Last updated on 2026-02-18 10:49:54 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.1.3 | 19.61 | 258.57 | 278.18 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.1.3 | 15.24 | 175.59 | 190.83 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.1.3 | 34.00 | 427.89 | 461.89 | ERROR | |
| r-devel-linux-x86_64-fedora-gcc | 1.1.3 | 35.00 | 409.45 | 444.45 | ERROR | |
| r-devel-macos-arm64 | 1.1.3 | 5.00 | 55.00 | 60.00 | OK | |
| r-devel-windows-x86_64 | 1.1.3 | 24.00 | 205.00 | 229.00 | OK | |
| r-patched-linux-x86_64 | 1.1.3 | 19.28 | 239.96 | 259.24 | OK | |
| r-release-linux-x86_64 | 1.1.3 | 19.57 | 242.61 | 262.18 | OK | |
| r-release-macos-arm64 | 1.1.3 | OK | ||||
| r-release-macos-x86_64 | 1.1.3 | 13.00 | 203.00 | 216.00 | OK | |
| r-release-windows-x86_64 | 1.1.3 | 23.00 | 202.00 | 225.00 | OK | |
| r-oldrel-macos-arm64 | 1.1.3 | OK | ||||
| r-oldrel-macos-x86_64 | 1.1.3 | 13.00 | 203.00 | 216.00 | OK | |
| r-oldrel-windows-x86_64 | 1.1.3 | 31.00 | 267.00 | 298.00 | OK |
Version: 1.1.3
Check: tests
Result: ERROR
Running ‘testthat.R’ [213s/420s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # File tests/testthat.R in package ergm.ego, part of the Statnet suite of
> # packages for network analysis, https://statnet.org .
> #
> # This software is distributed under the GPL-3 license. It is free, open
> # source, and has the attribution requirements (GPL Section 7) at
> # https://statnet.org/attribution .
> #
> # Copyright 2015-2025 Statnet Commons
> ################################################################################
> library(testthat)
> library(ergm.ego)
Loading required package: ergm
Loading required package: network
'network' 1.20.0 (2026-02-06), part of the Statnet Project
* 'news(package="network")' for changes since last version
* 'citation("network")' for citation information
* 'https://statnet.org' for help, support, and other information
'ergm' 4.12.0 (2026-02-17), part of the Statnet Project
* 'news(package="ergm")' for changes since last version
* 'citation("ergm")' for citation information
* 'https://statnet.org' for help, support, and other information
'ergm' 4 is a major update that introduces some backwards-incompatible
changes. Please type 'news(package="ergm")' for a list of major
changes.
Loading required package: egor
Loading required package: dplyr
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Loading required package: tibble
'ergm.ego' 1.1.3 (2025-06-10), part of the Statnet Project
* 'news(package="ergm.ego")' for changes since last version
* 'citation("ergm.ego")' for citation information
* 'https://statnet.org' for help, support, and other information
Attaching package: 'ergm.ego'
The following objects are masked from 'package:ergm':
COLLAPSE_SMALLEST, snctrl
The following object is masked from 'package:base':
sample
>
> test_check("ergm.ego")
Starting 2 test processes.
> test-EgoStat.R: Starting simulated annealing (SAN)
> test-EgoStat.R: Iteration 1 of at most 4
> test-EgoStat.R: Iteration 2 of at most 4
> test-EgoStat.R: Iteration 3 of at most 4
> test-EgoStat.R: Finished simulated annealing
> test-attrmismatch.R: Constructing pseudopopulation network.
> test-attrmismatch.R: Starting simulated annealing (SAN)
> test-attrmismatch.R: Iteration 1 of at most 4
> test-attrmismatch.R: Iteration 2 of at most 4
> test-attrmismatch.R: Iteration 3 of at most 4
> test-attrmismatch.R: Iteration 4 of at most 4
> test-attrmismatch.R: Finished simulated annealing
> test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation.
> test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-attrmismatch.R: Obtaining the responsible dyads.
> test-attrmismatch.R: Evaluating the predictor and response matrix.
> test-attrmismatch.R: Maximizing the pseudolikelihood.
> test-attrmismatch.R: Finished MPLE.
> test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-attrmismatch.R: Iteration 1 of at most 60:
> test-attrmismatch.R: 1 Optimizing with step length 1.0000.
> test-attrmismatch.R: The log-likelihood improved by 0.0108.
> test-attrmismatch.R: Convergence test p-value: < 0.0001.
> test-attrmismatch.R: Converged with 99% confidence.
> test-attrmismatch.R: Finished MCMLE.
> test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check
> test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function.
