• using R Under development (unstable) (2026-02-18 r89435)
• using platform: x86_64-pc-linux-gnu
• R was compiled by     gcc-15 (Debian 15.2.0-12) 15.2.0     GNU Fortran (Debian 15.2.0-12) 15.2.0
• running under: Debian GNU/Linux forky/sid
• using session charset: UTF-8 * current time: 2026-02-19 15:03:12 UTC
• checking for file ‘ergm.ego/DESCRIPTION’ ... OK
• this is package ‘ergm.ego’ version ‘1.1.3’
• package encoding: UTF-8
• checking CRAN incoming feasibility ... [1s/1s] OK
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• checking for executable files ... OK
• checking for hidden files and directories ... OK
• checking for portable file names ... OK
• checking for sufficient/correct file permissions ... OK
• checking whether package ‘ergm.ego’ can be installed ... OK See the install log for details.
• used C compiler: ‘gcc-15 (Debian 15.2.0-12) 15.2.0’
• checking package directory ... OK
• checking for future file timestamps ... OK
• checking DESCRIPTION meta-information ... OK
• checking top-level files ... OK
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• checking code files for non-ASCII characters ... OK
• checking R files for syntax errors ... OK
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• checking use of S3 registration ... OK
• checking dependencies in R code ... OK
• checking S3 generic/method consistency ... OK
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• checking for unstated dependencies in examples ... OK
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• checking examples ... [8s/12s] OK
• checking for unstated dependencies in ‘tests’ ... OK
• checking tests ... [83s/58s] ERROR   Running ‘testthat.R’ [83s/57s] 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-attrmismatch.R: Iteration 1 of at most 4   > test-EgoStat.R: Iteration 3 of at most 4   > test-attrmismatch.R: Iteration 2 of at most 4   > test-attrmismatch.R: Iteration 3 of at most 4   > test-EgoStat.R: Finished simulated annealing   > 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 0.0314.   > 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 0.0195.   > 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-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   > 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-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-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 Optimizing with step length 1.0000.   > test-EgoStat.R: Iteration 3 of at most 4   > test-EgoStat.R: Finished simulated annealing   > 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   > 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.   > test-drop.R: 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   > 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
• checking PDF version of manual ... [5s/8s] OK
• checking HTML version of manual ... [2s/3s] OK
• checking for non-standard things in the check directory ... OK
• DONE

Status: 1 ERROR