R Under development (unstable) (2024-02-22 r85974 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 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) > library(tsnet) > > test_check("tsnet") SAMPLING FOR MODEL 'VAR_wishart' NOW (CHAIN 1). Chain 1: Chain 1: Gradient evaluation took 0.000461 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.61 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup) Chain 1: Iteration: 500 / 1000 [ 50%] (Warmup) Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling) Chain 1: Iteration: 1000 / 1000 [100%] (Sampling) Chain 1: Chain 1: Elapsed Time: 2.289 seconds (Warm-up) Chain 1: 1.762 seconds (Sampling) Chain 1: 4.051 seconds (Total) Chain 1: The VAR coefficient matrix of the input model is stable. The VAR coefficient matrix of the input model is stable. The VAR coefficient matrix of the input model is not stable. It is recommended to refit the model and to inspect your data. SAMPLING FOR MODEL 'VAR_wishart' NOW (CHAIN 1). Chain 1: Chain 1: Gradient evaluation took 0.00028 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.8 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup) Chain 1: Iteration: 500 / 1000 [ 50%] (Warmup) Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling) Chain 1: Iteration: 1000 / 1000 [100%] (Sampling) Chain 1: Chain 1: Elapsed Time: 2.008 seconds (Warm-up) Chain 1: 1.592 seconds (Sampling) Chain 1: 3.6 seconds (Total) Chain 1: The VAR coefficient matrix of the input model is stable. SAMPLING FOR MODEL 'VAR_wishart' NOW (CHAIN 1). Chain 1: Chain 1: Gradient evaluation took 0.000286 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.86 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup) Chain 1: Iteration: 500 / 1000 [ 50%] (Warmup) Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling) Chain 1: Iteration: 1000 / 1000 [100%] (Sampling) Chain 1: Chain 1: Elapsed Time: 1.779 seconds (Warm-up) Chain 1: 1.538 seconds (Sampling) Chain 1: 3.317 seconds (Total) Chain 1: SAMPLING FOR MODEL 'VAR_wishart' NOW (CHAIN 1). Chain 1: Chain 1: Gradient evaluation took 0.000217 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.17 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup) Chain 1: Iteration: 500 / 1000 [ 50%] (Warmup) Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling) Chain 1: Iteration: 1000 / 1000 [100%] (Sampling) Chain 1: Chain 1: Elapsed Time: 2.036 seconds (Warm-up) Chain 1: 1.563 seconds (Sampling) Chain 1: 3.599 seconds (Total) Chain 1: SAMPLING FOR MODEL 'VAR_wishart' NOW (CHAIN 1). Chain 1: Chain 1: Gradient evaluation took 0.000289 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.89 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup) Chain 1: Iteration: 500 / 1000 [ 50%] (Warmup) Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling) Chain 1: Iteration: 1000 / 1000 [100%] (Sampling) Chain 1: Chain 1: Elapsed Time: 2.132 seconds (Warm-up) Chain 1: 1.344 seconds (Sampling) Chain 1: 3.476 seconds (Total) Chain 1: SAMPLING FOR MODEL 'VAR_wishart' NOW (CHAIN 1). Chain 1: Chain 1: Gradient evaluation took 0.000203 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.03 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup) Chain 1: Iteration: 500 / 1000 [ 50%] (Warmup) Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling) Chain 1: Iteration: 1000 / 1000 [100%] (Sampling) Chain 1: Chain 1: Elapsed Time: 1.874 seconds (Warm-up) Chain 1: 1.578 seconds (Sampling) Chain 1: 3.452 seconds (Total) Chain 1: SAMPLING FOR MODEL 'VAR_wishart' NOW (CHAIN 1). Chain 1: Chain 1: Gradient evaluation took 0.000206 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.06 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup) Chain 1: Iteration: 500 / 1000 [ 50%] (Warmup) Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling) Chain 1: Iteration: 1000 / 1000 [100%] (Sampling) Chain 1: Chain 1: Elapsed Time: 1.62 seconds (Warm-up) Chain 1: 1.33 seconds (Sampling) Chain 1: 2.95 seconds (Total) Chain 1: SAMPLING FOR MODEL 'VAR_wishart' NOW (CHAIN 1). Chain 1: Chain 1: Gradient evaluation took 0.000207 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.07 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup) Chain 1: Iteration: 500 / 1000 [ 50%] (Warmup) Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling) Chain 1: Iteration: 1000 / 1000 [100%] (Sampling) Chain 1: Chain 1: Elapsed Time: 2.038 seconds (Warm-up) Chain 1: 1.705 seconds (Sampling) Chain 1: 3.743 seconds (Total) Chain 1: ### Summary of the Norm-Based Comparison Test ### #--- General Summary ---# In the temporal network 2 of the 2 comparisons were significant. In the contemporaneous network 2 of the 2 comparisons were significant. #--- Model-specific Results ---# For mod_a 0 of the reference distances of the temporal network and 0 of the reference distances of the contemporaneous network were larger than the empirical distance. For mod_b 0 of the reference distances of the temporal network and 0 of the reference distances of the contemporaneous network were larger than the empirical distance. SAMPLING FOR MODEL 'VAR_wishart' NOW (CHAIN 1). Chain 1: Chain 1: Gradient evaluation took 0.000239 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.39 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup) Chain 1: Iteration: 500 / 1000 [ 50%] (Warmup) Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling) Chain 1: Iteration: 1000 / 1000 [100%] (Sampling) Chain 1: Chain 1: Elapsed Time: 2.086 seconds (Warm-up) Chain 1: 1.569 seconds (Sampling) Chain 1: 3.655 seconds (Total) Chain 1: ### Summary of the Bayesian GVAR model ### #--- General Summary ---# Model was estimated using MCMC with 1 chains using 500 iterations each.Warmup was set to 500 iterations. The model was estimated using the IW covariance prior. #--- Parameter Summary ---# The posterior mean of the temporal coefficients is: V1 V2 V3 V1.l1 0.35444036 0.41961192 0.01680634 V2.l1 0.02266528 0.07844007 0.67494668 V3.l1 -0.19577775 0.54089113 0.02803813 Rownames correspond to the independent variable, column names to the dependent variable. The posterior mean of the contemporaneous coefficients is: V1 V2 V3 V1 0.0000000 0.3157253 -0.5485733 V2 0.3157253 0.0000000 -0.3339033 V3 -0.5485733 -0.3339033 0.0000000 Chain 1: ------------------------------------------------------------ Chain 1: EXPERIMENTAL ALGORITHM: Chain 1: This procedure has not been thoroughly tested and may be unstable Chain 1: or buggy. The interface is subject to change. Chain 1: ------------------------------------------------------------ Chain 1: Chain 1: Chain 1: Chain 1: Gradient evaluation took 0.000204 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.04 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Begin eta adaptation. Chain 1: Iteration: 1 / 250 [ 0%] (Adaptation) Chain 1: Iteration: 50 / 250 [ 20%] (Adaptation) Chain 1: Iteration: 100 / 250 [ 40%] (Adaptation) Chain 1: Iteration: 150 / 250 [ 60%] (Adaptation) Chain 1: Iteration: 200 / 250 [ 80%] (Adaptation) Chain 1: Success! Found best value [eta = 1] earlier than expected. Chain 1: Chain 1: Begin stochastic gradient ascent. Chain 1: iter ELBO delta_ELBO_mean delta_ELBO_med notes Chain 1: 100 -642.005 1.000 1.000 Chain 1: 200 -331.202 0.969 1.000 Chain 1: 300 -326.611 0.651 0.938 Chain 1: 400 -323.592 0.490 0.938 Chain 1: 500 -324.556 0.393 0.014 Chain 1: 600 -323.047 0.328 0.014 Chain 1: 700 -324.240 0.282 0.009 Chain 1: 800 -325.748 0.247 0.009 Chain 1: 900 -324.232 0.220 0.005 Chain 1: 1000 -322.878 0.199 0.005 Chain 1: 1100 -323.717 0.099 0.005 Chain 1: 1200 -324.231 0.005 0.005 Chain 1: 1300 -326.981 0.005 0.005 Chain 1: 1400 -325.983 0.004 0.004 Chain 1: 1500 -324.740 0.004 0.004 Chain 1: 1600 -323.159 0.004 0.004 Chain 1: 1700 -325.310 0.004 0.005 Chain 1: 1800 -323.325 0.005 0.005 Chain 1: 1900 -322.673 0.004 0.004 Chain 1: 2000 -323.021 0.004 0.004 Chain 1: 2100 -324.013 0.004 0.004 Chain 1: 2200 -324.013 0.004 0.004 Chain 1: 2300 -324.556 0.003 0.003 Chain 1: 2400 -323.922 0.003 0.003 Chain 1: 2500 -323.916 0.003 0.002 Chain 1: 2600 -323.364 0.002 0.002 Chain 1: 2700 -322.349 0.002 0.002 Chain 1: 2800 -323.807 0.002 0.002 Chain 1: 2900 -323.342 0.002 0.002 Chain 1: 3000 -323.370 0.002 0.002 Chain 1: 3100 -323.482 0.001 0.002 Chain 1: 3200 -323.203 0.002 0.002 Chain 1: 3300 -322.348 0.002 0.002 Chain 1: 3400 -323.793 0.002 0.002 Chain 1: 3500 -323.918 0.002 0.002 Chain 1: 3600 -324.551 0.002 0.002 Chain 1: 3700 -323.211 0.002 0.002 Chain 1: 3800 -323.097 0.002 0.001 Chain 1: 3900 -323.228 0.002 0.001 MEDIAN ELBO CONVERGED Chain 1: Chain 1: Drawing a sample of size 500 from the approximate posterior... Chain 1: COMPLETED. ### Summary of the Bayesian GVAR model ### #--- General Summary ---# Model was estimated using variational inference with 500 iterations. The model was estimated using the IW covariance prior. #--- Parameter Summary ---# The posterior mean of the temporal coefficients is: V1 V2 V3 V1.l1 0.34861605 0.41655402 -0.02077500 V2.l1 0.01609562 0.07838889 0.66734569 V3.l1 -0.19365282 0.54548002 0.04041698 Rownames correspond to the independent variable, column names to the dependent variable. The posterior mean of the contemporaneous coefficients is: V1 V2 V3 V1 0.0000000 0.2964747 -0.5384615 V2 0.2964747 0.0000000 -0.3434627 V3 -0.5384615 -0.3434627 0.0000000 SAMPLING FOR MODEL 'VAR_wishart' NOW (CHAIN 1). Chain 1: Chain 1: Gradient evaluation took 0.000205 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.05 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup) Chain 1: Iteration: 500 / 1000 [ 50%] (Warmup) Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling) Chain 1: Iteration: 1000 / 1000 [100%] (Sampling) Chain 1: Chain 1: Elapsed Time: 2.146 seconds (Warm-up) Chain 1: 2.356 seconds (Sampling) Chain 1: 4.502 seconds (Total) Chain 1: Chain 1: ------------------------------------------------------------ Chain 1: EXPERIMENTAL ALGORITHM: Chain 1: This procedure has not been thoroughly tested and may be unstable Chain 1: or buggy. The interface is subject to change. Chain 1: ------------------------------------------------------------ Chain 1: Chain 1: Chain 1: Chain 1: Gradient evaluation took 0.000282 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.82 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Begin eta adaptation. Chain 1: Iteration: 1 / 250 [ 0%] (Adaptation) Chain 1: Iteration: 50 / 250 [ 20%] (Adaptation) Chain 1: Iteration: 100 / 250 [ 40%] (Adaptation) Chain 1: Iteration: 150 / 250 [ 60%] (Adaptation) Chain 1: Iteration: 200 / 250 [ 80%] (Adaptation) Chain 1: Success! Found best value [eta = 1] earlier than expected. Chain 1: Chain 1: Begin stochastic gradient ascent. Chain 1: iter ELBO delta_ELBO_mean delta_ELBO_med notes Chain 1: 100 -2848.050 1.000 1.000 Chain 1: 200 -514.039 2.770 4.541 Chain 1: 300 -339.010 2.019 1.000 Chain 1: 400 -326.601 1.524 1.000 Chain 1: 500 -324.381 1.220 0.516 Chain 1: 600 -324.646 1.017 0.516 Chain 1: 700 -325.512 0.872 0.038 Chain 1: 800 -325.770 0.763 0.038 Chain 1: 900 -325.544 0.679 0.007 Chain 1: 1000 -325.189 0.611 0.007 Chain 1: 1100 -322.749 0.512 0.007 MAY BE DIVERGING... INSPECT ELBO Chain 1: 1200 -324.030 0.058 0.004 Chain 1: 1300 -325.127 0.007 0.003 Chain 1: 1400 -323.749 0.003 0.003 Chain 1: 1500 -324.224 0.003 0.003 Chain 1: 1600 -323.653 0.003 0.003 Chain 1: 1700 -324.543 0.003 0.003 Chain 1: 1800 -323.882 0.003 0.003 Chain 1: 1900 -324.865 0.003 0.003 Chain 1: 2000 -323.942 0.003 0.003 Chain 1: 2100 -323.625 0.003 0.003 Chain 1: 2200 -323.751 0.002 0.003 Chain 1: 2300 -324.268 0.002 0.002 Chain 1: 2400 -324.669 0.002 0.002 Chain 1: 2500 -322.662 0.002 0.002 Chain 1: 2600 -323.728 0.002 0.003 Chain 1: 2700 -324.649 0.002 0.003 Chain 1: 2800 -324.133 0.002 0.003 Chain 1: 2900 -323.306 0.002 0.003 Chain 1: 3000 -323.021 0.002 0.002 Chain 1: 3100 -324.261 0.002 0.003 Chain 1: 3200 -324.225 0.002 0.003 Chain 1: 3300 -323.912 0.002 0.003 Chain 1: 3400 -323.505 0.002 0.003 Chain 1: 3500 -322.266 0.002 0.003 Chain 1: 3600 -323.275 0.002 0.003 Chain 1: 3700 -323.294 0.002 0.002 Chain 1: 3800 -324.644 0.002 0.003 Chain 1: 3900 -322.801 0.002 0.003 Chain 1: 4000 -323.462 0.003 0.003 Chain 1: 4100 -323.907 0.002 0.002 Chain 1: 4200 -325.174 0.003 0.003 Chain 1: 4300 -322.758 0.003 0.004 Chain 1: 4400 -323.345 0.003 0.004 Chain 1: 4500 -323.630 0.003 0.003 Chain 1: 4600 -323.709 0.003 0.002 Chain 1: 4700 -322.666 0.003 0.003 Chain 1: 4800 -323.402 0.003 0.002 Chain 1: 4900 -323.132 0.002 0.002 Chain 1: 5000 -322.777 0.002 0.002 Chain 1: 5100 -325.328 0.003 0.002 Chain 1: 5200 -323.601 0.003 0.002 Chain 1: 5300 -323.636 0.002 0.002 Chain 1: 5400 -324.520 0.002 0.002 Chain 1: 5500 -322.545 0.003 0.003 Chain 1: 5600 -323.077 0.003 0.003 Chain 1: 5700 -323.371 0.003 0.002 Chain 1: 5800 -323.277 0.003 0.002 Chain 1: 5900 -323.517 0.003 0.002 Chain 1: 6000 -322.372 0.003 0.003 Chain 1: 6100 -322.706 0.002 0.002 Chain 1: 6200 -322.975 0.002 0.001 Chain 1: 6300 -323.076 0.002 0.001 Chain 1: 6400 -323.473 0.002 0.001 Chain 1: 6500 -322.947 0.001 0.001 Chain 1: 6600 -322.783 0.001 0.001 MEDIAN ELBO CONVERGED Chain 1: Chain 1: Drawing a sample of size 10000 from the approximate posterior... Chain 1: COMPLETED. SAMPLING FOR MODEL 'VAR_LKJ' NOW (CHAIN 1). Chain 1: Chain 1: Gradient evaluation took 0.000298 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.98 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup) Chain 1: Iteration: 500 / 1000 [ 50%] (Warmup) Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling) Chain 1: Iteration: 1000 / 1000 [100%] (Sampling) Chain 1: Chain 1: Elapsed Time: 2.111 seconds (Warm-up) Chain 1: 1.43 seconds (Sampling) Chain 1: 3.541 seconds (Total) Chain 1: SAMPLING FOR MODEL 'VAR_wishart' NOW (CHAIN 1). Chain 1: Chain 1: Gradient evaluation took 0.000207 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.07 