> test-attrmismatch.R: Constructing pseudopopulation network.
> test-attrmismatch.R: Starting simulated annealing (SAN)
> test-attrmismatch.R: Iteration 1 of at most 4
> test-attrmismatch.R: Iteration 2 of at most 4
> test-attrmismatch.R: Iteration 3 of at most 4
> test-attrmismatch.R: Iteration 4 of at most 4
> test-attrmismatch.R: Finished simulated annealing
> test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation.
> test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-attrmismatch.R: Obtaining the responsible dyads.
> test-attrmismatch.R: Evaluating the predictor and response matrix.
> test-attrmismatch.R: Maximizing the pseudolikelihood.
> test-attrmismatch.R: Finished MPLE.
> test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-attrmismatch.R: Iteration 1 of at most 60:
> test-attrmismatch.R: 1
> test-attrmismatch.R: Optimizing with step length 1.0000.
> test-attrmismatch.R: The log-likelihood improved by 0.0176.
> test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-attrmismatch.R: Finished MCMLE.
> test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check
> test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function.
> test-boot_jack.R: Constructing pseudopopulation network.
> test-boot_jack.R: Starting simulated annealing (SAN)
> test-boot_jack.R: Iteration 1 of at most 4
> test-boot_jack.R: Finished simulated annealing
> test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
> test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-boot_jack.R: Obtaining the responsible dyads.
> test-boot_jack.R: Evaluating the predictor and response matrix.
> test-boot_jack.R: Maximizing the pseudolikelihood.
> test-boot_jack.R: Finished MPLE.
> test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-boot_jack.R: Iteration 1 of at most 60:
> test-boot_jack.R: 1
> test-boot_jack.R: Optimizing with step length 1.0000.
> test-boot_jack.R: The log-likelihood improved by 0.0223.
> test-boot_jack.R: Convergence test p-value: 0.0016.
> test-boot_jack.R: Converged with 99% confidence.
> test-boot_jack.R: Finished MCMLE.
> test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check
> test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
> test-boot_jack.R: Constructing pseudopopulation network.
> test-boot_jack.R: Starting simulated annealing (SAN)
> test-boot_jack.R: Iteration 1 of at most 4
> test-boot_jack.R: Iteration 2 of at most 4
> test-boot_jack.R: Iteration 3 of at most 4
> test-boot_jack.R: Iteration 4 of at most 4
> test-boot_jack.R: Finished simulated annealing
> test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
> test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-boot_jack.R: Obtaining the responsible dyads.
> test-boot_jack.R: Evaluating the predictor and response matrix.
> test-boot_jack.R: Maximizing the pseudolikelihood.
> test-boot_jack.R: Finished MPLE.
> test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-boot_jack.R: Iteration 1 of at most 60:
> test-boot_jack.R: 1
> test-boot_jack.R: Optimizing with step length 1.0000.
> test-boot_jack.R: The log-likelihood improved by 0.0009.
> test-boot_jack.R: Convergence test p-value: 0.0006.
> test-boot_jack.R: Converged with 99% confidence.
> test-boot_jack.R: Finished MCMLE.
> test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check
> test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
> test-boot_jack.R: Constructing pseudopopulation network.
> test-boot_jack.R: Starting simulated annealing (SAN)
> test-boot_jack.R: Iteration 1 of at most 4
> test-boot_jack.R: Finished simulated annealing
> test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
> test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-boot_jack.R: Obtaining the responsible dyads.
> test-boot_jack.R: Evaluating the predictor and response matrix.
> test-boot_jack.R: Maximizing the pseudolikelihood.
> test-boot_jack.R: Finished MPLE.
> test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-boot_jack.R: Iteration 1 of at most 60:
> test-boot_jack.R: 1
> test-boot_jack.R: Optimizing with step length 1.0000.
> test-boot_jack.R: The log-likelihood improved by 0.0488.
> test-boot_jack.R: Convergence test p-value: 0.0024.
> test-boot_jack.R: Converged with 99% confidence.
> test-boot_jack.R: Finished MCMLE.
> test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check
> test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
> test-boot_jack.R: Constructing pseudopopulation network.
> test-boot_jack.R: Starting simulated annealing (SAN)
> test-boot_jack.R: Iteration 1 of at most 4
> test-boot_jack.R: Finished simulated annealing
> test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
> test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-boot_jack.R: Obtaining the responsible dyads.
> test-boot_jack.R: Evaluating the predictor and response matrix.
> test-boot_jack.R: Maximizing the pseudolikelihood.
> test-boot_jack.R: Finished MPLE.
