pflug_sum = 1.23643 --------------------------- iteration = : 93 grad.norm() = 0.0568896 pflug_sum = -9.31338, max_pflug_sum = 1.23643 --------------------------- iteration = : 94 grad.norm() = 0.0835013 pflug_sum = -10.1427, max_pflug_sum = 1.23643 --------------------------- iteration = : 95 grad.norm() = 0.0375866 pflug_sum = -10.3067, max_pflug_sum = 1.23643 --------------------------- iteration = : 96 grad.norm() = 0.0119943 pflug_sum = -10.393, max_pflug_sum = 1.23643 --------------------------- iteration = : 97 grad.norm() = 0.00944945 pflug_sum = -10.0064, max_pflug_sum = 1.23643 --------------------------- iteration = : 98 grad.norm() = 0.0426211 pflug_sum = -10.1546, max_pflug_sum = 1.23643 --------------------------- iteration = : 99 grad.norm() = 0.0419435 pflug_sum = -10.0735, max_pflug_sum = 1.23643 --------------------------- iteration = : 100 grad.norm() = 0.0518181 pflug_sum = -10.3043, max_pflug_sum = 1.23643 --------------------------- iteration = : 101 grad.norm() = 0.00693552 pflug_sum = -9.9669, max_pflug_sum = 1.23643 --------------------------- iteration = : 102 grad.norm() = 0.0530646 pflug_sum = -10.3422, max_pflug_sum = 1.23643 --------------------------- iteration = : 103 grad.norm() = 0.0144116 pflug_sum = -10.7164, max_pflug_sum = 1.23643 --------------------------- iteration = : 104 grad.norm() = 0.00784149 pflug_sum = -10.7176, max_pflug_sum = 1.23643 --------------------------- iteration = : 105 grad.norm() = 0.0172702 pflug_sum = -10.8668, max_pflug_sum = 1.23643 --------------------------- iteration = : 106 grad.norm() = 0.00833798 pflug_sum = -10.9579, max_pflug_sum = 1.23643 --------------------------- iteration = : 107 grad.norm() = 0.0672268 pflug_sum = -11.0605, max_pflug_sum = 1.23643 --------------------------- iteration = : 108 grad.norm() = 0.0176355 pflug_sum = -11.3652, max_pflug_sum = 1.23643 --------------------------- iteration = : 109 grad.norm() = 0.0777667 pflug_sum = -11.1519, max_pflug_sum = 1.23643 --------------------------- iteration = : 110 grad.norm() = 0.0639675 pflug_sum = -10.5623, max_pflug_sum = 1.23643 --------------------------- iteration = : 111 grad.norm() = 0.0970138 pflug_sum = -11.0656, max_pflug_sum = 1.23643 --------------------------- iteration = : 112 grad.norm() = 0.0608998 pflug_sum = -11.4031, max_pflug_sum = 1.23643 --------------------------- iteration = : 113 grad.norm() = 0.0316247 pflug_sum = -11.6413, max_pflug_sum = 1.23643 --------------------------- iteration = : 114 grad.norm() = 0.00713901 pflug_sum = -11.6093, max_pflug_sum = 1.23643 --------------------------- iteration = : 115 grad.norm() = 0.031796 pflug_sum = -11.6675, max_pflug_sum = 1.23643 --------------------------- iteration = : 116 grad.norm() = 0.036736 pflug_sum = -11.7197, max_pflug_sum = 1.23643 --------------------------- iteration = : 117 grad.norm() = 0.0631007 pflug_sum = -11.822, max_pflug_sum = 1.23643 --------------------------- iteration = : 118 grad.norm() = 0.0113011 pflug_sum = -11.3282, max_pflug_sum = 1.23643 --------------------------- iteration = : 119 grad.norm() = 0.079449 pflug_sum = -11.1156, max_pflug_sum = 1.23643 --------------------------- iteration = : 120 grad.norm() = 0.0438665 pflug_sum = -10.5781, max_pflug_sum = 1.23643 --------------------------- iteration = : 121 grad.norm() = 0.019511 pflug_sum = -10.7804, max_pflug_sum = 1.23643 --------------------------- iteration = : 122 grad.norm() = 0.0812113 pflug_sum = -11.0436, max_pflug_sum = 1.23643 --------------------------- iteration = : 123 grad.norm() = 0.0115424 pflug_sum = -11.0615, max_pflug_sum = 1.23643 --------------------------- iteration = : 124 grad.norm() = 0.0846858 pflug_sum = -11.2235, max_pflug_sum = 1.23643 --------------------------- iteration = : 125 grad.norm() = 0.0350209 pflug_sum = -11.4954, max_pflug_sum = 1.23643 --------------------------- iteration = : 126 grad.norm() = 0.00508785 pflug_sum = -11.5911, max_pflug_sum = 1.23643 --------------------------- iteration = : 127 grad.norm() = 0.0132181 pflug_sum = -11.4768, max_pflug_sum = 1.23643 --------------------------- iteration = : 128 grad.norm() = 0.0400882 pflug_sum = -11.4103, max_pflug_sum = 1.23643 --------------------------- iteration = : 129 grad.norm() = 0.00692934 pflug_sum = -11.5367, max_pflug_sum = 1.23643 --------------------------- iteration = : 130 grad.norm() = 0.0228766 pflug_sum = -11.8133, max_pflug_sum = 1.23643 --------------------------- iteration = : 131 grad.norm() = 0.0631946 pflug_sum = -11.7953, max_pflug_sum = 1.23643 --------------------------- iteration = : 132 grad.norm() = 0.0558223 pflug_sum = -11.6477, max_pflug_sum = 1.23643 --------------------------- iteration = : 133 grad.norm() = 0.0227189 pflug_sum = -11.5437, max_pflug_sum = 1.23643 --------------------------- iteration = : 134 grad.norm() = 0.0279075 pflug_sum = -11.5192, max_pflug_sum = 1.23643 --------------------------- iteration = : 135 grad.norm() = 0.0370896 pflug_sum = -11.4724, max_pflug_sum = 1.23643 --------------------------- iteration = : 136 grad.norm() = 0.0312271 pflug_sum = -11.9332, max_pflug_sum = 1.23643 --------------------------- iteration = : 137 grad.norm() = 0.133925 pflug_sum = -11.7605, max_pflug_sum = 1.23643 --------------------------- iteration = : 138 grad.norm() = 0.0411689 pflug_sum = -11.4317, max_pflug_sum = 1.23643 --------------------------- iteration = : 139 grad.norm() = 0.0760503 pflug_sum = -11.6023, max_pflug_sum = 1.23643 --------------------------- iteration = : 140 grad.norm() = 0.0486992 pflug_sum = -11.4231, max_pflug_sum = 1.23643 --------------------------- iteration = : 141 grad.norm() = 0.0202521 pflug_sum = -11.9857, max_pflug_sum = 1.23643 --------------------------- iteration = : 142 grad.norm() = 0.0265128 pflug_sum = -11.8239, max_pflug_sum = 1.23643 --------------------------- iteration = : 143 grad.norm() = 0.0130708 pflug_sum = -12.1963, max_pflug_sum = 1.23643 --------------------------- iteration = : 144 grad.norm() = 0.0200016 pflug_sum = -12.455, max_pflug_sum = 1.23643 --------------------------- iteration = : 145 grad.norm() = 0.0122866 pflug_sum = -12.4524, max_pflug_sum = 1.23643 --------------------------- iteration = : 146 grad.norm() = 0.0597638 pflug_sum = -12.4372, max_pflug_sum = 1.23643 --------------------------- iteration = : 147 grad.norm() = 0.0708008 pflug_sum = -12.3458, max_pflug_sum = 1.23643 --------------------------- iteration = : 148 grad.norm() = 0.00928378 pflug_sum = -12.5143, max_pflug_sum = 1.23643 --------------------------- iteration = : 149 grad.norm() = 0.0566657 pflug_sum = -12.6597, max_pflug_sum = 1.23643 --------------------------- iteration = : 150 grad.norm() = 0.0184405 pflug_sum = -12.28, max_pflug_sum = 1.23643 --------------------------- iteration = : 151 grad.norm() = 0.0235074 pflug_sum = -12.5775, max_pflug_sum = 1.23643 --------------------------- iteration = : 152 grad.norm() = 0.0309839 pflug_sum = -13.1766, max_pflug_sum = 1.23643 --------------------------- iteration = : 153 grad.norm() = 0.0391213 pflug_sum = -13.3619, max_pflug_sum = 1.23643 --------------------------- iteration = : 154 grad.norm() = 0.0270418 pflug_sum = -13.3371, max_pflug_sum = 1.23643 --------------------------- iteration = : 155 grad.norm() = 0.0828181 pflug_sum = -13.485, max_pflug_sum = 1.23643 --------------------------- iteration = : 156 grad.norm() = 0.0209432 pflug_sum = -13.6069, max_pflug_sum = 1.23643 --------------------------- iteration = : 157 grad.norm() = 0.00440985 pflug_sum = -13.7058, max_pflug_sum = 1.23643 --------------------------- iteration = : 158 grad.norm() = 0.028555 pflug_sum = -13.7294, max_pflug_sum = 1.23643 --------------------------- iteration = : 159 grad.norm() = 0.0360978 pflug_sum = -13.8385, max_pflug_sum = 1.23643 --------------------------- iteration = : 160 grad.norm() = 0.0887133 pflug_sum = -13.5432, max_pflug_sum = 1.23643 --------------------------- iteration = : 161 grad.norm() = 0.0132887 pflug_sum = -13.4082, max_pflug_sum = 1.23643 --------------------------- iteration = : 162 grad.norm() = 0.013299 pflug_sum = -13.0762, max_pflug_sum = 1.23643 --------------------------- iteration = : 163 