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> #  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 2015-2026 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' 1.1.4 (2026-03-02), 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 0.0210.
> 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 0.0038.
> 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. 
> 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-EgoStat.R: Starting simulated annealing (SAN)
> test-EgoStat.R: Iteration 1 of at most 4
> test-boot_jack.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-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-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-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-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-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-gof.ergm.ego.R: Sampling ====>--------------------------   13% |  ETA:  9s
> 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-gof.ergm.ego.R: Sampling =============>-----------------   44% |  ETA:  6s
> 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-gof.ergm.ego.R: Sampling ======================>--------   75% |  ETA:  2s
> 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.
> 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-gof.ergm.ego.R: Sampling =========>---------------------   30% |  ETA:  8s
> test-gof.ergm.ego.R: Sampling =================>-------------   56% |  ETA:  5s
> test-gof.ergm.ego.R: Sampling ========================>------   81% |  ETA:  2s
> test-gof.ergm.ego.R: Sampling ========>----------------------   28% |  ETA:  9s
> test-gof.ergm.ego.R: Sampling ================>--------------   52% |  ETA:  6s
> test-gof.ergm.ego.R: Sampling ======================>--------   75% |  ETA:  3s
> test-gof.ergm.ego.R: Sampling ==============================>   99% |  ETA:  0s
> 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 0.4978.
> test-gof.ergm.ego.R: The log-likelihood improved by 1.9608.
> 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 0.9863.
> test-gof.ergm.ego.R: The log-likelihood improved by 3.3433.
> 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.
[ FAIL 0 | WARN 2 | SKIP 0 | PASS 108 ]

[ FAIL 0 | WARN 2 | SKIP 0 | PASS 108 ]
> 
> proc.time()
   user  system elapsed 
   3.56    0.54   59.45