> test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-boot_jack.R: Iteration 1 of at most 60:
> test-boot_jack.R: 1
> test-boot_jack.R: Optimizing with step length 1.0000.
> test-boot_jack.R: The log-likelihood improved by 0.0002.
> test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-boot_jack.R: Finished MCMLE.
> test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check
> test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
> test-boot_jack.R: Constructing pseudopopulation network.
> test-EgoStat.R: Starting simulated annealing (SAN)
> test-EgoStat.R: Iteration 1 of at most 4
> test-EgoStat.R: Iteration 2 of at most 4
> test-EgoStat.R: Iteration 3 of at most 4
> test-EgoStat.R: Finished simulated annealing
> test-boot_jack.R: Starting simulated annealing (SAN)
> test-boot_jack.R: Iteration 1 of at most 4
> test-boot_jack.R: Iteration 2 of at most 4
> test-boot_jack.R: Iteration 3 of at most 4
> test-boot_jack.R: Iteration 4 of at most 4
> test-boot_jack.R: Finished simulated annealing
> test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
> test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-boot_jack.R: Obtaining the responsible dyads.
> test-boot_jack.R: Evaluating the predictor and response matrix.
> test-boot_jack.R: Maximizing the pseudolikelihood.
> test-boot_jack.R: Finished MPLE.
> test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-boot_jack.R: Iteration 1 of at most 60:
> test-boot_jack.R: 1
> test-boot_jack.R: Optimizing with step length 1.0000.
> test-boot_jack.R: The log-likelihood improved by 0.0011.
> test-boot_jack.R: Convergence test p-value: < 0.0001.
> test-boot_jack.R: Converged with 99% confidence.
> test-boot_jack.R: Finished MCMLE.
> test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check
> test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
> test-boot_jack.R: Constructing pseudopopulation network.
> test-boot_jack.R: Starting simulated annealing (SAN)
> test-boot_jack.R: Iteration 1 of at most 4
> test-boot_jack.R: Finished simulated annealing
> test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
> test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-boot_jack.R: Obtaining the responsible dyads.
> test-boot_jack.R: Evaluating the predictor and response matrix.
> test-boot_jack.R: Maximizing the pseudolikelihood.
> test-boot_jack.R: Finished MPLE.
> test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-boot_jack.R: Iteration 1 of at most 60:
> test-boot_jack.R: 1
> test-boot_jack.R: Optimizing with step length 1.0000.
> test-boot_jack.R: The log-likelihood improved by 0.0001.
> test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-boot_jack.R: Finished MCMLE.
> test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check
> test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
> test-coef_recovery.R: Constructing pseudopopulation network.
> test-coef_recovery.R: Starting simulated annealing (SAN)
> test-coef_recovery.R: Iteration 1 of at most 4
> test-coef_recovery.R: Iteration 2 of at most 4
> test-coef_recovery.R: Iteration 3 of at most 4
> test-coef_recovery.R: Iteration 4 of at most 4
> test-coef_recovery.R: Finished simulated annealing
> test-coef_recovery.R: Unable to match target stats. Using MCMLE estimation.
> test-coef_recovery.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-coef_recovery.R: Obtaining the responsible dyads.
> test-coef_recovery.R: Evaluating the predictor and response matrix.
> test-coef_recovery.R: Maximizing the pseudolikelihood.
> test-coef_recovery.R: Finished MPLE.
> test-coef_recovery.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-coef_recovery.R: Iteration 1 of at most 60:
> test-coef_recovery.R: 1
> test-coef_recovery.R: Optimizing with step length 0.3646.
> test-coef_recovery.R: The log-likelihood improved by 2.8321.
> test-coef_recovery.R: Iteration 2 of at most 60:
> test-coef_recovery.R: 1
> test-coef_recovery.R: Optimizing with step length 0.8183.
> test-coef_recovery.R: The log-likelihood improved by 3.0954.
> test-coef_recovery.R: Iteration 3 of at most 60:
> test-coef_recovery.R: 1
> test-coef_recovery.R: Optimizing with step length 1.0000.
> test-coef_recovery.R: The log-likelihood improved by 1.4921.
> test-coef_recovery.R: Step length converged once. Increasing MCMC sample size.
> test-coef_recovery.R: Iteration 4 of at most 60:
> test-coef_recovery.R: 1
> test-coef_recovery.R: Optimizing with step length 1.0000.
> test-coef_recovery.R: The log-likelihood improved by 0.7405.
> test-coef_recovery.R: Step length converged twice. Stopping.
> test-coef_recovery.R: Finished MCMLE.