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup) Chain 1: Iteration: 500 / 1000 [ 50%] (Warmup) Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling) Chain 1: Iteration: 1000 / 1000 [100%] (Sampling) Chain 1: Chain 1: Elapsed Time: 1.737 seconds (Warm-up) Chain 1: 1.79 seconds (Sampling) Chain 1: 3.527 seconds (Total) Chain 1: SAMPLING FOR MODEL 'VAR_wishart' NOW (CHAIN 1). Chain 1: Chain 1: Gradient evaluation took 0.000206 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.06 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup) Chain 1: Iteration: 500 / 1000 [ 50%] (Warmup) Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling) Chain 1: Iteration: 1000 / 1000 [100%] (Sampling) Chain 1: Chain 1: Elapsed Time: 1.677 seconds (Warm-up) Chain 1: 1.61 seconds (Sampling) Chain 1: 3.287 seconds (Total) Chain 1: SAMPLING FOR MODEL 'VAR_LKJ_beep' NOW (CHAIN 1). Chain 1: Chain 1: Gradient evaluation took 0.000167 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.67 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup) Chain 1: Iteration: 500 / 1000 [ 50%] (Warmup) Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling) Chain 1: Iteration: 1000 / 1000 [100%] (Sampling) Chain 1: Chain 1: Elapsed Time: 1.389 seconds (Warm-up) Chain 1: 1.318 seconds (Sampling) Chain 1: 2.707 seconds (Total) Chain 1: SAMPLING FOR MODEL 'VAR_wishart_beep' NOW (CHAIN 1). Chain 1: Chain 1: Gradient evaluation took 0.000168 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.68 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup) Chain 1: Iteration: 500 / 1000 [ 50%] (Warmup) Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling) Chain 1: Iteration: 1000 / 1000 [100%] (Sampling) Chain 1: Chain 1: Elapsed Time: 1.488 seconds (Warm-up) Chain 1: 1.139 seconds (Sampling) Chain 1: 2.627 seconds (Total) Chain 1: SAMPLING FOR MODEL 'VAR_wishart' NOW (CHAIN 1). Chain 1: Chain 1: Gradient evaluation took 0.000215 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.15 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup) Chain 1: Iteration: 500 / 1000 [ 50%] (Warmup) Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling) Chain 1: Iteration: 1000 / 1000 [100%] (Sampling) Chain 1: Chain 1: Elapsed Time: 1.679 seconds (Warm-up) Chain 1: 1.521 seconds (Sampling) Chain 1: 3.2 seconds (Total) Chain 1: SAMPLING FOR MODEL 'VAR_LKJ' NOW (CHAIN 1). Chain 1: Chain 1: Gradient evaluation took 0.000197 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.97 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup) Chain 1: Iteration: 500 / 1000 [ 50%] (Warmup) Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling) Chain 1: Iteration: 1000 / 1000 [100%] (Sampling) Chain 1: Chain 1: Elapsed Time: 1.754 seconds (Warm-up) Chain 1: 1.305 seconds (Sampling) Chain 1: 3.059 seconds (Total) Chain 1: SAMPLING FOR MODEL 'VAR_wishart' NOW (CHAIN 1). Chain 1: Chain 1: Gradient evaluation took 0.000358 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.58 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup) Chain 1: Iteration: 500 / 1000 [ 50%] (Warmup) Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling) Chain 1: Iteration: 1000 / 1000 [100%] (Sampling) Chain 1: Chain 1: Elapsed Time: 9.058 seconds (Warm-up) Chain 1: 7.637 seconds (Sampling) Chain 1: 16.695 seconds (Total) Chain 1: [ FAIL 0 | WARN 14 | SKIP 0 | PASS 108 ] [ FAIL 0 | WARN 14 | SKIP 0 | PASS 108 ] > > proc.time() user system elapsed 99.78 1.07 100.87