Package check result: NOTE Check: CRAN incoming feasibility, Result: NOTE Maintainer: ‘Pavel N. Krivitsky ’ License components with restrictions and base license permitting such: GPL-3 + file LICENSE File 'LICENSE': -------------------------------------------------- License for the 'statnet' component package 'ergm' -------------------------------------------------- This software is distributed under the GPL-3 license. It is free, open source, and has the following attribution requirements (GPL Section 7): (a) you agree to retain in 'ergm' and any modifications to 'ergm' the copyright, author attribution and URL information as provided at statnet.org/attribution (b) you agree that 'ergm' and any modifications to 'ergm' will, when used, display the attribution: Based on 'statnet' project software (statnet.org). For license and citation information see statnet.org/attribution -------------------------------------------------- What does this mean? ==================== If you are modifying 'ergm' or adopting any source code from 'ergm' for use in another application, you must ensure that the copyright and attributions mentioned in the license above appear in the code of your modified version or application. These attributions must also appear when the package is loaded (e.g., via 'library' or 'require'). Enjoy! Mark S. Handcock, University of California, Los Angeles David R. Hunter, Penn State University Carter T. Butts, University of California, Irvine Steven M. Goodreau, University of Washington Pavel N. Krivitsky, University of New South Wales Michał Bojanowski, Kozminski University Martina Morris, University of Washington The 'statnet' development team Copyright 2003-2026 Other contributors ==================== khash.h "library" is Copyright (c) 2008, 2009, 2011 by Attractive Chaos , incorporated under the MIT license. kvec.h "library" is Copyright (c) 2008 by Attractive Chaos , incorporated under the MIT license. Found the following (possibly) invalid URLs: URL: https://csss.uw.edu/research/working-papers/assessing-degeneracy-statistical-models-social-networks From: man/ergm-package.Rd man/ergm.Rd inst/doc/ergm.html Status: Error Message: SSL peer certificate or SSH remote key was not OK [csss.uw.edu]: SSL certificate verification failed: certificate signer not trusted. (CAfile: /etc/ssl/certs/ca-certificates.crt CRLfile: none) Changes to worse in reverse depends: Package: ergm.ego Check: tests New result: ERROR Running ‘testthat.R’ [86s/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-EgoStat.R: Starting simulated annealing (SAN) > test-EgoStat.R: Iteration 1 of at most 4 > test-attrmismatch.R: Constructing pseudopopulation network. > 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: 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 0.0056. > 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 0.0207. > 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 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-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 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-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-coef_recovery.R: Constructing pseudopopulation network. > test-EgoStat.R: Iteration 3 of at most 4 > test-EgoStat.R: Finished simulated annealing > 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 > 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