> test-coef_recovery.R: This model was fit using MCMC. To examine model diagnostics and check
> test-coef_recovery.R: for degeneracy, use the mcmc.diagnostics() function.
> test-drop.R: Constructing pseudopopulation network.
> test-drop.R: Starting simulated annealing (SAN)
> test-drop.R: Iteration 1 of at most 4
> test-drop.R: Iteration 2 of at most 4
> test-drop.R: Iteration 3 of at most 4
> test-drop.R: Finished simulated annealing
> test-drop.R: Observed statistic(s) nodematch.a are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: Unable to match target stats. Using MCMLE estimation.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-drop.R: Iteration 1 of at most 60:
> test-drop.R: 1
> test-drop.R: Optimizing with step length 1.0000.
> test-drop.R: The log-likelihood improved by 0.0044.
> test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-drop.R: Finished MCMLE.
> test-drop.R: This model was fit using MCMC. To examine model diagnostics and check
> test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
> test-drop.R: Observed statistic(s) nodematch.a are at their smallest attainable values. Their coefficients will be fixed at -Inf.
> test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-drop.R: Obtaining the responsible dyads.
> test-drop.R: Evaluating the predictor and response matrix.
> test-drop.R: Maximizing the pseudolikelihood.
> test-drop.R: Finished MPLE.
> test-drop.R: Evaluating log-likelihood at the estimate.
> test-predict.ergm.ego.R: Constructing pseudopopulation network.
> test-predict.ergm.ego.R: Starting simulated annealing (SAN)
> test-predict.ergm.ego.R: Iteration 1 of at most 4
> test-predict.ergm.ego.R: Iteration 2 of at most 4
> test-predict.ergm.ego.R: Iteration 3 of at most 4
> test-predict.ergm.ego.R: Iteration 4 of at most 4
> test-predict.ergm.ego.R: Finished simulated annealing
> test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation.
> test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.ego.R: Obtaining the responsible dyads.
> test-predict.ergm.ego.R: Evaluating the predictor and response matrix.
> test-predict.ergm.ego.R: Maximizing the pseudolikelihood.
> test-predict.ergm.ego.R: Finished MPLE.
> test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-predict.ergm.ego.R: Iteration 1 of at most 2:
> test-predict.ergm.ego.R: 1
> test-predict.ergm.ego.R: Optimizing with step length 0.5473.
> test-predict.ergm.ego.R: The log-likelihood improved by 1.8671.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: Iteration 2 of at most 2:
> test-predict.ergm.ego.R: 1
> test-predict.ergm.ego.R: Optimizing with step length 1.0000.
> test-predict.ergm.ego.R: The log-likelihood improved by 1.0036.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-predict.ergm.ego.R: Finished MCMLE.
> test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check
> test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function.
> test-predict.ergm.ego.R: Constructing pseudopopulation network.
> test-predict.ergm.ego.R: Starting simulated annealing (SAN)
> test-predict.ergm.ego.R: Iteration 1 of at most 4
> test-predict.ergm.ego.R: Iteration 2 of at most 4
> test-predict.ergm.ego.R: Iteration 3 of at most 4
> test-predict.ergm.ego.R: Iteration 4 of at most 4
> test-predict.ergm.ego.R: Finished simulated annealing
> test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation.
> test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.ego.R: Obtaining the responsible dyads.
> test-predict.ergm.ego.R: Evaluating the predictor and response matrix.
> test-predict.ergm.ego.R: Maximizing the pseudolikelihood.
> test-predict.ergm.ego.R: Finished MPLE.
> test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-predict.ergm.ego.R: Iteration 1 of at most 2:
> test-predict.ergm.ego.R: 1
> test-predict.ergm.ego.R: Optimizing with step length 0.7865.
> test-predict.ergm.ego.R: The log-likelihood improved by 1.8825.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: Iteration 2 of at most 2:
> test-predict.ergm.ego.R: 1
> test-predict.ergm.ego.R: Optimizing with step length 1.0000.
> test-predict.ergm.ego.R: The log-likelihood improved by 0.3775.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-predict.ergm.ego.R: Finished MCMLE.
> test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check
> test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function.
> test-table_ppop.R: Constructing pseudopopulation network.
> test-table_ppop.R: Starting simulated annealing (SAN)
> test-table_ppop.R: Iteration 1 of at most 4
> test-table_ppop.R: Iteration 2 of at most 4
> test-table_ppop.R: Iteration 3 of at most 4
> test-table_ppop.R: Iteration 4 of at most 4
> test-table_ppop.R: Finished simulated annealing
> test-table_ppop.R: Unable to match target stats. Using MCMLE estimation.
