CRAN Package Check Results for Package tergm

Last updated on 2025-12-16 14:50:18 CET.

Flavor	Version	T_install	T_check	T_total	Status	Flags
r-devel-linux-x86_64-debian-clang	4.2.2	20.36	460.46	480.82	OK
r-devel-linux-x86_64-debian-gcc	4.2.2	16.76	301.54	318.30	OK
r-devel-linux-x86_64-fedora-clang	4.2.2	36.00	747.62	783.62	OK
r-devel-linux-x86_64-fedora-gcc	4.2.2	45.00	708.08	753.08	OK
r-devel-windows-x86_64	4.2.2	31.00	272.00	303.00	OK	--no-vignettes
r-patched-linux-x86_64	4.2.2	27.51	432.74	460.25	OK
r-release-linux-x86_64	4.2.2	23.60	433.04	456.64	OK
r-release-macos-arm64	4.2.2				OK
r-release-macos-x86_64	4.2.2	12.00	233.00	245.00	OK
r-release-windows-x86_64	4.2.2	30.00	293.00	323.00	OK	--no-vignettes
r-oldrel-macos-arm64	4.2.2				OK
r-oldrel-macos-x86_64	4.2.2	13.00	238.00	251.00	OK
r-oldrel-windows-x86_64	4.2.2	39.00	299.00	338.00	ERROR	--no-vignettes

Check Details

Version: 4.2.2
Flags: --no-vignettes
Check: tests
Result: ERROR Running 'degree.mean.age.R' [9s] Running 'dynamic_EGMME.R' [0s] Running 'dynamic_MLE_blockdiag.R' [0s] Running 'dynamic_MLE_blockdiag.bipartite.R' [0s] Running 'sim_gf_sum.R' [8s] Running 'simulate_networkDynamic.R' [6s] Running 'tergm_offset_tests.R' [0s] Running 'tergm_parallel.R' [0s] Running 'testthat.R' [108s] Running the tests in 'tests/testthat.R' failed. Complete output: > # File tests/testthat.R in package tergm, part of the Statnet suite of > # packages for network analysis, https://statnet.org . > # > # This software is distributed under the GPL-3 license. It is free, open > # source, and has the attribution requirements (GPL Section 7) at > # https://statnet.org/attribution . > # > # Copyright 2008-2025 Statnet Commons > ################################################################################ > > require(testthat) Loading required package: testthat > require(tergm) Loading required package: tergm Loading required package: ergm Loading required package: network 'network' 1.19.0 (2024-12-08), part of the Statnet Project * 'news(package="network")' for changes since last version * 'citation("network")' for citation information * 'https://statnet.org' for help, support, and other information 'ergm' 4.10.1 (2025-08-26), part of the Statnet Project * 'news(package="ergm")' for changes since last version * 'citation("ergm")' for citation information * 'https://statnet.org' for help, support, and other information 'ergm' 4 is a major update that introduces some backwards-incompatible changes. Please type 'news(package="ergm")' for a list of major changes. Loading required package: networkDynamic 'networkDynamic' 0.11.5 (2024-11-21), part of the Statnet Project * 'news(package="networkDynamic")' for changes since last version * 'citation("networkDynamic")' for citation information * 'https://statnet.org' for help, support, and other information Registered S3 method overwritten by 'tergm': method from simulate_formula.network ergm 'tergm' 4.2.2 (2025-06-15), part of the Statnet Project * 'news(package="tergm")' for changes since last version * 'citation("tergm")' for citation information * 'https://statnet.org' for help, support, and other information Attaching package: 'tergm' The following object is masked from 'package:ergm': snctrl > > test_check("tergm") Starting 2 test processes. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0014. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0018. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0002. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0018. > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0005. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0031. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: The log-likelihood improved by 0.0007. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0042. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by < 0.0001. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: 1 > test-CMLE-2-bip.R: Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0003. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0002. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by < 0.0001. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0033. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-dir.R: Convergence test p-value: 0.0002. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-dir.R: 1 > test-CMLE-2-dir.R: Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0063. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-dir.R: 1 > test-CMLE-2-dir.R: Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0026. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-dir.R: 1 > test-CMLE-2-dir.R: Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0006. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: 1 > test-CMLE-2-bip.R: Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0003. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0001. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0031. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0075. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: 1 > test-CMLE-2-dir.R: Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0008. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: The log-likelihood improved by 0.0001. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-dir.R: 1 > test-CMLE-2-dir.R: Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by < 0.0001. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0018. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: 1 > test-CMLE-2-dir.R: Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0013. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0008. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: 1 > test-CMLE-2-dir.R: Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0028. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-bip.R: 1 > test-CMLE-2-bip.R: Optimizing with step length 1.0000. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0010. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: 1 > test-CMLE-2-dir.R: Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0110. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by < 0.0001. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0145. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: 1 > test-CMLE-2-dir.R: Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0034. > test-CMLE-2-und.R: 1 > test-CMLE-2-und.R: Optimizing with step length 1.0000. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: The log-likelihood improved by 0.0038. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0003. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0024. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: 1 > test-CMLE-2-und.R: Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0010. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: The log-likelihood improved by 0.0004. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: Convergence test p-value: 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0001. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: 1 > test-CMLE-bip.R: Optimizing with step length 1.0000. > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-bip.R: The log-likelihood improved by 0.0015. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0021. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0107. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0051. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0007. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0020. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-bip.R: 1 > test-CMLE-bip.R: Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0029. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0004. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0049. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0018. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0025. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0005. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0005. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0005. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0033. > test-CMLE-bip.R: Convergence test p-value: 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: 1 > test-CMLE-2-und.R: Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0016. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0004. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0005. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0008. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0095. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0012. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0002. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-bip.R: 1 > test-CMLE-bip.R: Optimizing with step length 1.0000. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-bip.R: The log-likelihood improved by 0.0081. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-bip.R: Convergence test p-value: 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0014. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0005. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-dir.R: 1 > test-CMLE-dir.R: Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by 0.0108. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0004. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: The log-likelihood improved by < 0.0001. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by 0.0003. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by 0.0044. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by 0.0001. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Finished MPLE. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by 0.0002. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-und.R: Finished MPLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-und.R: Finished MPLE. > test-CMLE-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by 0.0005. > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by 0.0021. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0062. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0020. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0001. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0006. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0016. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0003. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0121. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: 1 > test-CMLE-dir.R: Optimizing with step length 1.0000. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-dir.R: The log-likelihood improved by 0.0173. > test-CMLE-dir.R: Convergence test p-value: 0.0002. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: 1 > test-CMLE-und.R: Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0001. > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by 0.0002. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by < 0.0001. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by < 0.0001. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by 0.0044. > test-CMLE-dir.R: Convergence test p-value: 0.0001. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Model statistics 'Persist(1)~edges' are not varying. This may indicate that the observed data occupies an extreme point in the sample space or that the estimation has reached a dead-end configuration. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-und.R: Post-burnin sample is constant; returning. > test-CMLE-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0002. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by 0.0008. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0013. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: The log-likelihood improved by 0.0042. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by 0.0002. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0041. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-EGMME-errors.R: Targets contains offset statistics; they will only be used during the SAN run, and removal of the offset statistics will be attempted for the EGMME targets. > test-CMLE-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0124. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-EGMME-errors.R: Targets contains offset statistics; they will only be used during the SAN run, and removal of the offset statistics will be attempted for the EGMME targets. > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-und.R: Model statistics 'Persist(1)~edges' are not varying. This may indicate that the observed data occupies an extreme point in the sample space or that the estimation has reached a dead-end configuration. > test-CMLE-und.R: Post-burnin sample is constant; returning. > test-CMLE-und.R: 1 > test-CMLE-und.R: Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0007. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0124. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE): > test-EGMME-initialfit.R: Obtaining the responsible dyads. > test-EGMME-initialfit.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: Maximizing the pseudolikelihood. > test-EGMME-initialfit.R: Finished MPLE. > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE): > test-EGMME-initialfit.R: Obtaining the responsible dyads. > test-EGMME-initialfit.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: Maximizing the pseudolikelihood. > test-EGMME-initialfit.R: Finished MPLE. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE): > test-EGMME-initialfit.R: Obtaining the responsible dyads. > test-EGMME-initialfit.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: Maximizing the pseudolikelihood. > test-EGMME-initialfit.R: Finished MPLE. > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE): > test-EGMME-initialfit.R: Obtaining the responsible dyads. > test-EGMME-initialfit.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: Maximizing the pseudolikelihood. > test-EGMME-initialfit.R: Finished MPLE. > test-CMLE-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0040. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-EGMME-initialfit.R: Unable to match target stats. Using MCMLE estimation. > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE): > test-EGMME-initialfit.R: Obtaining the responsible dyads. > test-EGMME-initialfit.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: Maximizing the pseudolikelihood. > test-EGMME-initialfit.R: Finished MPLE. > test-EGMME-initialfit.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-EGMME-initialfit.R: Iteration 1 of at most 60: > test-CMLE-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0039. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-EGMME-simple.R: Initializing unconstrained Metropolis-Hastings proposal: > test-EGMME-simple.R: 'ergm:MH_SPDyad'. > test-EGMME-simple.R: Initializing model... > test-EGMME-simple.R: Model initialized. > test-EGMME-simple.R: Starting 4 SAN iterations of 524288 steps each. > test-EGMME-simple.R: #1 of 4: > test-EGMME-simple.R: SAN Metropolis-Hastings accepted 75.742% of 32768 proposed steps. > test-EGMME-simple.R: SAN summary statistics: > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: 6.9 > test-EGMME-simple.R: Meanstats Goal: > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: 10 > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats = > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: -3.1 > test-EGMME-simple.R: New statistics scaling = > test-EGMME-simple.R: [1] 1 > test-EGMME-simple.R: Scaled Mahalanobis distance = 9.61000000000013 > test-EGMME-simple.R: #2 of 4: > test-EGMME-initialfit.R: 1 Optimizing with step length 1.0000. > test-EGMME-initialfit.R: The log-likelihood improved by 0.0157. > test-EGMME-simple.R: SAN Metropolis-Hastings accepted 66.619% of 69632 proposed steps. > test-EGMME-simple.R: SAN summary statistics: > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: 8.4 > test-EGMME-simple.R: Meanstats Goal: > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: 10 > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats = > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: -1.6 > test-EGMME-simple.R: New statistics scaling = > test-EGMME-simple.R: [1] 1 > test-EGMME-simple.R: Scaled Mahalanobis distance = 2.56000000000006 > test-EGMME-simple.R: #3 of 4: > test-EGMME-initialfit.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-EGMME-initialfit.R: Finished MCMLE. > test-EGMME-initialfit.R: This model was fit using MCMC. To examine model diagnostics and check > test-EGMME-initialfit.R: for degeneracy, use the mcmc.diagnostics() function. > test-EGMME-simple.R: SAN Metropolis-Hastings accepted 61.490% of 139264 proposed steps. > test-EGMME-simple.R: SAN summary statistics: > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: 9.7 > test-EGMME-simple.R: Meanstats Goal: > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: 10 > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats = > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: -0.3 > test-EGMME-simple.R: New statistics scaling = > test-EGMME-simple.R: [1] 1 > test-EGMME-simple.R: Scaled Mahalanobis distance = 0.0900000000000113 > test-EGMME-simple.R: #4 of 4: > test-EGMME-initialfit.R: Unable to match target stats. Using MCMLE estimation. > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE): > test-EGMME-initialfit.R: Obtaining the responsible dyads. > test-EGMME-initialfit.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: Maximizing the pseudolikelihood. > test-EGMME-initialfit.R: Finished MPLE. > test-EGMME-initialfit.