R version 4.6.0 RC (2026-04-20 r89921 ucrt) -- "Because it was There" 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 0.9.6 - 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] -1.959 0.805 -1.804 $ sd : num [1:3] 0.842 0.943 0.851 $ 0.05q : num [1:3] -3.31 -0.817 -3.204 $ 0.95q : num [1:3] -0.643 2.32 -0.495 $ median: num [1:3] -1.97 0.791 -1.776 $ mode : num [1:3] -2.1 0.7 -1.5 - attr(*, "samples")= num [1:10, 1:500] -1.423 0.149 -1.468 0.52 1.977 ... Starting estimation... iteration = : 1 grad.norm() = 521.07 pflug_sum = 0, max_pflug_sum = 0 --------------------------- iteration = : 2 grad.norm() = 501.111 pflug_sum = 260550, max_pflug_sum = 260550 --------------------------- iteration = : 3 grad.norm() = 474.239 pflug_sum = 497975, max_pflug_sum = 497975 --------------------------- iteration = : 4 grad.norm() = 458.919 pflug_sum = 715074, max_pflug_sum = 715074 --------------------------- iteration = : 5 grad.norm() = 432.353 pflug_sum = 912967, max_pflug_sum = 912967 --------------------------- iteration = : 6 grad.norm() = 406.078 pflug_sum = 1.08771e+06, max_pflug_sum = 1.08771e+06 --------------------------- iteration = : 7 grad.norm() = 390.096 pflug_sum = 1.2443e+06, max_pflug_sum = 1.2443e+06 --------------------------- iteration = : 8 grad.norm() = 353.118 pflug_sum = 1.38008e+06, max_pflug_sum = 1.38008e+06 --------------------------- iteration = : 9 grad.norm() = 320.872 pflug_sum = 1.49222e+06, max_pflug_sum = 1.49222e+06 --------------------------- iteration = : 10 grad.norm() = 285.466 pflug_sum = 1.58351e+06, max_pflug_sum = 1.58351e+06 --------------------------- Starting posterior sampling... Posterior sampling done. Average standard deviation of the posterior W: 0.265027624706329 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() = 0.105767 pflug_sum = 0, max_pflug_sum = 0 --------------------------- iteration = : 2 grad.norm() = 0.0790134 pflug_sum = 0.522756, max_pflug_sum = 0.522756 --------------------------- iteration = : 3 grad.norm() = 0.0961694 pflug_sum = 0.938242, max_pflug_sum = 0.938242 --------------------------- iteration = : 4 grad.norm() = 0.06316 pflug_sum = 0.801437, max_pflug_sum = 0.938242 --------------------------- iteration = : 5 grad.norm() = 0.0638171 pflug_sum = 0.596937, max_pflug_sum = 0.938242 --------------------------- iteration = : 6 grad.norm() = 0.0149701 pflug_sum = 0.636744, max_pflug_sum = 0.938242 --------------------------- iteration = : 7 grad.norm() = 0.091411 pflug_sum = 0.710474, max_pflug_sum = 0.938242 --------------------------- iteration = : 8 grad.norm() = 0.0591088 pflug_sum = 0.536066, max_pflug_sum = 0.938242 --------------------------- iteration = : 9 grad.norm() = 0.0836534 pflug_sum = 0.538044, max_pflug_sum = 0.938242 --------------------------- iteration = : 10 grad.norm() = 0.00642257 pflug_sum = 0.559285, max_pflug_sum = 0.938242 --------------------------- iteration = : 11 grad.norm() = 0.0596336 pflug_sum = 0.531902, max_pflug_sum = 0.938242 --------------------------- iteration = : 12 grad.norm() = 0.0315675 pflug_sum = 0.441311, max_pflug_sum = 0.938242 --------------------------- iteration = : 13 grad.norm() = 0.0266407 pflug_sum = 0.303701, max_pflug_sum = 0.938242 --------------------------- iteration = : 14 grad.norm() = 0.057514 pflug_sum = -0.0171086, max_pflug_sum = 0.938242 --------------------------- iteration = : 15 grad.norm() = 0.0918459 pflug_sum = -0.249278, max_pflug_sum = 0.938242 --------------------------- iteration = : 16 grad.norm() = 0.0575588 pflug_sum = -0.448764, max_pflug_sum = 0.938242 --------------------------- iteration = : 17 grad.norm() = 0.068202 pflug_sum = -0.558573, max_pflug_sum = 0.938242 --------------------------- iteration = : 18 grad.norm() = 0.0718294 pflug_sum = -1.2163, max_pflug_sum = 0.938242 --------------------------- iteration = : 19 grad.norm() = 0.0831529 pflug_sum = -1.35009, max_pflug_sum = 0.938242 --------------------------- iteration = : 20 grad.norm() = 0.0480653 pflug_sum = -1.65262, max_pflug_sum = 0.938242 --------------------------- iteration = : 21 grad.norm() = 0.0203772 pflug_sum = -1.20633, max_pflug_sum = 0.938242 --------------------------- iteration = : 22 grad.norm() = 0.0284136 pflug_sum = -1.39768, max_pflug_sum = 0.938242 --------------------------- iteration = : 23 grad.norm() = 0.0552596 pflug_sum = -1.54645, max_pflug_sum = 0.938242 --------------------------- iteration = : 24 grad.norm() = 0.0297665 pflug_sum = -1.75954, max_pflug_sum = 0.938242 --------------------------- iteration = : 25 