> test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-table_ppop.R: Obtaining the responsible dyads.
> test-table_ppop.R: Evaluating the predictor and response matrix.
> test-table_ppop.R: Maximizing the pseudolikelihood.
> test-table_ppop.R: Finished MPLE.
> test-table_ppop.R: Constructing pseudopopulation network.
> test-table_ppop.R: Starting simulated annealing (SAN)
> test-table_ppop.R: Iteration 1 of at most 4
> test-table_ppop.R: Iteration 2 of at most 4
> test-table_ppop.R: Iteration 3 of at most 4
> test-table_ppop.R: Iteration 4 of at most 4
> test-table_ppop.R: Finished simulated annealing
> test-table_ppop.R: Unable to match target stats. Using MCMLE estimation.
> test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-table_ppop.R: Obtaining the responsible dyads.
> test-table_ppop.R: Evaluating the predictor and response matrix.
> test-table_ppop.R: Maximizing the pseudolikelihood.
> test-table_ppop.R: Finished MPLE.
Saving _problems/test-table_ppop-39.R
> test-gof.ergm.ego.R: Constructing pseudopopulation network.
> test-gof.ergm.ego.R: Starting simulated annealing (SAN)
> test-gof.ergm.ego.R: Iteration 1 of at most 4
> test-gof.ergm.ego.R: Finished simulated annealing
> test-gof.ergm.ego.R: Unable to match target stats. Using MCMLE estimation.
> test-gof.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.ergm.ego.R: Obtaining the responsible dyads.
> test-gof.ergm.ego.R: Evaluating the predictor and response matrix.
> test-gof.ergm.ego.R: Maximizing the pseudolikelihood.
> test-gof.ergm.ego.R: Finished MPLE.
> test-gof.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gof.ergm.ego.R: Iteration 1 of at most 2:
> test-gof.ergm.ego.R: 1
> test-gof.ergm.ego.R: Optimizing with step length 1.0000.
> test-gof.ergm.ego.R: The log-likelihood improved by 1.6103.
> test-gof.ergm.ego.R: Estimating equations are not within tolerance region.
> test-gof.ergm.ego.R: Iteration 2 of at most 2:
> test-gof.ergm.ego.R: 1
> test-gof.ergm.ego.R: Optimizing with step length 1.0000.
> test-gof.ergm.ego.R: The log-likelihood improved by 0.0094.
> test-gof.ergm.ego.R: Convergence test p-value: 0.3069. Not converged with 99% confidence; increasing sample size.
> test-gof.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-gof.ergm.ego.R: Finished MCMLE.
> test-gof.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gof.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function.
Saving _problems/test-gof.ergm.ego-17.R
Saving _problems/test-gof.ergm.ego-32.R
Saving _problems/test-gof.ergm.ego-48.R
[ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ]
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test-table_ppop.R:39:3'): estimation and simulation work ──────────
Expected `(egosim <- simulate(egofit_scl, popsize = ppop))` to run silently.
Actual noise: messages.
── Failure ('test-gof.ergm.ego.R:15:3'): GOF='model' works ─────────────────────
Expected `z <- gof(fmhfit, GOF = "model")` to run silently.
Actual noise: messages.
── Failure ('test-gof.ergm.ego.R:30:3'): GOF='degree' works ────────────────────
Expected `z <- gof(fmhfit, GOF = "degree")` to run silently.
Actual noise: messages.
── Failure ('test-gof.ergm.ego.R:46:3'): GOF='espartners' works ────────────────
Expected `z <- gof(fmhfit, GOF = "espartners")` to run silently.
Actual noise: messages.
[ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.1.3
Check: tests
Result: ERROR
Running ‘testthat.R’ [207s/344s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # File tests/testthat.R in package ergm.ego, part of the Statnet suite of
> # packages for network analysis, https://statnet.org .
> #
> # This software is distributed under the GPL-3 license. It is free, open
> # source, and has the attribution requirements (GPL Section 7) at
> # https://statnet.org/attribution .
> #
> # Copyright 2015-2025 Statnet Commons
> ################################################################################
> library(testthat)
> library(ergm.ego)
Loading required package: ergm
Loading required package: network
'network' 1.20.0 (2026-02-06), part of the Statnet Project
* 'news(package="network")' for changes since last version
* 'citation("network")' for citation information
* 'https://statnet.org' for help, support, and other information
'ergm' 4.12.0 (2026-02-17), part of the Statnet Project
* 'news(package="ergm")' for changes since last version
* 'citation("ergm")' for citation information
* 'https://statnet.org' for help, support, and other information
'ergm' 4 is a major update that introduces some backwards-incompatible
changes. Please type 'news(package="ergm")' for a list of major
changes.