grad.norm() = 0.00704656 pflug_sum = -13.236, max_pflug_sum = 1.23643 --------------------------- iteration = : 164 grad.norm() = 0.0131503 pflug_sum = -13.3436, max_pflug_sum = 1.23643 --------------------------- iteration = : 165 grad.norm() = 0.0787618 pflug_sum = -13.251, max_pflug_sum = 1.23643 --------------------------- iteration = : 166 grad.norm() = 0.0326795 pflug_sum = -13.4798, max_pflug_sum = 1.23643 --------------------------- iteration = : 167 grad.norm() = 0.0998721 pflug_sum = -14.2086, max_pflug_sum = 1.23643 --------------------------- iteration = : 168 grad.norm() = 0.00826737 pflug_sum = -14.0328, max_pflug_sum = 1.23643 --------------------------- iteration = : 169 grad.norm() = 0.0360416 pflug_sum = -14.1582, max_pflug_sum = 1.23643 --------------------------- iteration = : 170 grad.norm() = 0.103657 pflug_sum = -14.2379, max_pflug_sum = 1.23643 --------------------------- iteration = : 171 grad.norm() = 0.0158343 pflug_sum = -14.473, max_pflug_sum = 1.23643 --------------------------- iteration = : 172 grad.norm() = 0.0558743 pflug_sum = -14.41, max_pflug_sum = 1.23643 --------------------------- iteration = : 173 grad.norm() = 0.0406568 pflug_sum = -14.5011, max_pflug_sum = 1.23643 --------------------------- iteration = : 174 grad.norm() = 0.0440419 pflug_sum = -14.7623, max_pflug_sum = 1.23643 --------------------------- iteration = : 175 grad.norm() = 0.0884259 pflug_sum = -14.5399, max_pflug_sum = 1.23643 --------------------------- iteration = : 176 grad.norm() = 0.0168617 pflug_sum = -14.6544, max_pflug_sum = 1.23643 --------------------------- iteration = : 177 grad.norm() = 0.0249583 pflug_sum = -14.6273, max_pflug_sum = 1.23643 --------------------------- iteration = : 178 grad.norm() = 0.0578795 pflug_sum = -14.5119, max_pflug_sum = 1.23643 --------------------------- iteration = : 179 grad.norm() = 0.0408355 pflug_sum = -14.6636, max_pflug_sum = 1.23643 --------------------------- iteration = : 180 grad.norm() = 0.0102066 pflug_sum = -14.612, max_pflug_sum = 1.23643 --------------------------- iteration = : 181 grad.norm() = 0.102753 pflug_sum = -14.6431, max_pflug_sum = 1.23643 --------------------------- iteration = : 182 grad.norm() = 0.0521068 pflug_sum = -14.9925, max_pflug_sum = 1.23643 --------------------------- iteration = : 183 grad.norm() = 0.0545981 pflug_sum = -14.8609, max_pflug_sum = 1.23643 --------------------------- iteration = : 184 grad.norm() = 0.00925912 pflug_sum = -14.8294, max_pflug_sum = 1.23643 --------------------------- iteration = : 185 grad.norm() = 0.021103 pflug_sum = -15.2074, max_pflug_sum = 1.23643 --------------------------- iteration = : 186 grad.norm() = 0.0312419 pflug_sum = -14.8989, max_pflug_sum = 1.23643 --------------------------- iteration = : 187 grad.norm() = 0.0352288 pflug_sum = -14.9913, max_pflug_sum = 1.23643 --------------------------- iteration = : 188 grad.norm() = 0.0205315 pflug_sum = -15.0912, max_pflug_sum = 1.23643 --------------------------- iteration = : 189 grad.norm() = 0.0588523 pflug_sum = -15.4932, max_pflug_sum = 1.23643 --------------------------- iteration = : 190 grad.norm() = 0.0349953 pflug_sum = -15.324, max_pflug_sum = 1.23643 --------------------------- iteration = : 191 grad.norm() = 0.0255064 pflug_sum = -16.0256, max_pflug_sum = 1.23643 --------------------------- iteration = : 192 grad.norm() = 0.0483554 pflug_sum = -15.9454, max_pflug_sum = 1.23643 --------------------------- iteration = : 193 grad.norm() = 0.048513 pflug_sum = -16.1139, max_pflug_sum = 1.23643 --------------------------- iteration = : 194 grad.norm() = 0.00952081 pflug_sum = -16.4114, max_pflug_sum = 1.23643 --------------------------- iteration = : 195 grad.norm() = 0.0203957 pflug_sum = -16.4147, max_pflug_sum = 1.23643 --------------------------- iteration = : 196 grad.norm() = 0.159744 pflug_sum = -16.4189, max_pflug_sum = 1.23643 --------------------------- iteration = : 197 grad.norm() = 0.0246935 pflug_sum = -16.5396, max_pflug_sum = 1.23643 --------------------------- iteration = : 198 grad.norm() = 0.090524 pflug_sum = -16.4427, max_pflug_sum = 1.23643 --------------------------- iteration = : 199 grad.norm() = 0.0368733 pflug_sum = -16.6627, max_pflug_sum = 1.23643 --------------------------- iteration = : 200 grad.norm() = 0.0664772 pflug_sum = -17.0739, max_pflug_sum = 1.23643 --------------------------- Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 2.211594 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Last estimates: $`alpha (field1)` [1] 2.269925 $`kappa (field1)` [1] 12.10952 $`sigma (field1)` [1] 188.6149 $sigma [1] 0.09941873 Last estimates: $alpha [1] 2.269925 $kappa [1] 12.10952 $sigma [1] 188.6149 Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 0.3242468 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. [1] 0.2488166 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 Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 0.3242468 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. [1] 0.2488166 Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 0.1827502 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 0.1827502 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 0.007968218 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 0.007968218 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 0.5291824 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. [1] 0.28398 Last estimates: $rho [1] 0.6701904 $sigma [1] 0.4446905 Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 0.5291824 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. [1] 0.28398 Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 0.07872049 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 0.07872049 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 0.03305194 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 0.03305194 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 0.05185993 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Last estimates: $kappa [1] 1.24138 $sigma [1] 0.3678616 Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 0.05185993 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 0.6165854 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 0.609882 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: NA Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Last estimates: $sigma [1] 0.4995907 $`(Intercept)` [1] -1.001274 $x [1] 1.994434 Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: NA Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Last estimates: $mu [1] 4.722265 $sigma [1] 0.8281018 $nu [1] 0.09103069 Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 6.316029 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Last estimates: $rho [1] 0.5869809 $sigma [1] 5.163637 Last estimates: $`rho (field1)` [1] 0.5869809 $`sigma (field1)` [1] 5.163637 $mu [1] 0.4278792 $sigma [1] 1.690419 $nu [1] 0.1648431 Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 0.8728917 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 1.266691 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 0.7091155 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: 1.556775 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. [1] "rho" "c1" "c2" "rho (1st)" "rho (2nd)" "sigma_1" [7] "sigma_2" "sigma_1" Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: NA Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: NA Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Last estimates: $rho [1] 0.3 $rho.1 [1] 0.8 Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: NA Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. Starting estimation... Starting posterior sampling... Posterior sampling done! Average standard deviation of the posterior W: NA Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. 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 Note: 1. Use ngme_post_samples(..) to access the posterior samples. 2. Use ngme_result(..) to access different latent models. [ FAIL 0 | WARN 0 | SKIP 10 | PASS 361 ] ══ 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' [ FAIL 0 | WARN 0 | SKIP 10 | PASS 361 ] > > proc.time() user system elapsed 139.42 1.43 140.93