* using log directory 'd:/Rcompile/CRANpkg/local/4.5/ergm.ego.Rcheck'
* using R version 4.5.2 Patched (2026-02-13 r89426 ucrt)
* using platform: x86_64-w64-mingw32
* R was compiled by
    gcc.exe (GCC) 14.3.0
    GNU Fortran (GCC) 14.3.0
* running under: Windows Server 2022 x64 (build 20348)
* using session charset: UTF-8
* checking for file 'ergm.ego/DESCRIPTION' ... OK
* this is package 'ergm.ego' version '1.1.3'
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking whether package 'ergm.ego' can be installed ... OK
* used C compiler: 'gcc.exe (GCC) 14.3.0'
* checking installed package size ... OK
* checking package directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... [3s] OK
* checking whether the package can be loaded with stated dependencies ... [3s] OK
* checking whether the package can be unloaded cleanly ... [3s] OK
* checking whether the namespace can be loaded with stated dependencies ... [3s] OK
* checking whether the namespace can be unloaded cleanly ... [3s] OK
* checking loading without being on the library search path ... [3s] OK
* checking whether startup messages can be suppressed ... [3s] OK
* checking use of S3 registration ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... [12s] OK
* checking Rd files ... [1s] OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of 'data' directory ... OK
* checking data for non-ASCII characters ... [3s] OK
* checking LazyData ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking pragmas in C/C++ headers and code ... OK
* checking compiled code ... OK
* checking examples ... [13s] OK
* checking for unstated dependencies in 'tests' ... OK
* checking tests ... [75s] ERROR
  Running 'testthat.R' [74s]
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-attrmismatch.R: Iteration 1 of at most 4
  > test-EgoStat.R: Finished simulated annealing
  > 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 0.0172.
  > 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 0.0348.
  > 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-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: 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-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 
  > 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 0.0982.
  > test-gof.ergm.ego.R: Convergence test p-value: 0.1273. Not converged with 99% confidence; increasing sample size.
  > 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 0.0086.
  > test-gof.ergm.ego.R: Convergence test p-value: 0.0466. 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 ... [19s] OK
* checking HTML version of manual ... [4s] OK
* DONE
Status: 1 ERROR