Loading required package: egor
Loading required package: dplyr
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Loading required package: tibble
'ergm.ego' 1.1.3 (2025-06-10), part of the Statnet Project
* 'news(package="ergm.ego")' for changes since last version
* 'citation("ergm.ego")' for citation information
* 'https://statnet.org' for help, support, and other information
Attaching package: 'ergm.ego'
The following objects are masked from 'package:ergm':
COLLAPSE_SMALLEST, snctrl
The following object is masked from 'package:base':
sample
>
> test_check("ergm.ego")
Starting 2 test processes.
> test-EgoStat.R: Starting simulated annealing (SAN)
> test-EgoStat.R: Iteration 1 of at most 4
> test-EgoStat.R: Iteration 2 of at most 4
> test-EgoStat.R: Iteration 3 of at most 4
> test-EgoStat.R: Finished simulated annealing
> test-attrmismatch.R: Constructing pseudopopulation network.
> test-attrmismatch.R: Starting simulated annealing (SAN)
> test-attrmismatch.R: Iteration 1 of at most 4
> test-attrmismatch.R: 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 0.0014.
> test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-attrmismatch.R: Finished MCMLE.
> test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check
> test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function.
> test-attrmismatch.R: Constructing pseudopopulation network.
> test-attrmismatch.R: Starting simulated annealing (SAN)
> test-attrmismatch.R: Iteration 1 of at most 4
> test-attrmismatch.R: Finished simulated annealing
> test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation.
> test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-attrmismatch.R: Obtaining the responsible dyads.
> test-attrmismatch.R: Evaluating the predictor and response matrix.
> test-attrmismatch.R: Maximizing the pseudolikelihood.
> test-attrmismatch.R: Finished MPLE.
> test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-attrmismatch.R: Iteration 1 of at most 60:
> test-attrmismatch.R: 1 Optimizing with step length 1.0000.
> test-attrmismatch.R: The log-likelihood improved by 0.0001.
> 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-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: Starting simulated annealing (SAN)
> test-EgoStat.R: Iteration 3 of at most 4
> test-boot_jack.R: Iteration 1 of at most 4
> test-boot_jack.R: Finished simulated annealing
> test-EgoStat.R: Finished simulated annealing
> test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
> test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-boot_jack.R: Obtaining the responsible dyads.
> test-boot_jack.R: Evaluating the predictor and response matrix.
> test-boot_jack.R: Maximizing the pseudolikelihood.
> test-boot_jack.R: Finished MPLE.
> test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-boot_jack.R: Iteration 1 of at most 60:
> test-boot_jack.R: 1
> test-boot_jack.R: Optimizing with step length 1.0000.
> test-boot_jack.R: The log-likelihood improved by 0.0488.
> test-boot_jack.R: Convergence test p-value: 0.0024. Converged with 99% confidence.
> test-boot_jack.R: Finished MCMLE.
> test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check
> test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
> test-boot_jack.R: Constructing pseudopopulation network.
> test-boot_jack.R: Starting simulated annealing (SAN)
> test-boot_jack.R: Iteration 1 of at most 4
> test-boot_jack.R: Finished simulated annealing
> test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
> test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-boot_jack.R: Obtaining the responsible dyads.
> test-boot_jack.R: Evaluating the predictor and response matrix.
> test-boot_jack.R: Maximizing the pseudolikelihood.
> test-boot_jack.R: Finished MPLE.
> test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-boot_jack.R: Iteration 1 of at most 60:
> test-boot_jack.R: 1
> test-boot_jack.R: Optimizing with step length 1.0000.
> test-boot_jack.R: The log-likelihood improved by 0.0002.
> test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-boot_jack.R: Finished MCMLE.
> test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check
> test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
> test-boot_jack.R: Constructing pseudopopulation network.
> test-boot_jack.R: Starting simulated annealing (SAN)
> test-boot_jack.R: Iteration 1 of at most 4
> test-boot_jack.R: Iteration 2 of at most 4
> test-boot_jack.R: Iteration 3 of at most 4
> test-boot_jack.R: Iteration 4 of at most 4
> test-boot_jack.R: Finished simulated annealing
> test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
> test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-boot_jack.R: Obtaining the responsible dyads.
> test-boot_jack.R: Evaluating the predictor and response matrix.
> test-boot_jack.R: Maximizing the pseudolikelihood.
> test-boot_jack.R: Finished MPLE.