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-EGMME-initialfit.R: Iteration 1 of at most 60: > test-EGMME-simple.R: SAN Metropolis-Hastings accepted 0.000% of 278528 proposed steps. > test-EGMME-simple.R: SAN summary statistics: > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: 10 > test-EGMME-simple.R: Meanstats Goal: > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: 10 > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats = > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: -1.879052e-14 > test-EGMME-simple.R: New statistics scaling = > test-EGMME-simple.R: [1] 1 > test-EGMME-simple.R: Scaled Mahalanobis distance = 3.53083818192423e-28 > test-EGMME-simple.R: Initializing Metropolis-Hastings proposal. > test-EGMME-simple.R: Constructing an approximate response network. > test-EGMME-simple.R: Starting 4 SAN iterations of 80000 steps each. > test-EGMME-simple.R: #1 of 4: > test-EGMME-simple.R: SAN Metropolis-Hastings accepted 59.619% of 4096 proposed steps. > test-EGMME-simple.R: SAN summary statistics: > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 11 1073741824 > test-EGMME-simple.R: Meanstats Goal: > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 10 10 > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats = > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 1 1073741814 > test-EGMME-simple.R: New statistics scaling = > test-EGMME-simple.R: [1] 0.5 0.5 > test-EGMME-simple.R: Scaled Mahalanobis distance = 576460741566005312 > test-EGMME-simple.R: #2 of 4: > test-EGMME-simple.R: SAN Metropolis-Hastings accepted 31.152% of 8192 proposed steps. > test-EGMME-simple.R: SAN summary statistics: > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 10 1073741824 > test-EGMME-simple.R: Meanstats Goal: > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 10 10 > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats = > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 0 1073741814 > test-EGMME-simple.R: New statistics scaling = > test-EGMME-simple.R: [1] 0.5 0.5 > test-EGMME-simple.R: Scaled Mahalanobis distance = 576460741566005312 > test-EGMME-simple.R: #3 of 4: > test-EGMME-simple.R: SAN Metropolis-Hastings accepted 2.061% of 20480 proposed steps. > test-EGMME-simple.R: SAN summary statistics: > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 10 1073741824 > test-EGMME-simple.R: Meanstats Goal: > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 10 10 > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats = > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 0 1073741814 > test-EGMME-simple.R: New statistics scaling = > test-EGMME-simple.R: [1] 0.5 0.5 > test-EGMME-simple.R: Scaled Mahalanobis distance = 576460741566005312 > test-EGMME-simple.R: #4 of 4: > test-EGMME-simple.R: SAN Metropolis-Hastings accepted 0.000% of 40960 proposed steps. > test-EGMME-simple.R: SAN summary statistics: > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 10 1073741824 > test-EGMME-simple.R: Meanstats Goal: > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 10 10 > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats = > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 0 1073741814 > test-EGMME-simple.R: New statistics scaling = > test-EGMME-simple.R: [1] 0.5 0.5 > test-EGMME-simple.R: Scaled Mahalanobis distance = 576460741566005312 > test-EGMME-simple.R: SAN summary statistics: > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 10 1073741824 > test-EGMME-simple.R: Meanstats Goal: > test-EGMME-simple.R: [1] 10 10 > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats = > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 0 1073741814 > test-EGMME-simple.R: Fitting TERGM Equilibrium GMME. > test-EGMME-simple.R: Starting optimization with with coef_0 = ( -2.94443897916644 1 ). > test-EGMME-simple.R: ======== Phase 1: Burn in, get initial gradient values, and find a configuration under which all targets vary. ======== > test-EGMME-simple.R: Burning in... > test-EGMME-initialfit.R: 1 > test-EGMME-initialfit.R: Optimizing with step length 1.0000. > test-EGMME-initialfit.R: The log-likelihood improved by 0.0157. > test-EGMME-initialfit.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-EGMME-initialfit.R: Finished MCMLE. > test-EGMME-initialfit.R: This model was fit using MCMC. To examine model diagnostics and check > test-EGMME-initialfit.R: for degeneracy, use the mcmc.diagnostics() function. > test-EGMME-initialfit.R: Unable to match target stats. Using MCMLE estimation. > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE): > test-EGMME-initialfit.R: Obtaining the responsible dyads. > test-EGMME-initialfit.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: Maximizing the pseudolikelihood. > test-EGMME-initialfit.R: Finished MPLE. > test-EGMME-initialfit.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-EGMME-initialfit.R: Iteration 1 of at most 60: > test-EGMME-initialfit.R: 1 Optimizing with step length 1.0000. > test-EGMME-initialfit.R: The log-likelihood improved by 0.0157. > test-EGMME-initialfit.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-EGMME-initialfit.R: Finished MCMLE. > test-EGMME-initialfit.R: This model was fit using MCMC. To examine model diagnostics and check > test-EGMME-initialfit.R: for degeneracy, use the mcmc.diagnostics() function. > test-EGMME-initialfit.R: Unable to match target stats. Using MCMLE estimation. > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE): > test-EGMME-initialfit.R: Obtaining the responsible dyads. > test-EGMME-initialfit.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: Maximizing the pseudolikelihood. > test-EGMME-initialfit.R: Finished MPLE. > test-EGMME-initialfit.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-EGMME-initialfit.R: Iteration 1 of at most 60: > test-EGMME-simple.R: Returned from STERGM burnin > test-EGMME-simple.R: Done. > test-EGMME-simple.R: ======== Attempt 1 ======== > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-initialfit.R: 1 > test-EGMME-initialfit.R: Optimizing with step length 1.0000. > test-EGMME-initialfit.R: The log-likelihood improved by 0.0157. > test-EGMME-initialfit.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-EGMME-initialfit.R: Finished MCMLE. > test-EGMME-initialfit.R: This model was fit using MCMC. To examine model diagnostics and check > test-EGMME-initialfit.R: for degeneracy, use the mcmc.diagnostics() function. > test-EGMME-initialfit.R: Unable to match target stats. Using MCMLE estimation. > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE): > test-EGMME-initialfit.R: Obtaining the responsible dyads. > test-EGMME-initialfit.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: Maximizing the pseudolikelihood. > test-EGMME-initialfit.R: Finished MPLE. > test-EGMME-initialfit.