grad.norm() = 0.0765314 pflug_sum = -1.52951, max_pflug_sum = 0.938242 --------------------------- iteration = : 26 grad.norm() = 0.0335386 pflug_sum = -1.60382, max_pflug_sum = 0.938242 --------------------------- iteration = : 27 grad.norm() = 0.0444536 pflug_sum = -1.64295, max_pflug_sum = 0.938242 --------------------------- iteration = : 28 grad.norm() = 0.0213751 pflug_sum = -1.55898, max_pflug_sum = 0.938242 --------------------------- iteration = : 29 grad.norm() = 0.053126 pflug_sum = -1.33004, max_pflug_sum = 0.938242 --------------------------- iteration = : 30 grad.norm() = 0.075869 pflug_sum = -1.7328, max_pflug_sum = 0.938242 --------------------------- iteration = : 31 grad.norm() = 0.0336134 pflug_sum = -1.60139, max_pflug_sum = 0.938242 --------------------------- iteration = : 32 grad.norm() = 0.0434728 pflug_sum = -1.54635, max_pflug_sum = 0.938242 --------------------------- iteration = : 33 grad.norm() = 0.107976 pflug_sum = -1.8225, max_pflug_sum = 0.938242 --------------------------- iteration = : 34 grad.norm() = 0.0941674 pflug_sum = -1.26776, max_pflug_sum = 0.938242 --------------------------- iteration = : 35 grad.norm() = 0.113645 pflug_sum = -2.64136, max_pflug_sum = 0.938242 --------------------------- iteration = : 36 grad.norm() = 0.082979 pflug_sum = -2.58471, max_pflug_sum = 0.938242 --------------------------- iteration = : 37 grad.norm() = 0.0448447 pflug_sum = -2.79266, max_pflug_sum = 0.938242 --------------------------- iteration = : 38 grad.norm() = 0.022145 pflug_sum = -2.65121, max_pflug_sum = 0.938242 --------------------------- iteration = : 39 grad.norm() = 0.00935894 pflug_sum = -2.60645, max_pflug_sum = 0.938242 --------------------------- iteration = : 40 grad.norm() = 0.154736 pflug_sum = -2.7208, max_pflug_sum = 0.938242 --------------------------- iteration = : 41 grad.norm() = 0.0611763 pflug_sum = -3.23071, max_pflug_sum = 0.938242 --------------------------- iteration = : 42 grad.norm() = 0.0422108 pflug_sum = -3.47772, max_pflug_sum = 0.938242 --------------------------- iteration = : 43 grad.norm() = 0.103841 pflug_sum = -3.71945, max_pflug_sum = 0.938242 --------------------------- iteration = : 44 grad.norm() = 0.0248152 pflug_sum = -2.9854, max_pflug_sum = 0.938242 --------------------------- iteration = : 45 grad.norm() = 0.0661476 pflug_sum = -3.44501, max_pflug_sum = 0.938242 --------------------------- iteration = : 46 grad.norm() = 0.0690095 pflug_sum = -3.79874, max_pflug_sum = 0.938242 --------------------------- iteration = : 47 grad.norm() = 0.021611 pflug_sum = -3.29717, max_pflug_sum = 0.938242 --------------------------- iteration = : 48 grad.norm() = 0.119839 pflug_sum = -2.95034, max_pflug_sum = 0.938242 --------------------------- iteration = : 49 grad.norm() = 0.132892 pflug_sum = -4.13228, max_pflug_sum = 0.938242 --------------------------- iteration = : 50 grad.norm() = 0.0562771 pflug_sum = -3.90057, max_pflug_sum = 0.938242 --------------------------- iteration = : 51 grad.norm() = 0.0126421 pflug_sum = -4.12315, max_pflug_sum = 0.938242 --------------------------- iteration = : 52 grad.norm() = 0.0564822 pflug_sum = -3.88513, max_pflug_sum = 0.938242 --------------------------- iteration = : 53 grad.norm() = 0.0425619 pflug_sum = -4.45074, max_pflug_sum = 0.938242 --------------------------- iteration = : 54 grad.norm() = 0.00802203 pflug_sum = -5.26693, max_pflug_sum = 0.938242 --------------------------- iteration = : 55 grad.norm() = 0.0370633 pflug_sum = -5.31723, max_pflug_sum = 0.938242 --------------------------- iteration = : 56 grad.norm() = 0.00629521 pflug_sum = -5.26946, max_pflug_sum = 0.938242 --------------------------- iteration = : 57 grad.norm() = 0.0167996 pflug_sum = -5.13079, max_pflug_sum = 0.938242 --------------------------- iteration = : 58 grad.norm() = 0.0455925 pflug_sum = -5.79643, max_pflug_sum = 0.938242 --------------------------- iteration = : 59 grad.norm() = 0.0158208 pflug_sum = -5.56528, max_pflug_sum = 0.938242 --------------------------- iteration = : 60 grad.norm() = 0.0968529 pflug_sum = -5.67515, max_pflug_sum = 0.938242 --------------------------- 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: 2.02438833063502 Use ngme_post_samples() to access posterior samples and ngme_result() to access latent model parameters. [1] 0.4029765 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] 0.4029765 [1] 0.2371031 [1] 0.2371031 [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 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 151.56 2.45 154.06