> test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-boot_jack.R: Iteration 1 of at most 60:
> test-boot_jack.R: 1
> test-boot_jack.R: Optimizing with step length 1.0000.
> test-boot_jack.R: The log-likelihood improved by 0.0011.
> test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-boot_jack.R: Finished MCMLE.
> test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check
> test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
> test-boot_jack.R: Constructing pseudopopulation network.
> test-boot_jack.R: Starting simulated annealing (SAN)
> test-boot_jack.R: Iteration 1 of at most 4
> test-boot_jack.R: Finished simulated annealing
> test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
> test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-boot_jack.R: Obtaining the responsible dyads.
> test-boot_jack.R: Evaluating the predictor and response matrix.
> test-boot_jack.R: Maximizing the pseudolikelihood.
> test-boot_jack.R: Finished MPLE.
> test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-boot_jack.R: Iteration 1 of at most 60:
> test-boot_jack.R: 1
> test-boot_jack.R: Optimizing with step length 1.0000.
> test-boot_jack.R: The log-likelihood improved by 0.0001.
> test-boot_jack.R: Convergence test p-value: < 0.0001.
> test-boot_jack.R: Converged with 99% confidence.
> test-boot_jack.R: Finished MCMLE.
> test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check
> test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
> test-coef_recovery.R: Constructing pseudopopulation network.
> test-coef_recovery.R: Starting simulated annealing (SAN)
> test-coef_recovery.R: Iteration 1 of at most 4
> test-coef_recovery.R: Iteration 2 of at most 4
> test-coef_recovery.R: Iteration 3 of at most 4
> test-coef_recovery.R: Iteration 4 of at most 4
> test-coef_recovery.R: Finished simulated annealing
> test-coef_recovery.R: Unable to match target stats. Using MCMLE estimation.
> test-coef_recovery.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-coef_recovery.R: Obtaining the responsible dyads.
> test-coef_recovery.R: Evaluating the predictor and response matrix.
> test-coef_recovery.R: Maximizing the pseudolikelihood.
> test-coef_recovery.R: Finished MPLE.
> test-coef_recovery.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-coef_recovery.R: Iteration 1 of at most 60:
> test-coef_recovery.R: 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 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-predict.ergm.ego.R: Constructing pseudopopulation network.
> test-predict.ergm.ego.R: Starting simulated annealing (SAN)
> test-predict.ergm.ego.R: Iteration 1 of at most 4
> test-predict.ergm.ego.R: Iteration 2 of at most 4
> test-predict.ergm.ego.R: Iteration 3 of at most 4
> test-predict.ergm.ego.R: Iteration 4 of at most 4
> test-predict.ergm.ego.R: Finished simulated annealing
> test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation.
> test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.ego.R: Obtaining the responsible dyads.
> test-predict.ergm.ego.R: Evaluating the predictor and response matrix.
> test-predict.ergm.ego.R: Maximizing the pseudolikelihood.
> test-predict.ergm.ego.R: Finished MPLE.
> test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-predict.ergm.ego.R: Iteration 1 of at most 2:
> test-predict.ergm.ego.R: 1
> test-predict.ergm.ego.R: Optimizing with step length 0.5473.
> test-predict.ergm.ego.R: The log-likelihood improved by 1.8671.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: Iteration 2 of at most 2:
> test-predict.ergm.ego.R: 1
> test-predict.ergm.ego.R: Optimizing with step length 1.0000.
> test-predict.ergm.ego.R: The log-likelihood improved by 1.0036.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-predict.ergm.ego.R: Finished MCMLE.
> test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check
> test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function.
> test-predict.ergm.ego.R: Constructing pseudopopulation network.
> test-predict.ergm.ego.R: Starting simulated annealing (SAN)
> test-predict.ergm.ego.R: Iteration 1 of at most 4
> test-predict.ergm.ego.R: Iteration 2 of at most 4
> test-predict.ergm.ego.R: Iteration 3 of at most 4
> test-predict.ergm.ego.R: Iteration 4 of at most 4
> test-predict.ergm.ego.R: Finished simulated annealing
> test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation.
> test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.ego.R: Obtaining the responsible dyads.
> test-predict.ergm.ego.R: Evaluating the predictor and response matrix.
> test-predict.ergm.ego.R: Maximizing the pseudolikelihood.
> test-predict.ergm.ego.R: Finished MPLE.
> test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-predict.ergm.ego.R: Iteration 1 of at most 2:
> test-predict.ergm.ego.R: 1
> test-predict.ergm.ego.R: Optimizing with step length 0.7865.