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-EGMME-initialfit.R: Iteration 1 of at most 60: > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-initialfit.R: 1 > test-EGMME-initialfit.R: Optimizing with step length 1.0000. > test-EGMME-initialfit.R: The log-likelihood improved by 0.0157. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-initialfit.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-EGMME-initialfit.R: Finished MCMLE. > test-EGMME-initialfit.R: This model was fit using MCMC. To examine model diagnostics and check > test-EGMME-initialfit.R: for degeneracy, use the mcmc.diagnostics() function. > test-EGMME-initialfit.R: Unable to match target stats. Using MCMLE estimation. > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE): > test-EGMME-initialfit.R: Obtaining the responsible dyads. > test-EGMME-initialfit.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: Maximizing the pseudolikelihood. > test-EGMME-initialfit.R: Finished MPLE. > test-EGMME-initialfit.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-EGMME-initialfit.R: Iteration 1 of at most 60: > test-EGMME-initialfit.R: 1 > test-EGMME-initialfit.R: Optimizing with step length 1.0000. > test-EGMME-initialfit.R: The log-likelihood improved by 0.0157. > test-EGMME-initialfit.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-EGMME-initialfit.R: Finished MCMLE. > test-EGMME-initialfit.R: This model was fit using MCMC. To examine model diagnostics and check > test-EGMME-initialfit.R: for degeneracy, use the mcmc.diagnostics() function. > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: Starting maximum pseudolikelihood estimation (MPLE): > test-basis.R: Obtaining the responsible dyads. > test-basis.R: Evaluating the predictor and response matrix. > test-basis.R: Maximizing the pseudolikelihood. > test-basis.R: Finished MPLE. > test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-basis.R: Iteration 1 of at most 60: > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 1.2158. > test-basis.R: Estimating equations are not within tolerance region. > test-basis.R: Iteration 2 of at most 60: > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: All parameters have some effect and all statistics are moving. Proceeding to Phase 2. > test-EGMME-simple.R: ======== Phase 2: Find and refine the estimate. ======== > test-EGMME-simple.R: ======== Subphase 1 ======== > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0687. > test-basis.R: Convergence test p-value: 0.0280. Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 3 of at most 60: > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -3.535745 1.475307 > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-EGMME-simple.R: Estimating equations = 0 p-value: 1.3914799890544e-110 , trending: 3.27131906336549e-08 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: The log-likelihood improved by 0.0837. > test-basis.R: Convergence test p-value: 0.6094. Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 4 of at most 60: > test-EGMME-simple.R: Finished. Extracting. > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0656. > test-basis.R: Convergence test p-value: 0.5885. Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 5 of at most 60: > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -4.118841 1.802270 > test-EGMME-simple.R: Estimating equations = 0 p-value: 3.01245591281223e-42 , trending: 3.81048849286777e-19 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -4.930926 2.325456 > test-EGMME-simple.R: Estimating equations = 0 p-value: 5.01298005644045e-24 , trending: 5.10804229077213e-13 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0914. > test-basis.R: Convergence test p-value: 0.2077. Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 6 of at most 60: > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.280278 2.102755 > test-EGMME-simple.R: Estimating equations = 0 p-value: 3.60431227021322e-07 , trending: 4.19606113727738e-13 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.068490 2.059625 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.00114190507638694 , trending: 5.57325010969205e-19 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.226398 2.400553 > test-EGMME-simple.R: Estimating equations = 0 p-value: 7.02401736927543e-05 , trending: 7.5737719187006e-11 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0096. > test-basis.R: Convergence test p-value: 0.0031. Converged with 99% confidence. > test-basis.R: Finished MCMLE. > test-basis.R: Evaluating log-likelihood at the estimate. > test-EGMME-simple.R: Finished. Extracting. > test-basis.R: Fitting the dyad-independent submodel... > test-basis.R: Bridging between the dyad-independent submodel and the full model... > test-basis.R: Setting up bridge sampling... > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.106173 2.441485 > test-basis.R: Using 16 bridges: 1 > test-basis.R: 2 > test-basis.R: 3 > test-basis.R: 4 > test-basis.R: 5 > test-basis.R: 6 > test-basis.R: 7 > test-basis.R: 8 > test-basis.R: 9 > test-basis.R: 10 > test-basis.R: 11 > test-basis.R: 12 > test-basis.R: 13 > test-basis.R: 14 > test-basis.R: 15 > test-basis.R: 16 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.00583560731513221 , trending: 3.11134253124785e-11 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: . > test-basis.R: Bridging finished. > test-basis.R: > test-basis.R: This model was fit using MCMC. To examine model diagnostics and check > test-basis.R: for degeneracy, use the mcmc.diagnostics() function. > test-basis.R: Starting maximum pseudolikelihood estimation (MPLE): > test-basis.R: Obtaining the responsible dyads. > test-basis.R: Evaluating the predictor and response matrix. > test-basis.R: Maximizing the pseudolikelihood. > test-basis.R: Finished MPLE. > test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-basis.R: Iteration 1 of at most 60: > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.633203 2.130450 > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-EGMME-simple.R: Estimating equations = 0 p-value: 6.22153092853623e-05 , trending: 6.06750743355267e-07 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: The log-likelihood improved by 1.2158. > test-basis.R: Estimating equations are not within tolerance region. > test-basis.R: Iteration 2 of at most 60: > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0687. > test-basis.R: Convergence test p-value: 0.0280. Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 3 of at most 60: > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.330221 2.182673 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.00496616862578934 , trending: 2.60795493515843e-16 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0837. > test-basis.R: Convergence test p-value: 0.6094. Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 4 of at most 60: > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.268428 2.343567 > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0656. > test-basis.R: Convergence test p-value: 0.5885. Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 5 of at most 60: > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.963218136606521 , trending: 0.0671395053447981 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 4 / 5 to go. > test-EGMME-simple.R: ======== Subphase 2 ======== > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.224301 2.272558 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.878641316766649 , trending: 0.0564044565523399 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 4 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0914. > test-basis.R: Convergence test p-value: 0.2077. Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 6 of at most 60: > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.157947 2.304233 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.828767985605079 , trending: 0.038176878635704 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 3 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.097439 2.306586 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.726377956354663 , trending: 0.00550122868318379 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.18524 2.07143 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.688236715199918 , trending: 0.000244622114012557 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.165517 2.377492 > test-basis.R: 1 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.887135556274084 , trending: 0.00435733865560731 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0096. > test-basis.R: Convergence test p-value: 0.0031. Converged with 99% confidence. > test-basis.R: Finished MCMLE. > test-basis.R: Evaluating log-likelihood at the estimate. > test-basis.R: Fitting the dyad-independent submodel... > test-basis.R: Bridging between the dyad-independent submodel and the full model... > test-basis.R: Setting up bridge sampling... > test-EGMME-simple.R: Finished. Extracting. > test-basis.R: Using 16 bridges: 1 > test-basis.R: 2 > test-basis.R: 3 > test-basis.R: 4 > test-basis.R: 5 > test-basis.R: 6 > test-basis.R: 7 > test-basis.R: 8 > test-basis.R: 9 > test-basis.R: 10 > test-basis.R: 11 > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.082794 2.227639 > test-basis.R: 12 > test-basis.R: 13 > test-basis.R: 14 > test-basis.R: 15 > test-basis.R: 16 > test-basis.R: . > test-basis.R: Bridging finished. > test-basis.R: > test-basis.R: This model was fit using MCMC. To examine model diagnostics and check > test-basis.R: for degeneracy, use the mcmc.diagnostics() function. > test-basis.R: Starting maximum pseudolikelihood estimation (MPLE): > test-basis.R: Obtaining the responsible dyads. > test-basis.R: Evaluating the predictor and response matrix. > test-basis.R: Maximizing the pseudolikelihood. > test-basis.R: Finished MPLE. > test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-basis.R: Iteration 1 of at most 60: > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.746906892213617 , trending: 0.00984507486438503 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.332418 2.184492 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.72903979754815 , trending: 0.143800594218699 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 4 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 1.2158. > test-basis.R: Estimating equations are not within tolerance region. > test-basis.R: Iteration 2 of at most 60: > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.204873 2.165226 > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0687. > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.915517851686919 , trending: 0.0308422566828079 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 3 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: Convergence test p-value: 0.0280. Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 3 of at most 60: > test-EGMME-simple.R: Finished. Extracting. > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.251966 1.944937 > test-basis.R: The log-likelihood improved by 0.0837. > test-basis.R: Convergence test p-value: 0.6094. Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 4 of at most 60: > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.849842619342716 , trending: 0.156756643900556 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 2 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0656. > test-basis.R: Convergence test p-value: 0.5885. Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 5 of at most 60: > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.108031 2.223334 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.898419792326419 , trending: 0.0787005201225524 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 1 / 5 to go. > test-EGMME-simple.R: Approximate standard error of the estimate: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: 0.4922024 0.5499035 > test-EGMME-simple.R: Approximate standard error of window means: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: 0.01975644 0.01576257 > test-EGMME-simple.R: par. var. / (std. var. + par. var.): > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: 0.0016085363 0.0008209645 > test-EGMME-simple.R: Local nonlinearity p-value: 0.0948548594075957 > test-EGMME-simple.R: There is evidence of local nonlinearity. Continuing. > test-EGMME-simple.R: ======== Subphase 3 ======== > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.104021 2.255011 > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0914. > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.852201823894968 , trending: 0.0481180630546847 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 4 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: Convergence test p-value: 0.2077. Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 6 of at most 60: > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.143689 2.200477 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.865672941020689 , trending: 0.219231451535762 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 3 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.151976 2.173453 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.811071100224124 , trending: 0.528424223198149 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 2 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.139175 2.102897 > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0096. > test-basis.R: Convergence test p-value: 0.0031. Converged with 99% confidence. > test-basis.R: Finished MCMLE. > test-basis.R: Evaluating log-likelihood at the estimate. > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.947368135956696 , trending: 0.535566962489831 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 1 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: Fitting the dyad-independent submodel... > test-basis.R: Bridging between the dyad-independent submodel and the full model... > test-basis.R: Setting up bridge sampling... > test-basis.R: Using 16 bridges: 1 > test-basis.R: 2 > test-basis.R: 3 > test-basis.R: 4 > test-basis.R: 5 > test-basis.R: 6 > test-basis.R: 7 > test-basis.R: 8 > test-basis.R: 9 > test-basis.R: 10 > test-basis.R: 11 > test-basis.R: 12 > test-basis.R: 13 > test-basis.R: 14 > test-basis.R: 15 > test-basis.R: 16 > test-basis.R: . > test-basis.R: Bridging finished. > test-basis.R: > test-basis.R: This model was fit using MCMC. To examine model diagnostics and check > test-basis.R: for degeneracy, use the mcmc.diagnostics() function. > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.175996 2.148141 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.974056708436821 , trending: 0.744214575272452 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and neither they nor the parameters exhibit a significant trend. Reducing gain. > test-EGMME-simple.R: Approximate standard error of the estimate: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: 0.3104041 0.5135670 > test-EGMME-simple.R: Approximate standard error of window means: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: 0.01014973 0.01687378 > test-EGMME-simple.R: par. var. / (std. var. + par. var.): > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: 0.001068046 0.001078356 > test-EGMME-simple.R: Local nonlinearity p-value: 0.720167289336991 > test-EGMME-simple.R: EGMME does not appear to be estimated with sufficient prescision. Continuing. > test-EGMME-simple.R: ======== Subphase 4 ======== > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.231970 2.259602 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.982532758325356 , trending: 0.605756452659413 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 4 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.185316 2.237948 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.974448108052989 , trending: 0.545326240721661 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 3 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.244654 2.173530 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.936352885655037 , trending: 0.968030111947793 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 2 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.138225 2.248962 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.995431953517572 , trending: 0.433599476477221 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 1 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.153525 2.245627 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.986594407698035 , trending: 0.478569355659096 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and neither they nor the parameters exhibit a significant trend. Reducing gain. > test-EGMME-simple.R: Approximate standard error of the estimate: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: 0.3338714 0.4219504 > test-EGMME-simple.R: Approximate standard error of window means: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: 0.01006127 0.01301498 > test-EGMME-simple.R: par. var. / (std. var. + par. var.): > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: 0.0009073040 0.0009504983 > test-EGMME-simple.R: Local nonlinearity p-value: 0.778429922249166 > test-EGMME-simple.R: Maximum number of gain levels exceeded. Stopping.Refining the estimate using the mean method. New estimate: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.171135 2.188753 > test-EGMME-simple.R: ======== Phase 3: Simulate from the fit and estimate standard errors. ======== > test-EGMME-simple.R: Evaluating target statistics at the estimate. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: Finished. > test-EGMME-simple.R: Estimating equation = 0 p-value: 0.798393159978643 > test-EGMME-simple.R: Maximum number of gain levels exceeded. Stopping. > test-EGMME-simple.R: Call: > test-EGMME-simple.R: tergm(formula = g1 ~ Form(~edges) + Persist(~edges), constraints = ~., > test-EGMME-simple.R: target.stats = target.stats[-3], estimate = "EGMME", control = control.tergm(SA.plot.progress = do.plot, > test-EGMME-simple.R: SA.phase2.levels.min = 2, SA.phase2.levels.max = 4, SA.phase2.repeats = 10, > test-EGMME-simple.R: SA.restart.on.err = FALSE, init = c(-log(0.95/0.05), > test-EGMME-simple.R: 1)), verbose = TRUE, targets = ~edges + mean.age) > test-EGMME-simple.R: > test-EGMME-simple.R: Gradient Descent Equilibrium Generalized Method of Moments Results Results: > test-EGMME-simple.R: > test-EGMME-simple.R: Estimate Std. Error MCMC % z value Pr(>|z|) > test-EGMME-simple.R: Form~edges -5.1711 0.3348 0 -15.447 <1e-04 *** > test-EGMME-simple.R: Persist~edges 2.1888 0.4104 0 5.333 <1e-04 *** > test-EGMME-simple.R: --- > test-EGMME-simple.R: Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > test-EGMME-simple.R: > test-EGMME-simple.R: Sample statistics summary: > test-EGMME-simple.R: > test-EGMME-simple.R: Iterations = 21:5270 > test-EGMME-simple.R: Thinning interval = 1 > test-EGMME-simple.R: Number of chains = 1 > test-EGMME-simple.R: Sample size per chain = 5250 > test-EGMME-simple.R: > test-EGMME-simple.R: 1. Empirical mean and standard deviation for each variable, > test-EGMME-simple.R: plus standard error of the mean: > test-EGMME-simple.R: > test-EGMME-simple.R: Mean SD Naive SE Time-series SE > test-EGMME-simple.R: edges 0.04171 3.150 0.04348 0.1888 > test-EGMME-simple.R: mean.age -0.08878 3.178 0.04385 0.1920 > test-EGMME-simple.R: > test-EGMME-simple.R: 2. Quantiles for each variable: > test-EGMME-simple.R: > test-EGMME-simple.R: 2.5% 25% 50% 75% 97.5% > test-EGMME-simple.R: edges -6.00 -2.0 0.0000 2.000 7 > test-EGMME-simple.R: mean.age -5.49 > test-EGMME-simple.R: -2.2 -0.4286 1.778 7 > test-EGMME-simple.R: > test-EGMME-simple.R: > test-EGMME-simple.R: Are sample statistics significantly different from observed? > test-EGMME-simple.R: edges mean.age (Omni) > test-EGMME-simple.R: diff. 0.04171429 -0.08877985 NA > test-EGMME-simple.R: test stat. 0.22090947 -0.46228617 0.4520099 > test-EGMME-simple.R: P-val. 0.82516293 0.64387611 0.7983932 > test-EGMME-simple.R: > test-EGMME-simple.R: Sample statistics cross-correlations: > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: edges 1.00000000 -0.03095688 > test-EGMME-simple.R: mean.age -0.03095688 1.00000000 > test-EGMME-simple.R: > test-EGMME-simple.R: Sample statistics auto-correlation: > test-EGMME-simple.R: Chain 1 > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: Lag 0 1.0000000 1.0000000 > test-EGMME-simple.R: Lag 1 0.8992875 0.8840140 > test-EGMME-simple.R: Lag 2 0.8095137 0.7907080 > test-EGMME-simple.R: Lag 3 0.7277822 0.7070889 > test-EGMME-simple.R: Lag 4 0.6590848 0.6408322 > test-EGMME-simple.R: Lag 5 0.5975276 0.5821078 > test-EGMME-simple.R: > test-EGMME-simple.R: Sample statistics burn-in diagnostic (Geweke): > test-EGMME-simple.R: Chain 1 > test-EGMME-simple.R: > test-EGMME-simple.R: Fraction in 1st window = 0.1 > test-EGMME-simple.R: Fraction in 2nd window = 0.5 > test-EGMME-simple.R: > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: -0.3064371 -0.3217347 > test-EGMME-simple.R: > test-EGMME-simple.R: Individual P-values (lower = worse): > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 0.7592719 0.7476537 > test-EGMME-simple.R: Joint P-value (lower = worse): 0.943036 > test-EGMME-simple.R: > test-EGMME-simple.R: Note: MCMC diagnostics shown here are from the last round of > test-EGMME-simple.R: simulation, prior to computation of final parameter estimates. > test-EGMME-simple.R: Because the final estimates are refinements of those used for this > test-EGMME-simple.R: simulation run, these diagnostics may understate model performance. > test-EGMME-simple.R: To directly assess the performance of the final model on in-model > test-EGMME-simple.R: statistics, please use the GOF command: gof(ergmFitObject, > test-EGMME-simple.R: GOF=~model). > test-EGMME-simple.R: > test-networkLite.R: Loading required package: networkLite > test-nwelt.R: Edge activity in base.net was ignored > test-nwelt.R: Created net.obs.period to describe network > test-nwelt.R: Network observation period info: > test-nwelt.R: Number of observation spells: 1 > test-nwelt.R: Maximal time range observed: -Inf until Inf > test-nwelt.R: Temporal mode: discrete > test-nwelt.R: Time unit: step > test-nwelt.R: Suggested time increment: 1 > test-nwelt.R: Edge activity in base.net was ignored > test-nwelt.R: Created net.obs.period to describe network > test-nwelt.R: Network observation period info: > test-nwelt.R: Number of observation spells: 1 > test-nwelt.R: Maximal time range observed: -Inf until Inf > test-nwelt.R: Temporal mode: discrete > test-nwelt.R: Time unit: step > test-nwelt.R: Suggested time increment: 1 > test-nwelt.R: Edge activity in base.net was ignored > test-nwelt.R: Created net.obs.period to describe network > test-nwelt.R: Network observation period info: > test-nwelt.R: Number of observation spells: 1 > test-nwelt.R: Maximal time range observed: -Inf until Inf > test-nwelt.R: Temporal mode: discrete > test-nwelt.R: Time unit: step > test-nwelt.R: Suggested time increment: 1 > test-nwelt.R: Created net.obs.period to describe network > test-nwelt.R: Network observation period info: > test-nwelt.R: Number of observation spells: 1 > test-nwelt.R: Maximal time range observed: 1 until Inf > test-nwelt.R: Temporal mode: discrete > test-nwelt.R: Time unit: step > test-nwelt.R: Suggested time increment: 1 > test-simulate.R: simulate.tergm test(s) skipped. Set ENABLE_statnet_TESTS environment variable to run. > test-term-EdgeAges.R: Error: ! testthat subprocess exited in file 'test-term-EdgeAges.R'. Caused by error: ! R session crashed with exit code -1073741819 Backtrace: ▆ 1. └─testthat::test_check("tergm") 2. └─testthat::test_dir(...) 3. └─testthat:::test_files(...) 4. └─testthat:::test_files_parallel(...) 5. ├─withr::with_dir(...) 6. │ └─base::force(code) 7. ├─testthat::with_reporter(...) 8. │ └─base::tryCatch(...) 9. │ └─base (local) tryCatchList(expr, classes, parentenv, handlers) 10. │ └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]]) 11. │ └─base (local) doTryCatch(return(expr), name, parentenv, handler) 12. └─testthat:::parallel_event_loop_chunky(queue, reporters, ".") 13. └─queue$poll(Inf) 14. └─base::lapply(...) 15. └─testthat (local) FUN(X[[i]], ...) 16. └─private$handle_error(msg, i) 17. └─cli::cli_abort(...) 18. └─rlang::abort(...) Execution halted Flavor: r-oldrel-windows-x86_64