CRAN Package Check Results for Package ergm.ego

Last updated on 2026-02-28 11:49:22 CET.

Flavor	Version	T_install	T_check	T_total	Status
r-devel-linux-x86_64-debian-clang	1.1.3	19.14	259.59	278.73	ERROR
r-devel-linux-x86_64-debian-gcc	1.1.3	15.28	174.57	189.85	ERROR
r-devel-linux-x86_64-fedora-clang	1.1.3	34.00	427.89	461.89	ERROR
r-devel-linux-x86_64-fedora-gcc	1.1.3	35.00	396.50	431.50	ERROR
r-devel-macos-arm64	1.1.3	5.00	55.00	60.00	OK
r-devel-windows-x86_64	1.1.3	24.00	204.00	228.00	ERROR
r-patched-linux-x86_64	1.1.3	20.75	240.42	261.17	ERROR
r-release-linux-x86_64	1.1.3	18.09	241.05	259.14	ERROR
r-release-macos-arm64	1.1.3				OK
r-release-macos-x86_64	1.1.3	13.00	203.00	216.00	OK
r-release-windows-x86_64	1.1.3	23.00	203.00	226.00	ERROR
r-oldrel-macos-arm64	1.1.3				OK
r-oldrel-macos-x86_64	1.1.3	13.00	203.00	216.00	OK
r-oldrel-windows-x86_64	1.1.3	32.00	268.00	300.00	ERROR

Check Details

Version: 1.1.3
Check: tests
Result: ERROR Running ‘testthat.R’ [126s/82s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # File tests/testthat.R in package ergm.ego, 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 2015-2025 Statnet Commons > ################################################################################ > library(testthat) > library(ergm.ego) Loading required package: ergm Loading required package: network 'network' 1.20.0 (2026-02-06), 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' 4.12.0 (2026-02-17), 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: egor Loading required package: dplyr Attaching package: 'dplyr' The following objects are masked from 'package:stats': filter, lag The following objects are masked from 'package:base': intersect, setdiff, setequal, union Loading required package: tibble 'ergm.ego' 1.1.3 (2025-06-10), part of the Statnet Project * 'news(package="ergm.ego")' for changes since last version * 'citation("ergm.ego")' for citation information * 'https://statnet.org' for help, support, and other information Attaching package: 'ergm.ego' The following objects are masked from 'package:ergm': COLLAPSE_SMALLEST, snctrl The following object is masked from 'package:base': sample > > test_check("ergm.ego") Starting 2 test processes. > test-EgoStat.R: Starting simulated annealing (SAN) > test-EgoStat.R: Iteration 1 of at most 4 > test-EgoStat.R: Iteration 2 of at most 4 > test-EgoStat.R: Iteration 3 of at most 4 > test-EgoStat.R: Finished simulated annealing > test-attrmismatch.R: Constructing pseudopopulation network. > test-attrmismatch.R: Starting simulated annealing (SAN) > test-attrmismatch.R: Iteration 1 of at most 4 > test-attrmismatch.R: Iteration 2 of at most 4 > test-attrmismatch.R: Iteration 3 of at most 4 > test-attrmismatch.R: Iteration 4 of at most 4 > test-attrmismatch.R: Finished simulated annealing > test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation. > test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE): > test-attrmismatch.R: Obtaining the responsible dyads. > test-attrmismatch.R: Evaluating the predictor and response matrix. > test-attrmismatch.R: Maximizing the pseudolikelihood. > test-attrmismatch.R: Finished MPLE. > test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-attrmismatch.R: Iteration 1 of at most 60: > test-attrmismatch.R: 1 Optimizing with step length 1.0000. > test-attrmismatch.R: The log-likelihood improved by 0.0195. > test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-attrmismatch.R: Finished MCMLE. > test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check > test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function. > test-attrmismatch.R: Constructing pseudopopulation network. > test-attrmismatch.R: Starting simulated annealing (SAN) > test-attrmismatch.R: Iteration 1 of at most 4 > test-attrmismatch.R: Iteration 2 of at most 4 > test-attrmismatch.R: Iteration 3 of at most 4 > test-attrmismatch.R: Iteration 4 of at most 4 > test-attrmismatch.R: Finished simulated annealing > test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation. > test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE): > test-attrmismatch.R: Obtaining the responsible dyads. > test-attrmismatch.R: Evaluating the predictor and response matrix. > test-attrmismatch.R: Maximizing the pseudolikelihood. > test-attrmismatch.R: Finished MPLE. > test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-attrmismatch.R: Iteration 1 of at most 60: > test-attrmismatch.R: 1 > test-attrmismatch.R: Optimizing with step length 1.0000. > test-attrmismatch.R: The log-likelihood improved by 0.0181. > test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-attrmismatch.R: Finished MCMLE. > test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check > test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0223. > test-boot_jack.R: Convergence test p-value: 0.0016. > test-boot_jack.R: Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Iteration 2 of at most 4 > test-boot_jack.R: Iteration 3 of at most 4 > test-boot_jack.R: Iteration 4 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0009. > test-boot_jack.R: Convergence test p-value: 0.0006. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0488. > test-boot_jack.R: Convergence test p-value: 0.0024. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0002. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-EgoStat.R: Starting simulated annealing (SAN) > test-EgoStat.R: Iteration 1 of at most 4 > test-EgoStat.R: Iteration 2 of at most 4 > test-EgoStat.R: Iteration 3 of at most 4 > test-EgoStat.R: Finished simulated annealing > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Iteration 2 of at most 4 > test-boot_jack.R: Iteration 3 of at most 4 > test-boot_jack.R: Iteration 4 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0011. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0001. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-coef_recovery.R: Constructing pseudopopulation network. > test-coef_recovery.R: Starting simulated annealing (SAN) > test-coef_recovery.R: Iteration 1 of at most 4 > test-coef_recovery.R: Iteration 2 of at most 4 > test-coef_recovery.R: Iteration 3 of at most 4 > test-coef_recovery.R: Iteration 4 of at most 4 > test-coef_recovery.R: Finished simulated annealing > test-coef_recovery.R: Unable to match target stats. Using MCMLE estimation. > test-coef_recovery.R: Starting maximum pseudolikelihood estimation (MPLE): > test-coef_recovery.R: Obtaining the responsible dyads. > test-coef_recovery.R: Evaluating the predictor and response matrix. > test-coef_recovery.R: Maximizing the pseudolikelihood. > test-coef_recovery.R: Finished MPLE. > test-coef_recovery.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-coef_recovery.R: Iteration 1 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 0.3646. > test-coef_recovery.R: The log-likelihood improved by 2.8321. > test-coef_recovery.R: Iteration 2 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 0.8183. > test-coef_recovery.R: The log-likelihood improved by 3.0954. > test-coef_recovery.R: Iteration 3 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 1.0000. > test-coef_recovery.R: The log-likelihood improved by 1.4921. > test-coef_recovery.R: Step length converged once. Increasing MCMC sample size. > test-coef_recovery.R: Iteration 4 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 1.0000. > test-coef_recovery.R: The log-likelihood improved by 0.7405. > test-coef_recovery.R: Step length converged twice. Stopping. > test-coef_recovery.R: Finished MCMLE. > test-coef_recovery.R: This model was fit using MCMC. To examine model diagnostics and check > test-coef_recovery.R: for degeneracy, use the mcmc.diagnostics() function. > test-drop.R: Constructing pseudopopulation network. > test-drop.R: Starting simulated annealing (SAN) > test-drop.R: Iteration 1 of at most 4 > test-drop.R: Iteration 2 of at most 4 > test-drop.R: Iteration 3 of at most 4 > test-drop.R: Finished simulated annealing > test-drop.R: Observed statistic(s) nodematch.a are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: Unable to match target stats. Using MCMLE estimation. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-drop.R: Iteration 1 of at most 60: > test-drop.R: 1 Optimizing with step length 1.0000. > test-drop.R: The log-likelihood improved by 0.0044. > test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-drop.R: Finished MCMLE. > test-drop.R: This model was fit using MCMC. To examine model diagnostics and check > test-drop.R: for degeneracy, use the mcmc.diagnostics() function. > test-drop.R: Observed statistic(s) nodematch.a are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Evaluating log-likelihood at the estimate. > test-predict.ergm.ego.R: Constructing pseudopopulation network. > test-predict.ergm.ego.R: Starting simulated annealing (SAN) > test-predict.ergm.ego.R: Iteration 1 of at most 4 > test-predict.ergm.ego.R: Iteration 2 of at most 4 > test-predict.ergm.ego.R: Iteration 3 of at most 4 > test-predict.ergm.ego.R: Iteration 4 of at most 4 > test-predict.ergm.ego.R: Finished simulated annealing > test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.ego.R: Obtaining the responsible dyads. > test-predict.ergm.ego.R: Evaluating the predictor and response matrix. > test-predict.ergm.ego.R: Maximizing the pseudolikelihood. > test-predict.ergm.ego.R: Finished MPLE. > test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-predict.ergm.ego.R: Iteration 1 of at most 2: > test-predict.ergm.ego.R: 1 Optimizing with step length 0.5473. > test-predict.ergm.ego.R: The log-likelihood improved by 1.8671. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: Iteration 2 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 1.0000. > test-predict.ergm.ego.R: The log-likelihood improved by 1.0036. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-predict.ergm.ego.R: Finished MCMLE. > test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. > test-predict.ergm.ego.R: Constructing pseudopopulation network. > test-predict.ergm.ego.R: Starting simulated annealing (SAN) > test-predict.ergm.ego.R: Iteration 1 of at most 4 > test-predict.ergm.ego.R: Iteration 2 of at most 4 > test-predict.ergm.ego.R: Iteration 3 of at most 4 > test-predict.ergm.ego.R: Iteration 4 of at most 4 > test-predict.ergm.ego.R: Finished simulated annealing > test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.ego.R: Obtaining the responsible dyads. > test-predict.ergm.ego.R: Evaluating the predictor and response matrix. > test-predict.ergm.ego.R: Maximizing the pseudolikelihood. > test-predict.ergm.ego.R: Finished MPLE. > test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-predict.ergm.ego.R: Iteration 1 of at most 2: > test-predict.ergm.ego.R: 1 Optimizing with step length 0.7865. > test-predict.ergm.ego.R: The log-likelihood improved by 1.8825. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: Iteration 2 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 1.0000. > test-predict.ergm.ego.R: The log-likelihood improved by 0.3775. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-predict.ergm.ego.R: Finished MCMLE. > test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. > test-table_ppop.R: Constructing pseudopopulation network. > test-table_ppop.R: Starting simulated annealing (SAN) > test-table_ppop.R: Iteration 1 of at most 4 > test-table_ppop.R: Iteration 2 of at most 4 > test-table_ppop.R: Iteration 3 of at most 4 > test-table_ppop.R: Iteration 4 of at most 4 > test-table_ppop.R: Finished simulated annealing > test-table_ppop.R: Unable to match target stats. Using MCMLE estimation. > test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-table_ppop.R: Obtaining the responsible dyads. > test-table_ppop.R: Evaluating the predictor and response matrix. > test-table_ppop.R: Maximizing the pseudolikelihood. > test-table_ppop.R: Finished MPLE. > test-table_ppop.R: Constructing pseudopopulation network. > test-table_ppop.R: Starting simulated annealing (SAN) > test-table_ppop.R: Iteration 1 of at most 4 > test-table_ppop.R: Iteration 2 of at most 4 > test-table_ppop.R: Iteration 3 of at most 4 > test-table_ppop.R: Iteration 4 of at most 4 > test-table_ppop.R: Finished simulated annealing > test-table_ppop.R: Unable to match target stats. Using MCMLE estimation. > test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-table_ppop.R: Obtaining the responsible dyads. > test-table_ppop.R: Evaluating the predictor and response matrix. > test-table_ppop.R: Maximizing the pseudolikelihood. > test-table_ppop.R: Finished MPLE. Saving _problems/test-table_ppop-39.R > test-gof.ergm.ego.R: Constructing pseudopopulation network. > test-gof.ergm.ego.R: Starting simulated annealing (SAN) > test-gof.ergm.ego.R: Iteration 1 of at most 4 > test-gof.ergm.ego.R: Finished simulated annealing > test-gof.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-gof.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-gof.ergm.ego.R: Obtaining the responsible dyads. > test-gof.ergm.ego.R: Evaluating the predictor and response matrix. > test-gof.ergm.ego.R: Maximizing the pseudolikelihood. > test-gof.ergm.ego.R: Finished MPLE. > test-gof.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-gof.ergm.ego.R: Iteration 1 of at most 2: > test-gof.ergm.ego.R: 1 Optimizing with step length 1.0000. > test-gof.ergm.ego.R: The log-likelihood improved by 1.6103. > test-gof.ergm.ego.R: Estimating equations are not within tolerance region. > test-gof.ergm.ego.R: Iteration 2 of at most 2: > test-gof.ergm.ego.R: 1 Optimizing with step length 1.0000. > test-gof.ergm.ego.R: The log-likelihood improved by 0.0094. > test-gof.ergm.ego.R: Convergence test p-value: 0.3069. Not converged with 99% confidence; increasing sample size. > test-gof.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-gof.ergm.ego.R: Finished MCMLE. > test-gof.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-gof.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. Saving _problems/test-gof.ergm.ego-17.R Saving _problems/test-gof.ergm.ego-32.R Saving _problems/test-gof.ergm.ego-48.R [ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-table_ppop.R:39:3'): estimation and simulation work ────────── Expected `(egosim <- simulate(egofit_scl, popsize = ppop))` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:15:3'): GOF='model' works ───────────────────── Expected `z <- gof(fmhfit, GOF = "model")` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:30:3'): GOF='degree' works ──────────────────── Expected `z <- gof(fmhfit, GOF = "degree")` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:46:3'): GOF='espartners' works ──────────────── Expected `z <- gof(fmhfit, GOF = "espartners")` to run silently. Actual noise: messages. [ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-debian-clang

Version: 1.1.3
Check: tests
Result: ERROR Running ‘testthat.R’ [83s/55s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # File tests/testthat.R in package ergm.ego, 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 2015-2025 Statnet Commons > ################################################################################ > library(testthat) > library(ergm.ego) Loading required package: ergm Loading required package: network 'network' 1.20.0 (2026-02-06), 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' 4.12.0 (2026-02-17), 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: egor Loading required package: dplyr Attaching package: 'dplyr' The following objects are masked from 'package:stats': filter, lag The following objects are masked from 'package:base': intersect, setdiff, setequal, union Loading required package: tibble 'ergm.ego' 1.1.3 (2025-06-10), part of the Statnet Project * 'news(package="ergm.ego")' for changes since last version * 'citation("ergm.ego")' for citation information * 'https://statnet.org' for help, support, and other information Attaching package: 'ergm.ego' The following objects are masked from 'package:ergm': COLLAPSE_SMALLEST, snctrl The following object is masked from 'package:base': sample > > test_check("ergm.ego") Starting 2 test processes. > test-EgoStat.R: Starting simulated annealing (SAN) > test-EgoStat.R: Iteration 1 of at most 4 > test-EgoStat.R: Iteration 2 of at most 4 > test-attrmismatch.R: Constructing pseudopopulation network. > test-EgoStat.R: Iteration 3 of at most 4 > test-EgoStat.R: Finished simulated annealing > test-attrmismatch.R: Starting simulated annealing (SAN) > test-attrmismatch.R: Iteration 1 of at most 4 > test-attrmismatch.R: Finished simulated annealing > test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation. > test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE): > test-attrmismatch.R: Obtaining the responsible dyads. > test-attrmismatch.R: Evaluating the predictor and response matrix. > test-attrmismatch.R: Maximizing the pseudolikelihood. > test-attrmismatch.R: Finished MPLE. > test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-attrmismatch.R: Iteration 1 of at most 60: > test-attrmismatch.R: 1 Optimizing with step length 1.0000. > test-attrmismatch.R: The log-likelihood improved by 0.0030. > test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-attrmismatch.R: Finished MCMLE. > test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check > test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function. > test-attrmismatch.R: Constructing pseudopopulation network. > test-attrmismatch.R: Starting simulated annealing (SAN) > test-attrmismatch.R: Iteration 1 of at most 4 > test-attrmismatch.R: Finished simulated annealing > test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation. > test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE): > test-attrmismatch.R: Obtaining the responsible dyads. > test-attrmismatch.R: Evaluating the predictor and response matrix. > test-attrmismatch.R: Maximizing the pseudolikelihood. > test-attrmismatch.R: Finished MPLE. > test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-attrmismatch.R: Iteration 1 of at most 60: > test-attrmismatch.R: 1 Optimizing with step length 1.0000. > test-attrmismatch.R: The log-likelihood improved by 0.0031. > test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-attrmismatch.R: Finished MCMLE. > test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check > test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0223. > test-boot_jack.R: Convergence test p-value: 0.0016. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Iteration 2 of at most 4 > test-boot_jack.R: Iteration 3 of at most 4 > test-boot_jack.R: Iteration 4 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0009. > test-boot_jack.R: Convergence test p-value: 0.0006. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0488. > test-boot_jack.R: Convergence test p-value: 0.0024. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0002. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-EgoStat.R: Starting simulated annealing (SAN) > test-EgoStat.R: Iteration 1 of at most 4 > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Iteration 2 of at most 4 > test-boot_jack.R: Iteration 3 of at most 4 > test-EgoStat.R: Iteration 2 of at most 4 > test-boot_jack.R: Iteration 4 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-EgoStat.R: Iteration 3 of at most 4 > test-EgoStat.R: Finished simulated annealing > test-boot_jack.R: 1 Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0011. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0001. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-coef_recovery.R: Constructing pseudopopulation network. > test-coef_recovery.R: Starting simulated annealing (SAN) > test-coef_recovery.R: Iteration 1 of at most 4 > test-coef_recovery.R: Iteration 2 of at most 4 > test-coef_recovery.R: Iteration 3 of at most 4 > test-coef_recovery.R: Iteration 4 of at most 4 > test-coef_recovery.R: Finished simulated annealing > test-coef_recovery.R: Unable to match target stats. Using MCMLE estimation. > test-coef_recovery.R: Starting maximum pseudolikelihood estimation (MPLE): > test-coef_recovery.R: Obtaining the responsible dyads. > test-coef_recovery.R: Evaluating the predictor and response matrix. > test-coef_recovery.R: Maximizing the pseudolikelihood. > test-coef_recovery.R: Finished MPLE. > test-coef_recovery.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-coef_recovery.R: Iteration 1 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 0.3646. > test-coef_recovery.R: The log-likelihood improved by 2.8321. > test-coef_recovery.R: Iteration 2 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 0.8183. > test-coef_recovery.R: The log-likelihood improved by 3.0954. > test-coef_recovery.R: Iteration 3 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 1.0000. > test-coef_recovery.R: The log-likelihood improved by 1.4921. > test-coef_recovery.R: Step length converged once. Increasing MCMC sample size. > test-coef_recovery.R: Iteration 4 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 1.0000. > test-coef_recovery.R: The log-likelihood improved by 0.7405. > test-coef_recovery.R: Step length converged twice. Stopping. > test-coef_recovery.R: Finished MCMLE. > test-coef_recovery.R: This model was fit using MCMC. To examine model diagnostics and check > test-coef_recovery.R: for degeneracy, use the mcmc.diagnostics() function. > test-drop.R: Constructing pseudopopulation network. > test-drop.R: Starting simulated annealing (SAN) > test-drop.R: Iteration 1 of at most 4 > test-drop.R: Iteration 2 of at most 4 > test-drop.R: Iteration 3 of at most 4 > test-drop.R: Finished simulated annealing > test-drop.R: Observed statistic(s) nodematch.a are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: Unable to match target stats. Using MCMLE estimation. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-drop.R: Iteration 1 of at most 60: > test-drop.R: 1 Optimizing with step length 1.0000. > test-drop.R: The log-likelihood improved by 0.0044. > test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-drop.R: Finished MCMLE. > test-drop.R: This model was fit using MCMC. To examine model diagnostics and check > test-drop.R: for degeneracy, use the mcmc.diagnostics() function. > test-drop.R: Observed statistic(s) nodematch.a are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Evaluating log-likelihood at the estimate. > test-drop.R: > test-predict.ergm.ego.R: Constructing pseudopopulation network. > test-predict.ergm.ego.R: Starting simulated annealing (SAN) > test-predict.ergm.ego.R: Iteration 1 of at most 4 > test-predict.ergm.ego.R: Iteration 2 of at most 4 > test-predict.ergm.ego.R: Iteration 3 of at most 4 > test-predict.ergm.ego.R: Iteration 4 of at most 4 > test-predict.ergm.ego.R: Finished simulated annealing > test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.ego.R: Obtaining the responsible dyads. > test-predict.ergm.ego.R: Evaluating the predictor and response matrix. > test-predict.ergm.ego.R: Maximizing the pseudolikelihood. > test-predict.ergm.ego.R: Finished MPLE. > test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-predict.ergm.ego.R: Iteration 1 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 0.5473. > test-predict.ergm.ego.R: The log-likelihood improved by 1.8671. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: Iteration 2 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 1.0000. > test-predict.ergm.ego.R: The log-likelihood improved by 1.0036. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-predict.ergm.ego.R: Finished MCMLE. > test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. > test-predict.ergm.ego.R: Constructing pseudopopulation network. > test-predict.ergm.ego.R: Starting simulated annealing (SAN) > test-predict.ergm.ego.R: Iteration 1 of at most 4 > test-predict.ergm.ego.R: Iteration 2 of at most 4 > test-predict.ergm.ego.R: Iteration 3 of at most 4 > test-predict.ergm.ego.R: Iteration 4 of at most 4 > test-predict.ergm.ego.R: Finished simulated annealing > test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.ego.R: Obtaining the responsible dyads. > test-predict.ergm.ego.R: Evaluating the predictor and response matrix. > test-predict.ergm.ego.R: Maximizing the pseudolikelihood. > test-predict.ergm.ego.R: Finished MPLE. > test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-predict.ergm.ego.R: Iteration 1 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 0.7865. > test-predict.ergm.ego.R: The log-likelihood improved by 1.8825. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: Iteration 2 of at most 2: > test-predict.ergm.ego.R: 1 Optimizing with step length 1.0000. > test-predict.ergm.ego.R: The log-likelihood improved by 0.3775. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-predict.ergm.ego.R: Finished MCMLE. > test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. > test-table_ppop.R: Constructing pseudopopulation network. > test-table_ppop.R: Starting simulated annealing (SAN) > test-table_ppop.R: Iteration 1 of at most 4 > test-table_ppop.R: Iteration 2 of at most 4 > test-table_ppop.R: Iteration 3 of at most 4 > test-table_ppop.R: Iteration 4 of at most 4 > test-table_ppop.R: Finished simulated annealing > test-table_ppop.R: Unable to match target stats. Using MCMLE estimation. > test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-table_ppop.R: Obtaining the responsible dyads. > test-table_ppop.R: Evaluating the predictor and response matrix. > test-table_ppop.R: Maximizing the pseudolikelihood. > test-table_ppop.R: Finished MPLE. > test-table_ppop.R: Constructing pseudopopulation network. > test-table_ppop.R: Starting simulated annealing (SAN) > test-table_ppop.R: Iteration 1 of at most 4 > test-table_ppop.R: Iteration 2 of at most 4 > test-table_ppop.R: Iteration 3 of at most 4 > test-table_ppop.R: Iteration 4 of at most 4 > test-table_ppop.R: Finished simulated annealing > test-table_ppop.R: Unable to match target stats. Using MCMLE estimation. > test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-table_ppop.R: Obtaining the responsible dyads. > test-table_ppop.R: Evaluating the predictor and response matrix. > test-table_ppop.R: Maximizing the pseudolikelihood. > test-table_ppop.R: Finished MPLE. Saving _problems/test-table_ppop-39.R > test-gof.ergm.ego.R: Constructing pseudopopulation network. > test-gof.ergm.ego.R: Starting simulated annealing (SAN) > test-gof.ergm.ego.R: Iteration 1 of at most 4 > test-gof.ergm.ego.R: Finished simulated annealing > test-gof.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-gof.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-gof.ergm.ego.R: Obtaining the responsible dyads. > test-gof.ergm.ego.R: Evaluating the predictor and response matrix. > test-gof.ergm.ego.R: Maximizing the pseudolikelihood. > test-gof.ergm.ego.R: Finished MPLE. > test-gof.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-gof.ergm.ego.R: Iteration 1 of at most 2: > test-gof.ergm.ego.R: 1 Optimizing with step length 1.0000. > test-gof.ergm.ego.R: The log-likelihood improved by 1.6103. > test-gof.ergm.ego.R: Estimating equations are not within tolerance region. > test-gof.ergm.ego.R: Iteration 2 of at most 2: > test-gof.ergm.ego.R: 1 Optimizing with step length 1.0000. > test-gof.ergm.ego.R: The log-likelihood improved by 0.0094. > test-gof.ergm.ego.R: Convergence test p-value: 0.3069. Not converged with 99% confidence; increasing sample size. > test-gof.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-gof.ergm.ego.R: Finished MCMLE. > test-gof.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-gof.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. Saving _problems/test-gof.ergm.ego-17.R Saving _problems/test-gof.ergm.ego-32.R Saving _problems/test-gof.ergm.ego-48.R [ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-table_ppop.R:39:3'): estimation and simulation work ────────── Expected `(egosim <- simulate(egofit_scl, popsize = ppop))` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:15:3'): GOF='model' works ───────────────────── Expected `z <- gof(fmhfit, GOF = "model")` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:30:3'): GOF='degree' works ──────────────────── Expected `z <- gof(fmhfit, GOF = "degree")` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:46:3'): GOF='espartners' works ──────────────── Expected `z <- gof(fmhfit, GOF = "espartners")` to run silently. Actual noise: messages. [ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-debian-gcc

Version: 1.1.3
Check: tests
Result: ERROR Running ‘testthat.R’ [213s/420s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # File tests/testthat.R in package ergm.ego, 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 2015-2025 Statnet Commons > ################################################################################ > library(testthat) > library(ergm.ego) Loading required package: ergm Loading required package: network 'network' 1.20.0 (2026-02-06), 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' 4.12.0 (2026-02-17), 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: egor Loading required package: dplyr Attaching package: 'dplyr' The following objects are masked from 'package:stats': filter, lag The following objects are masked from 'package:base': intersect, setdiff, setequal, union Loading required package: tibble 'ergm.ego' 1.1.3 (2025-06-10), part of the Statnet Project * 'news(package="ergm.ego")' for changes since last version * 'citation("ergm.ego")' for citation information * 'https://statnet.org' for help, support, and other information Attaching package: 'ergm.ego' The following objects are masked from 'package:ergm': COLLAPSE_SMALLEST, snctrl The following object is masked from 'package:base': sample > > test_check("ergm.ego") Starting 2 test processes. > test-EgoStat.R: Starting simulated annealing (SAN) > test-EgoStat.R: Iteration 1 of at most 4 > test-EgoStat.R: Iteration 2 of at most 4 > test-EgoStat.R: Iteration 3 of at most 4 > test-EgoStat.R: Finished simulated annealing > test-attrmismatch.R: Constructing pseudopopulation network. > test-attrmismatch.R: Starting simulated annealing (SAN) > test-attrmismatch.R: Iteration 1 of at most 4 > test-attrmismatch.R: Iteration 2 of at most 4 > test-attrmismatch.R: Iteration 3 of at most 4 > test-attrmismatch.R: Iteration 4 of at most 4 > test-attrmismatch.R: Finished simulated annealing > test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation. > test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE): > test-attrmismatch.R: Obtaining the responsible dyads. > test-attrmismatch.R: Evaluating the predictor and response matrix. > test-attrmismatch.R: Maximizing the pseudolikelihood. > test-attrmismatch.R: Finished MPLE. > test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-attrmismatch.R: Iteration 1 of at most 60: > test-attrmismatch.R: 1 Optimizing with step length 1.0000. > test-attrmismatch.R: The log-likelihood improved by 0.0108. > test-attrmismatch.R: Convergence test p-value: < 0.0001. > test-attrmismatch.R: Converged with 99% confidence. > test-attrmismatch.R: Finished MCMLE. > test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check > test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function. > test-attrmismatch.R: Constructing pseudopopulation network. > test-attrmismatch.R: Starting simulated annealing (SAN) > test-attrmismatch.R: Iteration 1 of at most 4 > test-attrmismatch.R: Iteration 2 of at most 4 > test-attrmismatch.R: Iteration 3 of at most 4 > test-attrmismatch.R: Iteration 4 of at most 4 > test-attrmismatch.R: Finished simulated annealing > test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation. > test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE): > test-attrmismatch.R: Obtaining the responsible dyads. > test-attrmismatch.R: Evaluating the predictor and response matrix. > test-attrmismatch.R: Maximizing the pseudolikelihood. > test-attrmismatch.R: Finished MPLE. > test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-attrmismatch.R: Iteration 1 of at most 60: > test-attrmismatch.R: 1 > test-attrmismatch.R: Optimizing with step length 1.0000. > test-attrmismatch.R: The log-likelihood improved by 0.0176. > test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-attrmismatch.R: Finished MCMLE. > test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check > test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0223. > test-boot_jack.R: Convergence test p-value: 0.0016. > test-boot_jack.R: Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Iteration 2 of at most 4 > test-boot_jack.R: Iteration 3 of at most 4 > test-boot_jack.R: Iteration 4 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0009. > test-boot_jack.R: Convergence test p-value: 0.0006. > test-boot_jack.R: Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0488. > test-boot_jack.R: Convergence test p-value: 0.0024. > test-boot_jack.R: Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0002. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-EgoStat.R: Starting simulated annealing (SAN) > test-EgoStat.R: Iteration 1 of at most 4 > test-EgoStat.R: Iteration 2 of at most 4 > test-EgoStat.R: Iteration 3 of at most 4 > test-EgoStat.R: Finished simulated annealing > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Iteration 2 of at most 4 > test-boot_jack.R: Iteration 3 of at most 4 > test-boot_jack.R: Iteration 4 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0011. > test-boot_jack.R: Convergence test p-value: < 0.0001. > test-boot_jack.R: Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0001. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-coef_recovery.R: Constructing pseudopopulation network. > test-coef_recovery.R: Starting simulated annealing (SAN) > test-coef_recovery.R: Iteration 1 of at most 4 > test-coef_recovery.R: Iteration 2 of at most 4 > test-coef_recovery.R: Iteration 3 of at most 4 > test-coef_recovery.R: Iteration 4 of at most 4 > test-coef_recovery.R: Finished simulated annealing > test-coef_recovery.R: Unable to match target stats. Using MCMLE estimation. > test-coef_recovery.R: Starting maximum pseudolikelihood estimation (MPLE): > test-coef_recovery.R: Obtaining the responsible dyads. > test-coef_recovery.R: Evaluating the predictor and response matrix. > test-coef_recovery.R: Maximizing the pseudolikelihood. > test-coef_recovery.R: Finished MPLE. > test-coef_recovery.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-coef_recovery.R: Iteration 1 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 0.3646. > test-coef_recovery.R: The log-likelihood improved by 2.8321. > test-coef_recovery.R: Iteration 2 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 0.8183. > test-coef_recovery.R: The log-likelihood improved by 3.0954. > test-coef_recovery.R: Iteration 3 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 1.0000. > test-coef_recovery.R: The log-likelihood improved by 1.4921. > test-coef_recovery.R: Step length converged once. Increasing MCMC sample size. > test-coef_recovery.R: Iteration 4 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 1.0000. > test-coef_recovery.R: The log-likelihood improved by 0.7405. > test-coef_recovery.R: Step length converged twice. Stopping. > test-coef_recovery.R: Finished MCMLE. > test-coef_recovery.R: This model was fit using MCMC. To examine model diagnostics and check > test-coef_recovery.R: for degeneracy, use the mcmc.diagnostics() function. > test-drop.R: Constructing pseudopopulation network. > test-drop.R: Starting simulated annealing (SAN) > test-drop.R: Iteration 1 of at most 4 > test-drop.R: Iteration 2 of at most 4 > test-drop.R: Iteration 3 of at most 4 > test-drop.R: Finished simulated annealing > test-drop.R: Observed statistic(s) nodematch.a are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: Unable to match target stats. Using MCMLE estimation. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-drop.R: Iteration 1 of at most 60: > test-drop.R: 1 > test-drop.R: Optimizing with step length 1.0000. > test-drop.R: The log-likelihood improved by 0.0044. > test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-drop.R: Finished MCMLE. > test-drop.R: This model was fit using MCMC. To examine model diagnostics and check > test-drop.R: for degeneracy, use the mcmc.diagnostics() function. > test-drop.R: Observed statistic(s) nodematch.a are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Evaluating log-likelihood at the estimate. > test-predict.ergm.ego.R: Constructing pseudopopulation network. > test-predict.ergm.ego.R: Starting simulated annealing (SAN) > test-predict.ergm.ego.R: Iteration 1 of at most 4 > test-predict.ergm.ego.R: Iteration 2 of at most 4 > test-predict.ergm.ego.R: Iteration 3 of at most 4 > test-predict.ergm.ego.R: Iteration 4 of at most 4 > test-predict.ergm.ego.R: Finished simulated annealing > test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.ego.R: Obtaining the responsible dyads. > test-predict.ergm.ego.R: Evaluating the predictor and response matrix. > test-predict.ergm.ego.R: Maximizing the pseudolikelihood. > test-predict.ergm.ego.R: Finished MPLE. > test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-predict.ergm.ego.R: Iteration 1 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 0.5473. > test-predict.ergm.ego.R: The log-likelihood improved by 1.8671. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: Iteration 2 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 1.0000. > test-predict.ergm.ego.R: The log-likelihood improved by 1.0036. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-predict.ergm.ego.R: Finished MCMLE. > test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. > test-predict.ergm.ego.R: Constructing pseudopopulation network. > test-predict.ergm.ego.R: Starting simulated annealing (SAN) > test-predict.ergm.ego.R: Iteration 1 of at most 4 > test-predict.ergm.ego.R: Iteration 2 of at most 4 > test-predict.ergm.ego.R: Iteration 3 of at most 4 > test-predict.ergm.ego.R: Iteration 4 of at most 4 > test-predict.ergm.ego.R: Finished simulated annealing > test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.ego.R: Obtaining the responsible dyads. > test-predict.ergm.ego.R: Evaluating the predictor and response matrix. > test-predict.ergm.ego.R: Maximizing the pseudolikelihood. > test-predict.ergm.ego.R: Finished MPLE. > test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-predict.ergm.ego.R: Iteration 1 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 0.7865. > test-predict.ergm.ego.R: The log-likelihood improved by 1.8825. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: Iteration 2 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 1.0000. > test-predict.ergm.ego.R: The log-likelihood improved by 0.3775. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-predict.ergm.ego.R: Finished MCMLE. > test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. > test-table_ppop.R: Constructing pseudopopulation network. > test-table_ppop.R: Starting simulated annealing (SAN) > test-table_ppop.R: Iteration 1 of at most 4 > test-table_ppop.R: Iteration 2 of at most 4 > test-table_ppop.R: Iteration 3 of at most 4 > test-table_ppop.R: Iteration 4 of at most 4 > test-table_ppop.R: Finished simulated annealing > test-table_ppop.R: Unable to match target stats. Using MCMLE estimation. > test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-table_ppop.R: Obtaining the responsible dyads. > test-table_ppop.R: Evaluating the predictor and response matrix. > test-table_ppop.R: Maximizing the pseudolikelihood. > test-table_ppop.R: Finished MPLE. > test-table_ppop.R: Constructing pseudopopulation network. > test-table_ppop.R: Starting simulated annealing (SAN) > test-table_ppop.R: Iteration 1 of at most 4 > test-table_ppop.R: Iteration 2 of at most 4 > test-table_ppop.R: Iteration 3 of at most 4 > test-table_ppop.R: Iteration 4 of at most 4 > test-table_ppop.R: Finished simulated annealing > test-table_ppop.R: Unable to match target stats. Using MCMLE estimation. > test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-table_ppop.R: Obtaining the responsible dyads. > test-table_ppop.R: Evaluating the predictor and response matrix. > test-table_ppop.R: Maximizing the pseudolikelihood. > test-table_ppop.R: Finished MPLE. Saving _problems/test-table_ppop-39.R > test-gof.ergm.ego.R: Constructing pseudopopulation network. > test-gof.ergm.ego.R: Starting simulated annealing (SAN) > test-gof.ergm.ego.R: Iteration 1 of at most 4 > test-gof.ergm.ego.R: Finished simulated annealing > test-gof.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-gof.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-gof.ergm.ego.R: Obtaining the responsible dyads. > test-gof.ergm.ego.R: Evaluating the predictor and response matrix. > test-gof.ergm.ego.R: Maximizing the pseudolikelihood. > test-gof.ergm.ego.R: Finished MPLE. > test-gof.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-gof.ergm.ego.R: Iteration 1 of at most 2: > test-gof.ergm.ego.R: 1 > test-gof.ergm.ego.R: Optimizing with step length 1.0000. > test-gof.ergm.ego.R: The log-likelihood improved by 1.6103. > test-gof.ergm.ego.R: Estimating equations are not within tolerance region. > test-gof.ergm.ego.R: Iteration 2 of at most 2: > test-gof.ergm.ego.R: 1 > test-gof.ergm.ego.R: Optimizing with step length 1.0000. > test-gof.ergm.ego.R: The log-likelihood improved by 0.0094. > test-gof.ergm.ego.R: Convergence test p-value: 0.3069. Not converged with 99% confidence; increasing sample size. > test-gof.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-gof.ergm.ego.R: Finished MCMLE. > test-gof.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-gof.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. Saving _problems/test-gof.ergm.ego-17.R Saving _problems/test-gof.ergm.ego-32.R Saving _problems/test-gof.ergm.ego-48.R [ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-table_ppop.R:39:3'): estimation and simulation work ────────── Expected `(egosim <- simulate(egofit_scl, popsize = ppop))` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:15:3'): GOF='model' works ───────────────────── Expected `z <- gof(fmhfit, GOF = "model")` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:30:3'): GOF='degree' works ──────────────────── Expected `z <- gof(fmhfit, GOF = "degree")` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:46:3'): GOF='espartners' works ──────────────── Expected `z <- gof(fmhfit, GOF = "espartners")` to run silently. Actual noise: messages. [ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-fedora-clang

Version: 1.1.3
Check: tests
Result: ERROR Running ‘testthat.R’ [200s/283s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # File tests/testthat.R in package ergm.ego, 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 2015-2025 Statnet Commons > ################################################################################ > library(testthat) > library(ergm.ego) Loading required package: ergm Loading required package: network 'network' 1.20.0 (2026-02-06), 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' 4.12.0 (2026-02-17), 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: egor Loading required package: dplyr Attaching package: 'dplyr' The following objects are masked from 'package:stats': filter, lag The following objects are masked from 'package:base': intersect, setdiff, setequal, union Loading required package: tibble 'ergm.ego' 1.1.3 (2025-06-10), part of the Statnet Project * 'news(package="ergm.ego")' for changes since last version * 'citation("ergm.ego")' for citation information * 'https://statnet.org' for help, support, and other information Attaching package: 'ergm.ego' The following objects are masked from 'package:ergm': COLLAPSE_SMALLEST, snctrl The following object is masked from 'package:base': sample > > test_check("ergm.ego") Starting 2 test processes. > test-EgoStat.R: Starting simulated annealing (SAN) > test-EgoStat.R: Iteration 1 of at most 4 > test-EgoStat.R: Iteration 2 of at most 4 > test-EgoStat.R: Iteration 3 of at most 4 > test-EgoStat.R: Finished simulated annealing > test-attrmismatch.R: Constructing pseudopopulation network. > test-attrmismatch.R: Starting simulated annealing (SAN) > test-attrmismatch.R: Iteration 1 of at most 4 > test-attrmismatch.R: Iteration 2 of at most 4 > test-attrmismatch.R: Iteration 3 of at most 4 > test-attrmismatch.R: Iteration 4 of at most 4 > test-attrmismatch.R: Finished simulated annealing > test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation. > test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE): > test-attrmismatch.R: Obtaining the responsible dyads. > test-attrmismatch.R: Evaluating the predictor and response matrix. > test-attrmismatch.R: Maximizing the pseudolikelihood. > test-attrmismatch.R: Finished MPLE. > test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-attrmismatch.R: Iteration 1 of at most 60: > test-attrmismatch.R: 1 > test-attrmismatch.R: Optimizing with step length 1.0000. > test-attrmismatch.R: The log-likelihood improved by 0.0203. > test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-attrmismatch.R: Finished MCMLE. > test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check > test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function. > test-attrmismatch.R: Constructing pseudopopulation network. > test-attrmismatch.R: Starting simulated annealing (SAN) > test-attrmismatch.R: Iteration 1 of at most 4 > test-attrmismatch.R: Iteration 2 of at most 4 > test-attrmismatch.R: Iteration 3 of at most 4 > test-attrmismatch.R: Iteration 4 of at most 4 > test-attrmismatch.R: Finished simulated annealing > test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation. > test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE): > test-attrmismatch.R: Obtaining the responsible dyads. > test-attrmismatch.R: Evaluating the predictor and response matrix. > test-attrmismatch.R: Maximizing the pseudolikelihood. > test-attrmismatch.R: Finished MPLE. > test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-attrmismatch.R: Iteration 1 of at most 60: > test-attrmismatch.R: 1 > test-attrmismatch.R: Optimizing with step length 1.0000. > test-attrmismatch.R: The log-likelihood improved by 0.0263. > test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-attrmismatch.R: Finished MCMLE. > test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check > test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0223. > test-boot_jack.R: Convergence test p-value: 0.0016. > test-boot_jack.R: Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Iteration 2 of at most 4 > test-boot_jack.R: Iteration 3 of at most 4 > test-boot_jack.R: Iteration 4 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0009. > test-boot_jack.R: Convergence test p-value: 0.0006. > test-boot_jack.R: Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0488. > test-boot_jack.R: Convergence test p-value: 0.0024. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-EgoStat.R: Starting simulated annealing (SAN) > test-EgoStat.R: Iteration 1 of at most 4 > test-EgoStat.R: Iteration 2 of at most 4 > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0002. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-EgoStat.R: Iteration 3 of at most 4 > test-EgoStat.R: Finished simulated annealing > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Iteration 2 of at most 4 > test-boot_jack.R: Iteration 3 of at most 4 > test-boot_jack.R: Iteration 4 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0011. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0001. > test-boot_jack.R: Convergence test p-value: < 0.0001. > test-boot_jack.R: Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-coef_recovery.R: Constructing pseudopopulation network. > test-coef_recovery.R: Starting simulated annealing (SAN) > test-coef_recovery.R: Iteration 1 of at most 4 > test-coef_recovery.R: Iteration 2 of at most 4 > test-coef_recovery.R: Iteration 3 of at most 4 > test-coef_recovery.R: Iteration 4 of at most 4 > test-coef_recovery.R: Finished simulated annealing > test-coef_recovery.R: Unable to match target stats. Using MCMLE estimation. > test-coef_recovery.R: Starting maximum pseudolikelihood estimation (MPLE): > test-coef_recovery.R: Obtaining the responsible dyads. > test-coef_recovery.R: Evaluating the predictor and response matrix. > test-coef_recovery.R: Maximizing the pseudolikelihood. > test-coef_recovery.R: Finished MPLE. > test-coef_recovery.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-coef_recovery.R: Iteration 1 of at most 60: > test-coef_recovery.R: Sampling =============================>- 98% | ETA: 0s > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 0.3646. > test-coef_recovery.R: The log-likelihood improved by 2.8321. > test-coef_recovery.R: Iteration 2 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 0.8183. > test-coef_recovery.R: The log-likelihood improved by 3.0954. > test-coef_recovery.R: Iteration 3 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 1.0000. > test-coef_recovery.R: The log-likelihood improved by 1.4921. > test-coef_recovery.R: Step length converged once. Increasing MCMC sample size. > test-coef_recovery.R: Iteration 4 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 1.0000. > test-coef_recovery.R: The log-likelihood improved by 0.7405. > test-coef_recovery.R: Step length converged twice. Stopping. > test-coef_recovery.R: Finished MCMLE. > test-coef_recovery.R: This model was fit using MCMC. To examine model diagnostics and check > test-coef_recovery.R: for degeneracy, use the mcmc.diagnostics() function. > test-drop.R: Constructing pseudopopulation network. > test-drop.R: Starting simulated annealing (SAN) > test-drop.R: Iteration 1 of at most 4 > test-drop.R: Iteration 2 of at most 4 > test-drop.R: Iteration 3 of at most 4 > test-drop.R: Finished simulated annealing > test-drop.R: Observed statistic(s) nodematch.a are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: Unable to match target stats. Using MCMLE estimation. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-drop.R: Iteration 1 of at most 60: > test-drop.R: 1 > test-drop.R: Optimizing with step length 1.0000. > test-drop.R: The log-likelihood improved by 0.0044. > test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-drop.R: Finished MCMLE. > test-drop.R: This model was fit using MCMC. To examine model diagnostics and check > test-drop.R: for degeneracy, use the mcmc.diagnostics() function. > test-drop.R: Observed statistic(s) nodematch.a are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Evaluating log-likelihood at the estimate. > test-drop.R: > test-predict.ergm.ego.R: Constructing pseudopopulation network. > test-predict.ergm.ego.R: Starting simulated annealing (SAN) > test-predict.ergm.ego.R: Iteration 1 of at most 4 > test-predict.ergm.ego.R: Iteration 2 of at most 4 > test-predict.ergm.ego.R: Iteration 3 of at most 4 > test-predict.ergm.ego.R: Iteration 4 of at most 4 > test-predict.ergm.ego.R: Finished simulated annealing > test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.ego.R: Obtaining the responsible dyads. > test-predict.ergm.ego.R: Evaluating the predictor and response matrix. > test-predict.ergm.ego.R: Maximizing the pseudolikelihood. > test-predict.ergm.ego.R: Finished MPLE. > test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-predict.ergm.ego.R: Iteration 1 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 0.5473. > test-predict.ergm.ego.R: The log-likelihood improved by 1.8671. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: Iteration 2 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 1.0000. > test-predict.ergm.ego.R: The log-likelihood improved by 1.0036. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-predict.ergm.ego.R: Finished MCMLE. > test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. > test-predict.ergm.ego.R: Constructing pseudopopulation network. > test-predict.ergm.ego.R: Starting simulated annealing (SAN) > test-predict.ergm.ego.R: Iteration 1 of at most 4 > test-predict.ergm.ego.R: Iteration 2 of at most 4 > test-predict.ergm.ego.R: Iteration 3 of at most 4 > test-predict.ergm.ego.R: Iteration 4 of at most 4 > test-predict.ergm.ego.R: Finished simulated annealing > test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.ego.R: Obtaining the responsible dyads. > test-predict.ergm.ego.R: Evaluating the predictor and response matrix. > test-predict.ergm.ego.R: Maximizing the pseudolikelihood. > test-predict.ergm.ego.R: Finished MPLE. > test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-predict.ergm.ego.R: Iteration 1 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 0.7865. > test-predict.ergm.ego.R: The log-likelihood improved by 1.8825. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: Iteration 2 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 1.0000. > test-predict.ergm.ego.R: The log-likelihood improved by 0.3775. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-predict.ergm.ego.R: Finished MCMLE. > test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. > test-table_ppop.R: Constructing pseudopopulation network. > test-table_ppop.R: Starting simulated annealing (SAN) > test-table_ppop.R: Iteration 1 of at most 4 > test-table_ppop.R: Iteration 2 of at most 4 > test-table_ppop.R: Iteration 3 of at most 4 > test-table_ppop.R: Iteration 4 of at most 4 > test-table_ppop.R: Finished simulated annealing > test-table_ppop.R: Unable to match target stats. Using MCMLE estimation. > test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-table_ppop.R: Obtaining the responsible dyads. > test-table_ppop.R: Evaluating the predictor and response matrix. > test-table_ppop.R: Maximizing the pseudolikelihood. > test-table_ppop.R: Finished MPLE. > test-table_ppop.R: Constructing pseudopopulation network. > test-table_ppop.R: Starting simulated annealing (SAN) > test-table_ppop.R: Iteration 1 of at most 4 > test-table_ppop.R: Iteration 2 of at most 4 > test-table_ppop.R: Iteration 3 of at most 4 > test-table_ppop.R: Iteration 4 of at most 4 > test-table_ppop.R: Finished simulated annealing > test-table_ppop.R: Unable to match target stats. Using MCMLE estimation. > test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-table_ppop.R: Obtaining the responsible dyads. > test-table_ppop.R: Evaluating the predictor and response matrix. > test-table_ppop.R: Maximizing the pseudolikelihood. > test-table_ppop.R: Finished MPLE. Saving _problems/test-table_ppop-39.R > test-gof.ergm.ego.R: Constructing pseudopopulation network. > test-gof.ergm.ego.R: Starting simulated annealing (SAN) > test-gof.ergm.ego.R: Iteration 1 of at most 4 > test-gof.ergm.ego.R: Finished simulated annealing > test-gof.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-gof.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-gof.ergm.ego.R: Obtaining the responsible dyads. > test-gof.ergm.ego.R: Evaluating the predictor and response matrix. > test-gof.ergm.ego.R: Maximizing the pseudolikelihood. > test-gof.ergm.ego.R: Finished MPLE. > test-gof.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-gof.ergm.ego.R: Iteration 1 of at most 2: > test-gof.ergm.ego.R: 1 > test-gof.ergm.ego.R: Optimizing with step length 1.0000. > test-gof.ergm.ego.R: The log-likelihood improved by 1.6103. > test-gof.ergm.ego.R: Estimating equations are not within tolerance region. > test-gof.ergm.ego.R: Iteration 2 of at most 2: > test-gof.ergm.ego.R: 1 > test-gof.ergm.ego.R: Optimizing with step length 1.0000. > test-gof.ergm.ego.R: The log-likelihood improved by 0.0094. > test-gof.ergm.ego.R: Convergence test p-value: 0.3069. Not converged with 99% confidence; increasing sample size. > test-gof.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-gof.ergm.ego.R: Finished MCMLE. > test-gof.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-gof.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. Saving _problems/test-gof.ergm.ego-17.R Saving _problems/test-gof.ergm.ego-32.R Saving _problems/test-gof.ergm.ego-48.R [ FAIL 4 | WARN 0 | SKIP 0 | PASS 104 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-table_ppop.R:39:3'): estimation and simulation work ────────── Expected `(egosim <- simulate(egofit_scl, popsize = ppop))` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:15:3'): GOF='model' works ───────────────────── Expected `z <- gof(fmhfit, GOF = "model")` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:30:3'): GOF='degree' works ──────────────────── Expected `z <- gof(fmhfit, GOF = "degree")` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:46:3'): GOF='espartners' works ──────────────── Expected `z <- gof(fmhfit, GOF = "espartners")` to run silently. Actual noise: messages. [ FAIL 4 | WARN 0 | SKIP 0 | PASS 104 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 1.1.3
Check: tests
Result: ERROR Running 'testthat.R' [76s] Running the tests in 'tests/testthat.R' failed. Complete output: > # File tests/testthat.R in package ergm.ego, 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 2015-2025 Statnet Commons > ################################################################################ > library(testthat) > library(ergm.ego) Loading required package: ergm Loading required package: network 'network' 1.20.0 (2026-02-06), 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' 4.12.0 (2026-02-17), 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: egor Loading required package: dplyr Attaching package: 'dplyr' The following objects are masked from 'package:stats': filter, lag The following objects are masked from 'package:base': intersect, setdiff, setequal, union Loading required package: tibble 'ergm.ego' 1.1.3 (2025-06-10), part of the Statnet Project * 'news(package="ergm.ego")' for changes since last version * 'citation("ergm.ego")' for citation information * 'https://statnet.org' for help, support, and other information Attaching package: 'ergm.ego' The following objects are masked from 'package:ergm': COLLAPSE_SMALLEST, snctrl The following object is masked from 'package:base': sample > > test_check("ergm.ego") Starting 2 test processes. > test-EgoStat.R: Starting simulated annealing (SAN) > test-EgoStat.R: Iteration 1 of at most 4 > test-EgoStat.R: Iteration 2 of at most 4 > test-EgoStat.R: Iteration 3 of at most 4 > test-EgoStat.R: Finished simulated annealing > test-attrmismatch.R: Constructing pseudopopulation network. > test-attrmismatch.R: Starting simulated annealing (SAN) > test-attrmismatch.R: Iteration 1 of at most 4 > test-attrmismatch.R: Iteration 2 of at most 4 > test-attrmismatch.R: Iteration 3 of at most 4 > test-attrmismatch.R: Iteration 4 of at most 4 > test-attrmismatch.R: Finished simulated annealing > test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation. > test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE): > test-attrmismatch.R: Obtaining the responsible dyads. > test-attrmismatch.R: Evaluating the predictor and response matrix. > test-attrmismatch.R: Maximizing the pseudolikelihood. > test-attrmismatch.R: Finished MPLE. > test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-attrmismatch.R: Iteration 1 of at most 60: > test-attrmismatch.R: 1 Optimizing with step length 1.0000. > test-attrmismatch.R: The log-likelihood improved by 0.0150. > test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-attrmismatch.R: Finished MCMLE. > test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check > test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function. > test-attrmismatch.R: Constructing pseudopopulation network. > test-attrmismatch.R: Starting simulated annealing (SAN) > test-attrmismatch.R: Iteration 1 of at most 4 > test-attrmismatch.R: Iteration 2 of at most 4 > test-attrmismatch.R: Iteration 3 of at most 4 > test-attrmismatch.R: Iteration 4 of at most 4 > test-attrmismatch.R: Finished simulated annealing > test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation. > test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE): > test-attrmismatch.R: Obtaining the responsible dyads. > test-attrmismatch.R: Evaluating the predictor and response matrix. > test-attrmismatch.R: Maximizing the pseudolikelihood. > test-attrmismatch.R: Finished MPLE. > test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-attrmismatch.R: Iteration 1 of at most 60: > test-attrmismatch.R: 1 > test-attrmismatch.R: Optimizing with step length 1.0000. > test-attrmismatch.R: The log-likelihood improved by 0.0130. > test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-attrmismatch.R: Finished MCMLE. > test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check > test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0223. > test-boot_jack.R: Convergence test p-value: 0.0016. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Iteration 2 of at most 4 > test-boot_jack.R: Iteration 3 of at most 4 > test-boot_jack.R: Iteration 4 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0009. > test-boot_jack.R: Convergence test p-value: 0.0006. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0488. > test-boot_jack.R: Convergence test p-value: 0.0024. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0002. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-EgoStat.R: Starting simulated annealing (SAN) > test-EgoStat.R: Iteration 1 of at most 4 > test-EgoStat.R: Iteration 2 of at most 4 > test-EgoStat.R: Iteration 3 of at most 4 > test-EgoStat.R: Finished simulated annealing > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Iteration 2 of at most 4 > test-boot_jack.R: Iteration 3 of at most 4 > test-boot_jack.R: Iteration 4 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0011. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0001. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-coef_recovery.R: Constructing pseudopopulation network. > test-coef_recovery.R: Starting simulated annealing (SAN) > test-coef_recovery.R: Iteration 1 of at most 4 > test-coef_recovery.R: Iteration 2 of at most 4 > test-coef_recovery.R: Iteration 3 of at most 4 > test-coef_recovery.R: Iteration 4 of at most 4 > test-coef_recovery.R: Finished simulated annealing > test-coef_recovery.R: Unable to match target stats. Using MCMLE estimation. > test-coef_recovery.R: Starting maximum pseudolikelihood estimation (MPLE): > test-coef_recovery.R: Obtaining the responsible dyads. > test-coef_recovery.R: Evaluating the predictor and response matrix. > test-coef_recovery.R: Maximizing the pseudolikelihood. > test-coef_recovery.R: Finished MPLE. > test-coef_recovery.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-coef_recovery.R: Iteration 1 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 0.3646. > test-coef_recovery.R: The log-likelihood improved by 2.8321. > test-coef_recovery.R: Iteration 2 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 0.8183. > test-coef_recovery.R: The log-likelihood improved by 3.0954. > test-coef_recovery.R: Iteration 3 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 1.0000. > test-coef_recovery.R: The log-likelihood improved by 1.4921. > test-coef_recovery.R: Step length converged once. Increasing MCMC sample size. > test-coef_recovery.R: Iteration 4 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 1.0000. > test-coef_recovery.R: The log-likelihood improved by 0.7405. > test-coef_recovery.R: Step length converged twice. Stopping. > test-coef_recovery.R: Finished MCMLE. > test-coef_recovery.R: This model was fit using MCMC. To examine model diagnostics and check > test-coef_recovery.R: for degeneracy, use the mcmc.diagnostics() function. > test-drop.R: Constructing pseudopopulation network. > test-drop.R: Starting simulated annealing (SAN) > test-drop.R: Iteration 1 of at most 4 > test-drop.R: Finished simulated annealing > test-drop.R: Observed statistic(s) nodematch.a are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: Unable to match target stats. Using MCMLE estimation. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-drop.R: Iteration 1 of at most 60: > test-drop.R: 1 Optimizing with step length 1.0000. > test-drop.R: The log-likelihood improved by 0.0002. > test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-drop.R: Finished MCMLE. > test-drop.R: This model was fit using MCMC. To examine model diagnostics and check > test-drop.R: for degeneracy, use the mcmc.diagnostics() function. > test-drop.R: Observed statistic(s) nodematch.a are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Evaluating log-likelihood at the estimate. > test-drop.R: > test-predict.ergm.ego.R: Constructing pseudopopulation network. > test-predict.ergm.ego.R: Starting simulated annealing (SAN) > test-predict.ergm.ego.R: Iteration 1 of at most 4 > test-predict.ergm.ego.R: Iteration 2 of at most 4 > test-predict.ergm.ego.R: Iteration 3 of at most 4 > test-predict.ergm.ego.R: Iteration 4 of at most 4 > test-predict.ergm.ego.R: Finished simulated annealing > test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.ego.R: Obtaining the responsible dyads. > test-predict.ergm.ego.R: Evaluating the predictor and response matrix. > test-predict.ergm.ego.R: Maximizing the pseudolikelihood. > test-predict.ergm.ego.R: Finished MPLE. > test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-predict.ergm.ego.R: Iteration 1 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 0.5473. > test-predict.ergm.ego.R: The log-likelihood improved by 1.8671. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: Iteration 2 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 1.0000. > test-predict.ergm.ego.R: The log-likelihood improved by 1.0036. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-predict.ergm.ego.R: Finished MCMLE. > test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. > test-predict.ergm.ego.R: Constructing pseudopopulation network. > test-predict.ergm.ego.R: Starting simulated annealing (SAN) > test-predict.ergm.ego.R: Iteration 1 of at most 4 > test-predict.ergm.ego.R: Iteration 2 of at most 4 > test-predict.ergm.ego.R: Iteration 3 of at most 4 > test-predict.ergm.ego.R: Iteration 4 of at most 4 > test-predict.ergm.ego.R: Finished simulated annealing > test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.ego.R: Obtaining the responsible dyads. > test-predict.ergm.ego.R: Evaluating the predictor and response matrix. > test-predict.ergm.ego.R: Maximizing the pseudolikelihood. > test-predict.ergm.ego.R: Finished MPLE. > test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-predict.ergm.ego.R: Iteration 1 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 0.7865. > test-predict.ergm.ego.R: The log-likelihood improved by 1.8825. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: Iteration 2 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 1.0000. > test-predict.ergm.ego.R: The log-likelihood improved by 0.3775. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-predict.ergm.ego.R: Finished MCMLE. > test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. > test-table_ppop.R: Constructing pseudopopulation network. > test-table_ppop.R: Starting simulated annealing (SAN) > test-table_ppop.R: Iteration 1 of at most 4 > test-table_ppop.R: Iteration 2 of at most 4 > test-table_ppop.R: Iteration 3 of at most 4 > test-table_ppop.R: Iteration 4 of at most 4 > test-table_ppop.R: Finished simulated annealing > test-table_ppop.R: Unable to match target stats. Using MCMLE estimation. > test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-table_ppop.R: Obtaining the responsible dyads. > test-table_ppop.R: Evaluating the predictor and response matrix. > test-table_ppop.R: Maximizing the pseudolikelihood. > test-table_ppop.R: Finished MPLE. > test-table_ppop.R: Constructing pseudopopulation network. > test-table_ppop.R: Starting simulated annealing (SAN) > test-table_ppop.R: Iteration 1 of at most 4 > test-table_ppop.R: Iteration 2 of at most 4 > test-table_ppop.R: Iteration 3 of at most 4 > test-table_ppop.R: Iteration 4 of at most 4 > test-table_ppop.R: Finished simulated annealing > test-table_ppop.R: Unable to match target stats. Using MCMLE estimation. > test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-table_ppop.R: Obtaining the responsible dyads. > test-table_ppop.R: Evaluating the predictor and response matrix. > test-table_ppop.R: Maximizing the pseudolikelihood. > test-table_ppop.R: Finished MPLE. Saving _problems/test-table_ppop-39.R > test-gof.ergm.ego.R: Constructing pseudopopulation network. > test-gof.ergm.ego.R: Starting simulated annealing (SAN) > test-gof.ergm.ego.R: Iteration 1 of at most 4 > test-gof.ergm.ego.R: Finished simulated annealing > test-gof.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-gof.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-gof.ergm.ego.R: Obtaining the responsible dyads. > test-gof.ergm.ego.R: Evaluating the predictor and response matrix. > test-gof.ergm.ego.R: Maximizing the pseudolikelihood. > test-gof.ergm.ego.R: Finished MPLE. > test-gof.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-gof.ergm.ego.R: Iteration 1 of at most 2: > test-gof.ergm.ego.R: 1 > test-gof.ergm.ego.R: Optimizing with step length 0.6160. > test-gof.ergm.ego.R: The log-likelihood improved by 2.8670. > test-gof.ergm.ego.R: Estimating equations are not within tolerance region. > test-gof.ergm.ego.R: Iteration 2 of at most 2: > test-gof.ergm.ego.R: 1 > test-gof.ergm.ego.R: Optimizing with step length 1.0000. > test-gof.ergm.ego.R: The log-likelihood improved by 0.7726. > test-gof.ergm.ego.R: Estimating equations are not within tolerance region. > test-gof.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-gof.ergm.ego.R: Finished MCMLE. > test-gof.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-gof.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. Saving _problems/test-gof.ergm.ego-17.R Saving _problems/test-gof.ergm.ego-32.R Saving _problems/test-gof.ergm.ego-48.R [ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-table_ppop.R:39:3'): estimation and simulation work ────────── Expected `(egosim <- simulate(egofit_scl, popsize = ppop))` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:15:3'): GOF='model' works ───────────────────── Expected `z <- gof(fmhfit, GOF = "model")` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:30:3'): GOF='degree' works ──────────────────── Expected `z <- gof(fmhfit, GOF = "degree")` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:46:3'): GOF='espartners' works ──────────────── Expected `z <- gof(fmhfit, GOF = "espartners")` to run silently. Actual noise: messages. [ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ] Error: ! Test failures. Execution halted Flavor: r-devel-windows-x86_64

Version: 1.1.3
Check: tests
Result: ERROR Running ‘testthat.R’ [122s/81s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # File tests/testthat.R in package ergm.ego, 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 2015-2025 Statnet Commons > ################################################################################ > library(testthat) > library(ergm.ego) Loading required package: ergm Loading required package: network 'network' 1.20.0 (2026-02-06), 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' 4.12.0 (2026-02-17), 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: egor Loading required package: dplyr Attaching package: 'dplyr' The following objects are masked from 'package:stats': filter, lag The following objects are masked from 'package:base': intersect, setdiff, setequal, union Loading required package: tibble 'ergm.ego' 1.1.3 (2025-06-10), part of the Statnet Project * 'news(package="ergm.ego")' for changes since last version * 'citation("ergm.ego")' for citation information * 'https://statnet.org' for help, support, and other information Attaching package: 'ergm.ego' The following objects are masked from 'package:ergm': COLLAPSE_SMALLEST, snctrl The following object is masked from 'package:base': sample > > test_check("ergm.ego") Starting 2 test processes. > test-EgoStat.R: Starting simulated annealing (SAN) > test-EgoStat.R: Iteration 1 of at most 4 > test-EgoStat.R: Iteration 2 of at most 4 > test-attrmismatch.R: Constructing pseudopopulation network. > test-EgoStat.R: Iteration 3 of at most 4 > test-attrmismatch.R: Starting simulated annealing (SAN) > test-EgoStat.R: Finished simulated annealing > test-attrmismatch.R: Iteration 1 of at most 4 > test-attrmismatch.R: Iteration 2 of at most 4 > test-attrmismatch.R: Iteration 3 of at most 4 > test-attrmismatch.R: Iteration 4 of at most 4 > test-attrmismatch.R: Finished simulated annealing > test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation. > test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE): > test-attrmismatch.R: Obtaining the responsible dyads. > test-attrmismatch.R: Evaluating the predictor and response matrix. > test-attrmismatch.R: Maximizing the pseudolikelihood. > test-attrmismatch.R: Finished MPLE. > test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-attrmismatch.R: Iteration 1 of at most 60: > test-attrmismatch.R: 1 Optimizing with step length 1.0000. > test-attrmismatch.R: The log-likelihood improved by 0.0232. > test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-attrmismatch.R: Finished MCMLE. > test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check > test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function. > test-attrmismatch.R: Constructing pseudopopulation network. > test-attrmismatch.R: Starting simulated annealing (SAN) > test-attrmismatch.R: Iteration 1 of at most 4 > test-attrmismatch.R: Iteration 2 of at most 4 > test-attrmismatch.R: Iteration 3 of at most 4 > test-attrmismatch.R: Iteration 4 of at most 4 > test-attrmismatch.R: Finished simulated annealing > test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation. > test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE): > test-attrmismatch.R: Obtaining the responsible dyads. > test-attrmismatch.R: Evaluating the predictor and response matrix. > test-attrmismatch.R: Maximizing the pseudolikelihood. > test-attrmismatch.R: Finished MPLE. > test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-attrmismatch.R: Iteration 1 of at most 60: > test-attrmismatch.R: 1 > test-attrmismatch.R: Optimizing with step length 1.0000. > test-attrmismatch.R: The log-likelihood improved by 0.0210. > test-attrmismatch.R: Convergence test p-value: 0.0002. Converged with 99% confidence. > test-attrmismatch.R: Finished MCMLE. > test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check > test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0223. > test-boot_jack.R: Convergence test p-value: 0.0016. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Iteration 2 of at most 4 > test-boot_jack.R: Iteration 3 of at most 4 > test-boot_jack.R: Iteration 4 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0009. > test-boot_jack.R: Convergence test p-value: 0.0006. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0488. > test-boot_jack.R: Convergence test p-value: 0.0024. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0002. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-EgoStat.R: Starting simulated annealing (SAN) > test-EgoStat.R: Iteration 1 of at most 4 > test-EgoStat.R: Iteration 2 of at most 4 > test-EgoStat.R: Iteration 3 of at most 4 > test-EgoStat.R: Finished simulated annealing > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Iteration 2 of at most 4 > test-boot_jack.R: Iteration 3 of at most 4 > test-boot_jack.R: Iteration 4 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0011. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0001. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-coef_recovery.R: Constructing pseudopopulation network. > test-coef_recovery.R: Starting simulated annealing (SAN) > test-coef_recovery.R: Iteration 1 of at most 4 > test-coef_recovery.R: Iteration 2 of at most 4 > test-coef_recovery.R: Iteration 3 of at most 4 > test-coef_recovery.R: Iteration 4 of at most 4 > test-coef_recovery.R: Finished simulated annealing > test-coef_recovery.R: Unable to match target stats. Using MCMLE estimation. > test-coef_recovery.R: Starting maximum pseudolikelihood estimation (MPLE): > test-coef_recovery.R: Obtaining the responsible dyads. > test-coef_recovery.R: Evaluating the predictor and response matrix. > test-coef_recovery.R: Maximizing the pseudolikelihood. > test-coef_recovery.R: Finished MPLE. > test-coef_recovery.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-coef_recovery.R: Iteration 1 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 0.3646. > test-coef_recovery.R: The log-likelihood improved by 2.8321. > test-coef_recovery.R: Iteration 2 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 0.8183. > test-coef_recovery.R: The log-likelihood improved by 3.0954. > test-coef_recovery.R: Iteration 3 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 1.0000. > test-coef_recovery.R: The log-likelihood improved by 1.4921. > test-coef_recovery.R: Step length converged once. Increasing MCMC sample size. > test-coef_recovery.R: Iteration 4 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 1.0000. > test-coef_recovery.R: The log-likelihood improved by 0.7405. > test-coef_recovery.R: Step length converged twice. Stopping. > test-coef_recovery.R: Finished MCMLE. > test-coef_recovery.R: This model was fit using MCMC. To examine model diagnostics and check > test-coef_recovery.R: for degeneracy, use the mcmc.diagnostics() function. > test-drop.R: Constructing pseudopopulation network. > test-drop.R: Starting simulated annealing (SAN) > test-drop.R: Iteration 1 of at most 4 > test-drop.R: Iteration 2 of at most 4 > test-drop.R: Iteration 3 of at most 4 > test-drop.R: Finished simulated annealing > test-drop.R: Observed statistic(s) nodematch.a are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: Unable to match target stats. Using MCMLE estimation. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-drop.R: Iteration 1 of at most 60: > test-drop.R: 1 Optimizing with step length 1.0000. > test-drop.R: The log-likelihood improved by 0.0044. > test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-drop.R: Finished MCMLE. > test-drop.R: This model was fit using MCMC. To examine model diagnostics and check > test-drop.R: for degeneracy, use the mcmc.diagnostics() function. > test-drop.R: Observed statistic(s) nodematch.a are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Evaluating log-likelihood at the estimate. > test-drop.R: > test-predict.ergm.ego.R: Constructing pseudopopulation network. > test-predict.ergm.ego.R: Starting simulated annealing (SAN) > test-predict.ergm.ego.R: Iteration 1 of at most 4 > test-predict.ergm.ego.R: Iteration 2 of at most 4 > test-predict.ergm.ego.R: Iteration 3 of at most 4 > test-predict.ergm.ego.R: Iteration 4 of at most 4 > test-predict.ergm.ego.R: Finished simulated annealing > test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.ego.R: Obtaining the responsible dyads. > test-predict.ergm.ego.R: Evaluating the predictor and response matrix. > test-predict.ergm.ego.R: Maximizing the pseudolikelihood. > test-predict.ergm.ego.R: Finished MPLE. > test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-predict.ergm.ego.R: Iteration 1 of at most 2: > test-predict.ergm.ego.R: 1 Optimizing with step length 0.5473. > test-predict.ergm.ego.R: The log-likelihood improved by 1.8671. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: Iteration 2 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 1.0000. > test-predict.ergm.ego.R: The log-likelihood improved by 1.0036. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-predict.ergm.ego.R: Finished MCMLE. > test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. > test-predict.ergm.ego.R: Constructing pseudopopulation network. > test-predict.ergm.ego.R: Starting simulated annealing (SAN) > test-predict.ergm.ego.R: Iteration 1 of at most 4 > test-predict.ergm.ego.R: Iteration 2 of at most 4 > test-predict.ergm.ego.R: Iteration 3 of at most 4 > test-predict.ergm.ego.R: Iteration 4 of at most 4 > test-predict.ergm.ego.R: Finished simulated annealing > test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.ego.R: Obtaining the responsible dyads. > test-predict.ergm.ego.R: Evaluating the predictor and response matrix. > test-predict.ergm.ego.R: Maximizing the pseudolikelihood. > test-predict.ergm.ego.R: Finished MPLE. > test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-predict.ergm.ego.R: Iteration 1 of at most 2: > test-predict.ergm.ego.R: 1 Optimizing with step length 0.7865. > test-predict.ergm.ego.R: The log-likelihood improved by 1.8825. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: Iteration 2 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 1.0000. > test-predict.ergm.ego.R: The log-likelihood improved by 0.3775. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-predict.ergm.ego.R: Finished MCMLE. > test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. > test-table_ppop.R: Constructing pseudopopulation network. > test-table_ppop.R: Starting simulated annealing (SAN) > test-table_ppop.R: Iteration 1 of at most 4 > test-table_ppop.R: Iteration 2 of at most 4 > test-table_ppop.R: Iteration 3 of at most 4 > test-table_ppop.R: Iteration 4 of at most 4 > test-table_ppop.R: Finished simulated annealing > test-table_ppop.R: Unable to match target stats. Using MCMLE estimation. > test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-table_ppop.R: Obtaining the responsible dyads. > test-table_ppop.R: Evaluating the predictor and response matrix. > test-table_ppop.R: Maximizing the pseudolikelihood. > test-table_ppop.R: Finished MPLE. > test-table_ppop.R: Constructing pseudopopulation network. > test-table_ppop.R: Starting simulated annealing (SAN) > test-table_ppop.R: Iteration 1 of at most 4 > test-table_ppop.R: Iteration 2 of at most 4 > test-table_ppop.R: Iteration 3 of at most 4 > test-table_ppop.R: Iteration 4 of at most 4 > test-table_ppop.R: Finished simulated annealing > test-table_ppop.R: Unable to match target stats. Using MCMLE estimation. > test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-table_ppop.R: Obtaining the responsible dyads. > test-table_ppop.R: Evaluating the predictor and response matrix. > test-table_ppop.R: Maximizing the pseudolikelihood. > test-table_ppop.R: Finished MPLE. Saving _problems/test-table_ppop-39.R > test-gof.ergm.ego.R: Constructing pseudopopulation network. > test-gof.ergm.ego.R: Starting simulated annealing (SAN) > test-gof.ergm.ego.R: Iteration 1 of at most 4 > test-gof.ergm.ego.R: Finished simulated annealing > test-gof.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-gof.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-gof.ergm.ego.R: Obtaining the responsible dyads. > test-gof.ergm.ego.R: Evaluating the predictor and response matrix. > test-gof.ergm.ego.R: Maximizing the pseudolikelihood. > test-gof.ergm.ego.R: Finished MPLE. > test-gof.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-gof.ergm.ego.R: Iteration 1 of at most 2: > test-gof.ergm.ego.R: 1 Optimizing with step length 1.0000. > test-gof.ergm.ego.R: The log-likelihood improved by 1.6103. > test-gof.ergm.ego.R: Estimating equations are not within tolerance region. > test-gof.ergm.ego.R: Iteration 2 of at most 2: > test-gof.ergm.ego.R: 1 > test-gof.ergm.ego.R: Optimizing with step length 1.0000. > test-gof.ergm.ego.R: The log-likelihood improved by 0.0094. > test-gof.ergm.ego.R: Convergence test p-value: 0.3069. Not converged with 99% confidence; increasing sample size. > test-gof.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-gof.ergm.ego.R: Finished MCMLE. > test-gof.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-gof.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. Saving _problems/test-gof.ergm.ego-17.R Saving _problems/test-gof.ergm.ego-32.R Saving _problems/test-gof.ergm.ego-48.R [ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-table_ppop.R:39:3'): estimation and simulation work ────────── Expected `(egosim <- simulate(egofit_scl, popsize = ppop))` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:15:3'): GOF='model' works ───────────────────── Expected `z <- gof(fmhfit, GOF = "model")` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:30:3'): GOF='degree' works ──────────────────── Expected `z <- gof(fmhfit, GOF = "degree")` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:46:3'): GOF='espartners' works ──────────────── Expected `z <- gof(fmhfit, GOF = "espartners")` to run silently. Actual noise: messages. [ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ] Error: ! Test failures. Execution halted Flavor: r-patched-linux-x86_64

Version: 1.1.3
Check: tests
Result: ERROR Running ‘testthat.R’ [122s/80s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # File tests/testthat.R in package ergm.ego, 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 2015-2025 Statnet Commons > ################################################################################ > library(testthat) > library(ergm.ego) Loading required package: ergm Loading required package: network 'network' 1.20.0 (2026-02-06), 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' 4.12.0 (2026-02-17), 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: egor Loading required package: dplyr Attaching package: 'dplyr' The following objects are masked from 'package:stats': filter, lag The following objects are masked from 'package:base': intersect, setdiff, setequal, union Loading required package: tibble 'ergm.ego' 1.1.3 (2025-06-10), part of the Statnet Project * 'news(package="ergm.ego")' for changes since last version * 'citation("ergm.ego")' for citation information * 'https://statnet.org' for help, support, and other information Attaching package: 'ergm.ego' The following objects are masked from 'package:ergm': COLLAPSE_SMALLEST, snctrl The following object is masked from 'package:base': sample > > test_check("ergm.ego") Starting 2 test processes. > test-attrmismatch.R: Constructing pseudopopulation network. > test-EgoStat.R: Starting simulated annealing (SAN) > test-EgoStat.R: Iteration 1 of at most 4 > test-EgoStat.R: Iteration 2 of at most 4 > test-attrmismatch.R: Starting simulated annealing (SAN) > test-EgoStat.R: Iteration 3 of at most 4 > test-EgoStat.R: Finished simulated annealing > test-attrmismatch.R: Iteration 1 of at most 4 > test-attrmismatch.R: Iteration 2 of at most 4 > test-attrmismatch.R: Iteration 3 of at most 4 > test-attrmismatch.R: Iteration 4 of at most 4 > test-attrmismatch.R: Finished simulated annealing > test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation. > test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE): > test-attrmismatch.R: Obtaining the responsible dyads. > test-attrmismatch.R: Evaluating the predictor and response matrix. > test-attrmismatch.R: Maximizing the pseudolikelihood. > test-attrmismatch.R: Finished MPLE. > test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-attrmismatch.R: Iteration 1 of at most 60: > test-attrmismatch.R: 1 > test-attrmismatch.R: Optimizing with step length 1.0000. > test-attrmismatch.R: The log-likelihood improved by 0.0118. > test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-attrmismatch.R: Finished MCMLE. > test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check > test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function. > test-attrmismatch.R: Constructing pseudopopulation network. > test-attrmismatch.R: Starting simulated annealing (SAN) > test-attrmismatch.R: Iteration 1 of at most 4 > test-attrmismatch.R: Iteration 2 of at most 4 > test-attrmismatch.R: Iteration 3 of at most 4 > test-attrmismatch.R: Iteration 4 of at most 4 > test-attrmismatch.R: Finished simulated annealing > test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation. > test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE): > test-attrmismatch.R: Obtaining the responsible dyads. > test-attrmismatch.R: Evaluating the predictor and response matrix. > test-attrmismatch.R: Maximizing the pseudolikelihood. > test-attrmismatch.R: Finished MPLE. > test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-attrmismatch.R: Iteration 1 of at most 60: > test-attrmismatch.R: 1 Optimizing with step length 1.0000. > test-attrmismatch.R: The log-likelihood improved by 0.0219. > test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-attrmismatch.R: Finished MCMLE. > test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check > test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0223. > test-boot_jack.R: Convergence test p-value: 0.0016. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Iteration 2 of at most 4 > test-boot_jack.R: Iteration 3 of at most 4 > test-boot_jack.R: Iteration 4 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0009. > test-boot_jack.R: Convergence test p-value: 0.0006. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0488. > test-boot_jack.R: Convergence test p-value: 0.0024. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0002. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-EgoStat.R: Starting simulated annealing (SAN) > test-EgoStat.R: Iteration 1 of at most 4 > test-EgoStat.R: Iteration 2 of at most 4 > test-EgoStat.R: Iteration 3 of at most 4 > test-EgoStat.R: Finished simulated annealing > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Iteration 2 of at most 4 > test-boot_jack.R: Iteration 3 of at most 4 > test-boot_jack.R: Iteration 4 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0011. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0001. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-coef_recovery.R: Constructing pseudopopulation network. > test-coef_recovery.R: Starting simulated annealing (SAN) > test-coef_recovery.R: Iteration 1 of at most 4 > test-coef_recovery.R: Iteration 2 of at most 4 > test-coef_recovery.R: Iteration 3 of at most 4 > test-coef_recovery.R: Iteration 4 of at most 4 > test-coef_recovery.R: Finished simulated annealing > test-coef_recovery.R: Unable to match target stats. Using MCMLE estimation. > test-coef_recovery.R: Starting maximum pseudolikelihood estimation (MPLE): > test-coef_recovery.R: Obtaining the responsible dyads. > test-coef_recovery.R: Evaluating the predictor and response matrix. > test-coef_recovery.R: Maximizing the pseudolikelihood. > test-coef_recovery.R: Finished MPLE. > test-coef_recovery.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-coef_recovery.R: Iteration 1 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 0.3646. > test-coef_recovery.R: The log-likelihood improved by 2.8321. > test-coef_recovery.R: Iteration 2 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 0.8183. > test-coef_recovery.R: The log-likelihood improved by 3.0954. > test-coef_recovery.R: Iteration 3 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 1.0000. > test-coef_recovery.R: The log-likelihood improved by 1.4921. > test-coef_recovery.R: Step length converged once. Increasing MCMC sample size. > test-coef_recovery.R: Iteration 4 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 1.0000. > test-coef_recovery.R: The log-likelihood improved by 0.7405. > test-coef_recovery.R: Step length converged twice. Stopping. > test-coef_recovery.R: Finished MCMLE. > test-coef_recovery.R: This model was fit using MCMC. To examine model diagnostics and check > test-coef_recovery.R: for degeneracy, use the mcmc.diagnostics() function. > test-drop.R: Constructing pseudopopulation network. > test-drop.R: Starting simulated annealing (SAN) > test-drop.R: Iteration 1 of at most 4 > test-drop.R: Iteration 2 of at most 4 > test-drop.R: Iteration 3 of at most 4 > test-drop.R: Finished simulated annealing > test-drop.R: Observed statistic(s) nodematch.a are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: Unable to match target stats. Using MCMLE estimation. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-drop.R: Iteration 1 of at most 60: > test-drop.R: 1 > test-drop.R: Optimizing with step length 1.0000. > test-drop.R: The log-likelihood improved by 0.0044. > test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-drop.R: Finished MCMLE. > test-drop.R: This model was fit using MCMC. To examine model diagnostics and check > test-drop.R: for degeneracy, use the mcmc.diagnostics() function. > test-drop.R: Observed statistic(s) nodematch.a are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Evaluating log-likelihood at the estimate. > test-drop.R: > test-predict.ergm.ego.R: Constructing pseudopopulation network. > test-predict.ergm.ego.R: Starting simulated annealing (SAN) > test-predict.ergm.ego.R: Iteration 1 of at most 4 > test-predict.ergm.ego.R: Iteration 2 of at most 4 > test-predict.ergm.ego.R: Iteration 3 of at most 4 > test-predict.ergm.ego.R: Iteration 4 of at most 4 > test-predict.ergm.ego.R: Finished simulated annealing > test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.ego.R: Obtaining the responsible dyads. > test-predict.ergm.ego.R: Evaluating the predictor and response matrix. > test-predict.ergm.ego.R: Maximizing the pseudolikelihood. > test-predict.ergm.ego.R: Finished MPLE. > test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-predict.ergm.ego.R: Iteration 1 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 0.5473. > test-predict.ergm.ego.R: The log-likelihood improved by 1.8671. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: Iteration 2 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 1.0000. > test-predict.ergm.ego.R: The log-likelihood improved by 1.0036. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-predict.ergm.ego.R: Finished MCMLE. > test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. > test-predict.ergm.ego.R: Constructing pseudopopulation network. > test-predict.ergm.ego.R: Starting simulated annealing (SAN) > test-predict.ergm.ego.R: Iteration 1 of at most 4 > test-predict.ergm.ego.R: Iteration 2 of at most 4 > test-predict.ergm.ego.R: Iteration 3 of at most 4 > test-predict.ergm.ego.R: Iteration 4 of at most 4 > test-predict.ergm.ego.R: Finished simulated annealing > test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.ego.R: Obtaining the responsible dyads. > test-predict.ergm.ego.R: Evaluating the predictor and response matrix. > test-predict.ergm.ego.R: Maximizing the pseudolikelihood. > test-predict.ergm.ego.R: Finished MPLE. > test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-predict.ergm.ego.R: Iteration 1 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 0.7865. > test-predict.ergm.ego.R: The log-likelihood improved by 1.8825. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: Iteration 2 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 1.0000. > test-predict.ergm.ego.R: The log-likelihood improved by 0.3775. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-predict.ergm.ego.R: Finished MCMLE. > test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. > test-table_ppop.R: Constructing pseudopopulation network. > test-table_ppop.R: Starting simulated annealing (SAN) > test-table_ppop.R: Iteration 1 of at most 4 > test-table_ppop.R: Iteration 2 of at most 4 > test-table_ppop.R: Iteration 3 of at most 4 > test-table_ppop.R: Iteration 4 of at most 4 > test-table_ppop.R: Finished simulated annealing > test-table_ppop.R: Unable to match target stats. Using MCMLE estimation. > test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-table_ppop.R: Obtaining the responsible dyads. > test-table_ppop.R: Evaluating the predictor and response matrix. > test-table_ppop.R: Maximizing the pseudolikelihood. > test-table_ppop.R: Finished MPLE. > test-table_ppop.R: Constructing pseudopopulation network. > test-table_ppop.R: Starting simulated annealing (SAN) > test-table_ppop.R: Iteration 1 of at most 4 > test-table_ppop.R: Iteration 2 of at most 4 > test-table_ppop.R: Iteration 3 of at most 4 > test-table_ppop.R: Iteration 4 of at most 4 > test-table_ppop.R: Finished simulated annealing > test-table_ppop.R: Unable to match target stats. Using MCMLE estimation. > test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-table_ppop.R: Obtaining the responsible dyads. > test-table_ppop.R: Evaluating the predictor and response matrix. > test-table_ppop.R: Maximizing the pseudolikelihood. > test-table_ppop.R: Finished MPLE. Saving _problems/test-table_ppop-39.R > test-gof.ergm.ego.R: Constructing pseudopopulation network. > test-gof.ergm.ego.R: Starting simulated annealing (SAN) > test-gof.ergm.ego.R: Iteration 1 of at most 4 > test-gof.ergm.ego.R: Finished simulated annealing > test-gof.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-gof.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-gof.ergm.ego.R: Obtaining the responsible dyads. > test-gof.ergm.ego.R: Evaluating the predictor and response matrix. > test-gof.ergm.ego.R: Maximizing the pseudolikelihood. > test-gof.ergm.ego.R: Finished MPLE. > test-gof.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-gof.ergm.ego.R: Iteration 1 of at most 2: > test-gof.ergm.ego.R: 1 Optimizing with step length 1.0000. > test-gof.ergm.ego.R: The log-likelihood improved by 1.6103. > test-gof.ergm.ego.R: Estimating equations are not within tolerance region. > test-gof.ergm.ego.R: Iteration 2 of at most 2: > test-gof.ergm.ego.R: 1 Optimizing with step length 1.0000. > test-gof.ergm.ego.R: The log-likelihood improved by 0.0094. > test-gof.ergm.ego.R: Convergence test p-value: 0.3069. Not converged with 99% confidence; increasing sample size. > test-gof.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-gof.ergm.ego.R: Finished MCMLE. > test-gof.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-gof.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. Saving _problems/test-gof.ergm.ego-17.R Saving _problems/test-gof.ergm.ego-32.R Saving _problems/test-gof.ergm.ego-48.R [ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-table_ppop.R:39:3'): estimation and simulation work ────────── Expected `(egosim <- simulate(egofit_scl, popsize = ppop))` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:15:3'): GOF='model' works ───────────────────── Expected `z <- gof(fmhfit, GOF = "model")` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:30:3'): GOF='degree' works ──────────────────── Expected `z <- gof(fmhfit, GOF = "degree")` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:46:3'): GOF='espartners' works ──────────────── Expected `z <- gof(fmhfit, GOF = "espartners")` to run silently. Actual noise: messages. [ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ] Error: ! Test failures. Execution halted Flavor: r-release-linux-x86_64

Version: 1.1.3
Check: tests
Result: ERROR Running 'testthat.R' [77s] Running the tests in 'tests/testthat.R' failed. Complete output: > # File tests/testthat.R in package ergm.ego, 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 2015-2025 Statnet Commons > ################################################################################ > library(testthat) > library(ergm.ego) Loading required package: ergm Loading required package: network 'network' 1.20.0 (2026-02-06), 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' 4.12.0 (2026-02-17), 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: egor Loading required package: dplyr Attaching package: 'dplyr' The following objects are masked from 'package:stats': filter, lag The following objects are masked from 'package:base': intersect, setdiff, setequal, union Loading required package: tibble 'ergm.ego' 1.1.3 (2025-06-10), part of the Statnet Project * 'news(package="ergm.ego")' for changes since last version * 'citation("ergm.ego")' for citation information * 'https://statnet.org' for help, support, and other information Attaching package: 'ergm.ego' The following objects are masked from 'package:ergm': COLLAPSE_SMALLEST, snctrl The following object is masked from 'package:base': sample > > test_check("ergm.ego") Starting 2 test processes. > test-EgoStat.R: Starting simulated annealing (SAN) > test-EgoStat.R: Iteration 1 of at most 4 > test-EgoStat.R: Iteration 2 of at most 4 > test-attrmismatch.R: Constructing pseudopopulation network. > test-EgoStat.R: Iteration 3 of at most 4 > test-EgoStat.R: Finished simulated annealing > test-attrmismatch.R: Starting simulated annealing (SAN) > test-attrmismatch.R: Iteration 1 of at most 4 > test-attrmismatch.R: Iteration 2 of at most 4 > test-attrmismatch.R: Iteration 3 of at most 4 > test-attrmismatch.R: Iteration 4 of at most 4 > test-attrmismatch.R: Finished simulated annealing > test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation. > test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE): > test-attrmismatch.R: Obtaining the responsible dyads. > test-attrmismatch.R: Evaluating the predictor and response matrix. > test-attrmismatch.R: Maximizing the pseudolikelihood. > test-attrmismatch.R: Finished MPLE. > test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-attrmismatch.R: Iteration 1 of at most 60: > test-attrmismatch.R: 1 Optimizing with step length 1.0000. > test-attrmismatch.R: The log-likelihood improved by 0.0234. > test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-attrmismatch.R: Finished MCMLE. > test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check > test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function. > test-attrmismatch.R: Constructing pseudopopulation network. > test-attrmismatch.R: Starting simulated annealing (SAN) > test-attrmismatch.R: Iteration 1 of at most 4 > test-attrmismatch.R: Iteration 2 of at most 4 > test-attrmismatch.R: Iteration 3 of at most 4 > test-attrmismatch.R: Iteration 4 of at most 4 > test-attrmismatch.R: Finished simulated annealing > test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation. > test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE): > test-attrmismatch.R: Obtaining the responsible dyads. > test-attrmismatch.R: Evaluating the predictor and response matrix. > test-attrmismatch.R: Maximizing the pseudolikelihood. > test-attrmismatch.R: Finished MPLE. > test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-attrmismatch.R: Iteration 1 of at most 60: > test-attrmismatch.R: 1 > test-attrmismatch.R: Optimizing with step length 1.0000. > test-attrmismatch.R: The log-likelihood improved by 0.0421. > test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-attrmismatch.R: Finished MCMLE. > test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check > test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0223. > test-boot_jack.R: Convergence test p-value: 0.0016. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Iteration 2 of at most 4 > test-boot_jack.R: Iteration 3 of at most 4 > test-boot_jack.R: Iteration 4 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0009. > test-boot_jack.R: Convergence test p-value: 0.0006. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0488. > test-boot_jack.R: Convergence test p-value: 0.0024. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0002. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-EgoStat.R: Starting simulated annealing (SAN) > test-EgoStat.R: Iteration 1 of at most 4 > test-EgoStat.R: Iteration 2 of at most 4 > test-EgoStat.R: Iteration 3 of at most 4 > test-EgoStat.R: Finished simulated annealing > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Iteration 2 of at most 4 > test-boot_jack.R: Iteration 3 of at most 4 > test-boot_jack.R: Iteration 4 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0011. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0001. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-coef_recovery.R: Constructing pseudopopulation network. > test-coef_recovery.R: Starting simulated annealing (SAN) > test-coef_recovery.R: Iteration 1 of at most 4 > test-coef_recovery.R: Iteration 2 of at most 4 > test-coef_recovery.R: Iteration 3 of at most 4 > test-coef_recovery.R: Iteration 4 of at most 4 > test-coef_recovery.R: Finished simulated annealing > test-coef_recovery.R: Unable to match target stats. Using MCMLE estimation. > test-coef_recovery.R: Starting maximum pseudolikelihood estimation (MPLE): > test-coef_recovery.R: Obtaining the responsible dyads. > test-coef_recovery.R: Evaluating the predictor and response matrix. > test-coef_recovery.R: Maximizing the pseudolikelihood. > test-coef_recovery.R: Finished MPLE. > test-coef_recovery.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-coef_recovery.R: Iteration 1 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 0.3646. > test-coef_recovery.R: The log-likelihood improved by 2.8321. > test-coef_recovery.R: Iteration 2 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 0.8183. > test-coef_recovery.R: The log-likelihood improved by 3.0954. > test-coef_recovery.R: Iteration 3 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 1.0000. > test-coef_recovery.R: The log-likelihood improved by 1.4921. > test-coef_recovery.R: Step length converged once. Increasing MCMC sample size. > test-coef_recovery.R: Iteration 4 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 1.0000. > test-coef_recovery.R: The log-likelihood improved by 0.7405. > test-coef_recovery.R: Step length converged twice. Stopping. > test-coef_recovery.R: Finished MCMLE. > test-coef_recovery.R: This model was fit using MCMC. To examine model diagnostics and check > test-coef_recovery.R: for degeneracy, use the mcmc.diagnostics() function. > test-drop.R: Constructing pseudopopulation network. > test-drop.R: Starting simulated annealing (SAN) > test-drop.R: Iteration 1 of at most 4 > test-drop.R: Iteration 2 of at most 4 > test-drop.R: Iteration 3 of at most 4 > test-drop.R: Finished simulated annealing > test-drop.R: Observed statistic(s) nodematch.a are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: Unable to match target stats. Using MCMLE estimation. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-drop.R: Iteration 1 of at most 60: > test-drop.R: 1 > test-drop.R: Optimizing with step length 1.0000. > test-drop.R: The log-likelihood improved by 0.0044. > test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-drop.R: Finished MCMLE. > test-drop.R: This model was fit using MCMC. To examine model diagnostics and check > test-drop.R: for degeneracy, use the mcmc.diagnostics() function. > test-drop.R: Observed statistic(s) nodematch.a are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Evaluating log-likelihood at the estimate. > test-drop.R: > test-predict.ergm.ego.R: Constructing pseudopopulation network. > test-predict.ergm.ego.R: Starting simulated annealing (SAN) > test-predict.ergm.ego.R: Iteration 1 of at most 4 > test-predict.ergm.ego.R: Iteration 2 of at most 4 > test-predict.ergm.ego.R: Iteration 3 of at most 4 > test-predict.ergm.ego.R: Iteration 4 of at most 4 > test-predict.ergm.ego.R: Finished simulated annealing > test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.ego.R: Obtaining the responsible dyads. > test-predict.ergm.ego.R: Evaluating the predictor and response matrix. > test-predict.ergm.ego.R: Maximizing the pseudolikelihood. > test-predict.ergm.ego.R: Finished MPLE. > test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-predict.ergm.ego.R: Iteration 1 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 0.5473. > test-predict.ergm.ego.R: The log-likelihood improved by 1.8671. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: Iteration 2 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 1.0000. > test-predict.ergm.ego.R: The log-likelihood improved by 1.0036. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-predict.ergm.ego.R: Finished MCMLE. > test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. > test-predict.ergm.ego.R: Constructing pseudopopulation network. > test-predict.ergm.ego.R: Starting simulated annealing (SAN) > test-predict.ergm.ego.R: Iteration 1 of at most 4 > test-predict.ergm.ego.R: Iteration 2 of at most 4 > test-predict.ergm.ego.R: Iteration 3 of at most 4 > test-predict.ergm.ego.R: Iteration 4 of at most 4 > test-predict.ergm.ego.R: Finished simulated annealing > test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.ego.R: Obtaining the responsible dyads. > test-predict.ergm.ego.R: Evaluating the predictor and response matrix. > test-predict.ergm.ego.R: Maximizing the pseudolikelihood. > test-predict.ergm.ego.R: Finished MPLE. > test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-predict.ergm.ego.R: Iteration 1 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 0.7865. > test-predict.ergm.ego.R: The log-likelihood improved by 1.8825. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: Iteration 2 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 1.0000. > test-predict.ergm.ego.R: The log-likelihood improved by 0.3775. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-predict.ergm.ego.R: Finished MCMLE. > test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. > test-table_ppop.R: Constructing pseudopopulation network. > test-table_ppop.R: Starting simulated annealing (SAN) > test-table_ppop.R: Iteration 1 of at most 4 > test-table_ppop.R: Iteration 2 of at most 4 > test-table_ppop.R: Iteration 3 of at most 4 > test-table_ppop.R: Iteration 4 of at most 4 > test-table_ppop.R: Finished simulated annealing > test-table_ppop.R: Unable to match target stats. Using MCMLE estimation. > test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-table_ppop.R: Obtaining the responsible dyads. > test-table_ppop.R: Evaluating the predictor and response matrix. > test-table_ppop.R: Maximizing the pseudolikelihood. > test-table_ppop.R: Finished MPLE. > test-table_ppop.R: Constructing pseudopopulation network. > test-table_ppop.R: Starting simulated annealing (SAN) > test-table_ppop.R: Iteration 1 of at most 4 > test-table_ppop.R: Iteration 2 of at most 4 > test-table_ppop.R: Iteration 3 of at most 4 > test-table_ppop.R: Iteration 4 of at most 4 > test-table_ppop.R: Finished simulated annealing > test-table_ppop.R: Unable to match target stats. Using MCMLE estimation. > test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-table_ppop.R: Obtaining the responsible dyads. > test-table_ppop.R: Evaluating the predictor and response matrix. > test-table_ppop.R: Maximizing the pseudolikelihood. > test-table_ppop.R: Finished MPLE. Saving _problems/test-table_ppop-39.R > test-gof.ergm.ego.R: Constructing pseudopopulation network. > test-gof.ergm.ego.R: Starting simulated annealing (SAN) > test-gof.ergm.ego.R: Iteration 1 of at most 4 > test-gof.ergm.ego.R: Finished simulated annealing > test-gof.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-gof.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-gof.ergm.ego.R: Obtaining the responsible dyads. > test-gof.ergm.ego.R: Evaluating the predictor and response matrix. > test-gof.ergm.ego.R: Maximizing the pseudolikelihood. > test-gof.ergm.ego.R: Finished MPLE. > test-gof.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-gof.ergm.ego.R: Iteration 1 of at most 2: > test-gof.ergm.ego.R: 1 > test-gof.ergm.ego.R: Optimizing with step length 1.0000. > test-gof.ergm.ego.R: The log-likelihood improved by 1.6103. > test-gof.ergm.ego.R: Estimating equations are not within tolerance region. > test-gof.ergm.ego.R: Iteration 2 of at most 2: > test-gof.ergm.ego.R: 1 > test-gof.ergm.ego.R: Optimizing with step length 1.0000. > test-gof.ergm.ego.R: The log-likelihood improved by 0.0094. > test-gof.ergm.ego.R: Convergence test p-value: 0.3069. Not converged with 99% confidence; increasing sample size. > test-gof.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-gof.ergm.ego.R: Finished MCMLE. > test-gof.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-gof.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. Saving _problems/test-gof.ergm.ego-17.R Saving _problems/test-gof.ergm.ego-32.R Saving _problems/test-gof.ergm.ego-48.R [ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-table_ppop.R:39:3'): estimation and simulation work ────────── Expected `(egosim <- simulate(egofit_scl, popsize = ppop))` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:15:3'): GOF='model' works ───────────────────── Expected `z <- gof(fmhfit, GOF = "model")` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:30:3'): GOF='degree' works ──────────────────── Expected `z <- gof(fmhfit, GOF = "degree")` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:46:3'): GOF='espartners' works ──────────────── Expected `z <- gof(fmhfit, GOF = "espartners")` to run silently. Actual noise: messages. [ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ] Error: ! Test failures. Execution halted Flavor: r-release-windows-x86_64

Version: 1.1.3
Check: tests
Result: ERROR Running 'testthat.R' [105s] Running the tests in 'tests/testthat.R' failed. Complete output: > # File tests/testthat.R in package ergm.ego, 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 2015-2025 Statnet Commons > ################################################################################ > library(testthat) > library(ergm.ego) Loading required package: ergm Loading required package: network 'network' 1.20.0 (2026-02-06), 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' 4.12.0 (2026-02-17), 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: egor Loading required package: dplyr Attaching package: 'dplyr' The following objects are masked from 'package:stats': filter, lag The following objects are masked from 'package:base': intersect, setdiff, setequal, union Loading required package: tibble 'ergm.ego' 1.1.3 (2025-06-10), part of the Statnet Project * 'news(package="ergm.ego")' for changes since last version * 'citation("ergm.ego")' for citation information * 'https://statnet.org' for help, support, and other information Attaching package: 'ergm.ego' The following objects are masked from 'package:ergm': COLLAPSE_SMALLEST, snctrl The following object is masked from 'package:base': sample > > test_check("ergm.ego") Starting 2 test processes. > test-attrmismatch.R: Constructing pseudopopulation network. > test-EgoStat.R: Starting simulated annealing (SAN) > test-EgoStat.R: Iteration 1 of at most 4 > test-EgoStat.R: Iteration 2 of at most 4 > test-EgoStat.R: Iteration 3 of at most 4 > test-attrmismatch.R: Starting simulated annealing (SAN) > test-EgoStat.R: Finished simulated annealing > test-attrmismatch.R: Iteration 1 of at most 4 > test-attrmismatch.R: Finished simulated annealing > test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation. > test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE): > test-attrmismatch.R: Obtaining the responsible dyads. > test-attrmismatch.R: Evaluating the predictor and response matrix. > test-attrmismatch.R: Maximizing the pseudolikelihood. > test-attrmismatch.R: Finished MPLE. > test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-attrmismatch.R: Iteration 1 of at most 60: > test-attrmismatch.R: 1 Optimizing with step length 1.0000. > test-attrmismatch.R: The log-likelihood improved by 0.0068. > test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-attrmismatch.R: Finished MCMLE. > test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check > test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function. > test-attrmismatch.R: Constructing pseudopopulation network. > test-attrmismatch.R: Starting simulated annealing (SAN) > test-attrmismatch.R: Iteration 1 of at most 4 > test-attrmismatch.R: Finished simulated annealing > test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation. > test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE): > test-attrmismatch.R: Obtaining the responsible dyads. > test-attrmismatch.R: Evaluating the predictor and response matrix. > test-attrmismatch.R: Maximizing the pseudolikelihood. > test-attrmismatch.R: Finished MPLE. > test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-attrmismatch.R: Iteration 1 of at most 60: > test-attrmismatch.R: 1 > test-attrmismatch.R: Optimizing with step length 1.0000. > test-attrmismatch.R: The log-likelihood improved by 0.0004. > test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-attrmismatch.R: Finished MCMLE. > test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check > test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0223. > test-boot_jack.R: Convergence test p-value: 0.0016. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Iteration 2 of at most 4 > test-boot_jack.R: Iteration 3 of at most 4 > test-boot_jack.R: Iteration 4 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0009. > test-boot_jack.R: Convergence test p-value: 0.0006. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0488. > test-boot_jack.R: Convergence test p-value: 0.0024. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0002. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-EgoStat.R: Starting simulated annealing (SAN) > test-EgoStat.R: Iteration 1 of at most 4 > test-EgoStat.R: Iteration 2 of at most 4 > test-EgoStat.R: Iteration 3 of at most 4 > test-EgoStat.R: Finished simulated annealing > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Iteration 2 of at most 4 > test-boot_jack.R: Iteration 3 of at most 4 > test-boot_jack.R: Iteration 4 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0011. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-boot_jack.R: Constructing pseudopopulation network. > test-boot_jack.R: Starting simulated annealing (SAN) > test-boot_jack.R: Iteration 1 of at most 4 > test-boot_jack.R: Finished simulated annealing > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation. > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE): > test-boot_jack.R: Obtaining the responsible dyads. > test-boot_jack.R: Evaluating the predictor and response matrix. > test-boot_jack.R: Maximizing the pseudolikelihood. > test-boot_jack.R: Finished MPLE. > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-boot_jack.R: Iteration 1 of at most 60: > test-boot_jack.R: 1 > test-boot_jack.R: Optimizing with step length 1.0000. > test-boot_jack.R: The log-likelihood improved by 0.0001. > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-boot_jack.R: Finished MCMLE. > test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function. > test-coef_recovery.R: Constructing pseudopopulation network. > test-coef_recovery.R: Starting simulated annealing (SAN) > test-coef_recovery.R: Iteration 1 of at most 4 > test-coef_recovery.R: Iteration 2 of at most 4 > test-coef_recovery.R: Iteration 3 of at most 4 > test-coef_recovery.R: Iteration 4 of at most 4 > test-coef_recovery.R: Finished simulated annealing > test-coef_recovery.R: Unable to match target stats. Using MCMLE estimation. > test-coef_recovery.R: Starting maximum pseudolikelihood estimation (MPLE): > test-coef_recovery.R: Obtaining the responsible dyads. > test-coef_recovery.R: Evaluating the predictor and response matrix. > test-coef_recovery.R: Maximizing the pseudolikelihood. > test-coef_recovery.R: Finished MPLE. > test-coef_recovery.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-coef_recovery.R: Iteration 1 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 0.3646. > test-coef_recovery.R: The log-likelihood improved by 2.8321. > test-coef_recovery.R: Iteration 2 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 0.8183. > test-coef_recovery.R: The log-likelihood improved by 3.0954. > test-coef_recovery.R: Iteration 3 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 1.0000. > test-coef_recovery.R: The log-likelihood improved by 1.4921. > test-coef_recovery.R: Step length converged once. Increasing MCMC sample size. > test-coef_recovery.R: Iteration 4 of at most 60: > test-coef_recovery.R: 1 > test-coef_recovery.R: Optimizing with step length 1.0000. > test-coef_recovery.R: The log-likelihood improved by 0.7405. > test-coef_recovery.R: Step length converged twice. Stopping. > test-coef_recovery.R: Finished MCMLE. > test-coef_recovery.R: This model was fit using MCMC. To examine model diagnostics and check > test-coef_recovery.R: for degeneracy, use the mcmc.diagnostics() function. > test-drop.R: Constructing pseudopopulation network. > test-drop.R: Starting simulated annealing (SAN) > test-drop.R: Iteration 1 of at most 4 > test-drop.R: Iteration 2 of at most 4 > test-drop.R: Iteration 3 of at most 4 > test-drop.R: Finished simulated annealing > test-drop.R: Observed statistic(s) nodematch.a are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: Unable to match target stats. Using MCMLE estimation. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-drop.R: Iteration 1 of at most 60: > test-drop.R: 1 Optimizing with step length 1.0000. > test-drop.R: The log-likelihood improved by 0.0044. > test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-drop.R: Finished MCMLE. > test-drop.R: This model was fit using MCMC. To examine model diagnostics and check > test-drop.R: for degeneracy, use the mcmc.diagnostics() function. > test-drop.R: Observed statistic(s) nodematch.a are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Evaluating log-likelihood at the estimate. > test-drop.R: > test-predict.ergm.ego.R: Constructing pseudopopulation network. > test-predict.ergm.ego.R: Starting simulated annealing (SAN) > test-predict.ergm.ego.R: Iteration 1 of at most 4 > test-predict.ergm.ego.R: Iteration 2 of at most 4 > test-predict.ergm.ego.R: Iteration 3 of at most 4 > test-predict.ergm.ego.R: Iteration 4 of at most 4 > test-predict.ergm.ego.R: Finished simulated annealing > test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.ego.R: Obtaining the responsible dyads. > test-predict.ergm.ego.R: Evaluating the predictor and response matrix. > test-predict.ergm.ego.R: Maximizing the pseudolikelihood. > test-predict.ergm.ego.R: Finished MPLE. > test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-predict.ergm.ego.R: Iteration 1 of at most 2: > test-predict.ergm.ego.R: 1 Optimizing with step length 0.5473. > test-predict.ergm.ego.R: The log-likelihood improved by 1.8671. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: Iteration 2 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 1.0000. > test-predict.ergm.ego.R: The log-likelihood improved by 1.0036. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-predict.ergm.ego.R: Finished MCMLE. > test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. > test-predict.ergm.ego.R: Constructing pseudopopulation network. > test-predict.ergm.ego.R: Starting simulated annealing (SAN) > test-predict.ergm.ego.R: Iteration 1 of at most 4 > test-predict.ergm.ego.R: Iteration 2 of at most 4 > test-predict.ergm.ego.R: Iteration 3 of at most 4 > test-predict.ergm.ego.R: Iteration 4 of at most 4 > test-predict.ergm.ego.R: Finished simulated annealing > test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.ego.R: Obtaining the responsible dyads. > test-predict.ergm.ego.R: Evaluating the predictor and response matrix. > test-predict.ergm.ego.R: Maximizing the pseudolikelihood. > test-predict.ergm.ego.R: Finished MPLE. > test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-predict.ergm.ego.R: Iteration 1 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 0.7865. > test-predict.ergm.ego.R: The log-likelihood improved by 1.8825. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: Iteration 2 of at most 2: > test-predict.ergm.ego.R: 1 > test-predict.ergm.ego.R: Optimizing with step length 1.0000. > test-predict.ergm.ego.R: The log-likelihood improved by 0.3775. > test-predict.ergm.ego.R: Estimating equations are not within tolerance region. > test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-predict.ergm.ego.R: Finished MCMLE. > test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. > test-table_ppop.R: Constructing pseudopopulation network. > test-table_ppop.R: Starting simulated annealing (SAN) > test-table_ppop.R: Iteration 1 of at most 4 > test-table_ppop.R: Iteration 2 of at most 4 > test-table_ppop.R: Iteration 3 of at most 4 > test-table_ppop.R: Iteration 4 of at most 4 > test-table_ppop.R: Finished simulated annealing > test-table_ppop.R: Unable to match target stats. Using MCMLE estimation. > test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-table_ppop.R: Obtaining the responsible dyads. > test-table_ppop.R: Evaluating the predictor and response matrix. > test-table_ppop.R: Maximizing the pseudolikelihood. > test-table_ppop.R: Finished MPLE. > test-table_ppop.R: Constructing pseudopopulation network. > test-table_ppop.R: Starting simulated annealing (SAN) > test-table_ppop.R: Iteration 1 of at most 4 > test-table_ppop.R: Iteration 2 of at most 4 > test-table_ppop.R: Iteration 3 of at most 4 > test-table_ppop.R: Iteration 4 of at most 4 > test-table_ppop.R: Finished simulated annealing > test-table_ppop.R: Unable to match target stats. Using MCMLE estimation. > test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-table_ppop.R: Obtaining the responsible dyads. > test-table_ppop.R: Evaluating the predictor and response matrix. > test-table_ppop.R: Maximizing the pseudolikelihood. > test-table_ppop.R: Finished MPLE. Saving _problems/test-table_ppop-39.R > test-gof.ergm.ego.R: Constructing pseudopopulation network. > test-gof.ergm.ego.R: Starting simulated annealing (SAN) > test-gof.ergm.ego.R: Iteration 1 of at most 4 > test-gof.ergm.ego.R: Finished simulated annealing > test-gof.ergm.ego.R: Unable to match target stats. Using MCMLE estimation. > test-gof.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE): > test-gof.ergm.ego.R: Obtaining the responsible dyads. > test-gof.ergm.ego.R: Evaluating the predictor and response matrix. > test-gof.ergm.ego.R: Maximizing the pseudolikelihood. > test-gof.ergm.ego.R: Finished MPLE. > test-gof.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-gof.ergm.ego.R: Iteration 1 of at most 2: > test-gof.ergm.ego.R: 1 > test-gof.ergm.ego.R: Optimizing with step length 1.0000. > test-gof.ergm.ego.R: The log-likelihood improved by 1.6103. > test-gof.ergm.ego.R: Estimating equations are not within tolerance region. > test-gof.ergm.ego.R: Iteration 2 of at most 2: > test-gof.ergm.ego.R: 1 > test-gof.ergm.ego.R: Optimizing with step length 1.0000. > test-gof.ergm.ego.R: The log-likelihood improved by 0.0094. > test-gof.ergm.ego.R: Convergence test p-value: 0.3069. Not converged with 99% confidence; increasing sample size. > test-gof.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-gof.ergm.ego.R: Finished MCMLE. > test-gof.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check > test-gof.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function. Saving _problems/test-gof.ergm.ego-17.R Saving _problems/test-gof.ergm.ego-32.R Saving _problems/test-gof.ergm.ego-48.R [ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-table_ppop.R:39:3'): estimation and simulation work ────────── Expected `(egosim <- simulate(egofit_scl, popsize = ppop))` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:15:3'): GOF='model' works ───────────────────── Expected `z <- gof(fmhfit, GOF = "model")` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:30:3'): GOF='degree' works ──────────────────── Expected `z <- gof(fmhfit, GOF = "degree")` to run silently. Actual noise: messages. ── Failure ('test-gof.ergm.ego.R:46:3'): GOF='espartners' works ──────────────── Expected `z <- gof(fmhfit, GOF = "espartners")` to run silently. Actual noise: messages. [ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ] Error: ! Test failures. Execution halted Flavor: r-oldrel-windows-x86_64

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