> test-predict.ergm.ego.R: The log-likelihood improved by 1.8825.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: Iteration 2 of at most 2:
> test-predict.ergm.ego.R: 1
> test-predict.ergm.ego.R: Optimizing with step length 1.0000.
> test-predict.ergm.ego.R: The log-likelihood improved by 0.3775.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-predict.ergm.ego.R: Finished MCMLE.
> test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check
> test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function.
> test-table_ppop.R: Constructing pseudopopulation network.
> test-table_ppop.R: Starting simulated annealing (SAN)
> test-table_ppop.R: Iteration 1 of at most 4
> test-table_ppop.R: Iteration 2 of at most 4
> test-table_ppop.R: Iteration 3 of at most 4
> test-table_ppop.R: Iteration 4 of at most 4
> test-table_ppop.R: Finished simulated annealing
> test-table_ppop.R: Unable to match target stats. Using MCMLE estimation.
> test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-table_ppop.R: Obtaining the responsible dyads.
> test-table_ppop.R: Evaluating the predictor and response matrix.
> test-table_ppop.R: Maximizing the pseudolikelihood.
> test-table_ppop.R: Finished MPLE.
> test-table_ppop.R: Constructing pseudopopulation network.
> test-table_ppop.R: Starting simulated annealing (SAN)
> test-table_ppop.R: Iteration 1 of at most 4
> test-table_ppop.R: Iteration 2 of at most 4
> test-table_ppop.R: Iteration 3 of at most 4
> test-table_ppop.R: Iteration 4 of at most 4
> test-table_ppop.R: Finished simulated annealing
> test-table_ppop.R: Unable to match target stats. Using MCMLE estimation.
> test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-table_ppop.R: Obtaining the responsible dyads.
> test-table_ppop.R: Evaluating the predictor and response matrix.
> test-table_ppop.R: Maximizing the pseudolikelihood.
> test-table_ppop.R: Finished MPLE.
Saving _problems/test-table_ppop-39.R
> test-gof.ergm.ego.R: Constructing pseudopopulation network.
> test-gof.ergm.ego.R: Starting simulated annealing (SAN)
> test-gof.ergm.ego.R: Iteration 1 of at most 4
> test-gof.ergm.ego.R: Finished simulated annealing
> test-gof.ergm.ego.R: Unable to match target stats. Using MCMLE estimation.
> test-gof.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.ergm.ego.R: Obtaining the responsible dyads.
> test-gof.ergm.ego.R: Evaluating the predictor and response matrix.
> test-gof.ergm.ego.R: Maximizing the pseudolikelihood.
> test-gof.ergm.ego.R: Finished MPLE.
> test-gof.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gof.ergm.ego.R: Iteration 1 of at most 2:
> test-gof.ergm.ego.R: 1
> test-gof.ergm.ego.R: Optimizing with step length 1.0000.
> test-gof.ergm.ego.R: The log-likelihood improved by 1.6103.
> test-gof.ergm.ego.R: Estimating equations are not within tolerance region.
> test-gof.ergm.ego.R: Iteration 2 of at most 2:
> test-gof.ergm.ego.R: 1
> test-gof.ergm.ego.R: Optimizing with step length 1.0000.
> test-gof.ergm.ego.R: The log-likelihood improved by 0.0094.
> test-gof.ergm.ego.R: Convergence test p-value: 0.3069. Not converged with 99% confidence; increasing sample size.
> test-gof.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-gof.ergm.ego.R: Finished MCMLE.
> test-gof.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gof.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function.
Saving _problems/test-gof.ergm.ego-17.R
Saving _problems/test-gof.ergm.ego-32.R
Saving _problems/test-gof.ergm.ego-48.R
[ FAIL 4 | WARN 0 | SKIP 0 | PASS 104 ]
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test-table_ppop.R:39:3'): estimation and simulation work ──────────
Expected `(egosim <- simulate(egofit_scl, popsize = ppop))` to run silently.
Actual noise: messages.
── Failure ('test-gof.ergm.ego.R:15:3'): GOF='model' works ─────────────────────
Expected `z <- gof(fmhfit, GOF = "model")` to run silently.
Actual noise: messages.
── Failure ('test-gof.ergm.ego.R:30:3'): GOF='degree' works ────────────────────
Expected `z <- gof(fmhfit, GOF = "degree")` to run silently.
Actual noise: messages.
── Failure ('test-gof.ergm.ego.R:46:3'): GOF='espartners' works ────────────────
Expected `z <- gof(fmhfit, GOF = "espartners")` to run silently.
Actual noise: messages.
[ FAIL 4 | WARN 0 | SKIP 0 | PASS 104 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc