Last updated on 2026-02-28 19:50:54 CET.
| Package | ERROR | WARN | NOTE | OK |
|---|---|---|---|---|
| ergm | 1 | 3 | 10 | |
| ergm.count | 14 | |||
| ergm.ego | 9 | 5 | ||
| ergm.multi | 14 | |||
| ergm.rank | 14 | |||
| latentnet | 3 | 11 | ||
| piecemeal | 14 | |||
| rle | 14 | |||
| statnet.common | 14 | |||
| tergm | 14 |
Current CRAN status: WARN: 1, NOTE: 3, OK: 10
Version: 4.12.0
Flags: --no-vignettes
Check: whether package can be installed
Result: WARN
Found the following significant warnings:
Rd warning: no hsearch.rds meta data for package ergm
See ‘/home/hornik/tmp/R.check/r-patched-gcc/Work/PKGS/ergm.Rcheck/00install.out’ for details.
* used C compiler: ‘gcc-14 (Debian 14.3.0-12) 14.3.0’
* used C++ compiler: ‘g++-14 (Debian 14.3.0-12) 14.3.0’
Flavor: r-patched-linux-x86_64
Version: 4.12.0
Check: installed package size
Result: NOTE
installed size is 8.9Mb
sub-directories of 1Mb or more:
R 2.0Mb
doc 1.3Mb
help 1.1Mb
libs 3.5Mb
Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64
Version: 4.12.0
Flags: --no-examples --no-tests --no-vignettes
Check: installed package size
Result: NOTE
installed size is 5.0Mb
sub-directories of 1Mb or more:
R 1.1Mb
doc 1.3Mb
help 1.1Mb
Flavor: r-oldrel-windows-x86_64
Current CRAN status: OK: 14
Current CRAN status: ERROR: 9, OK: 5
Version: 1.1.3
Check: tests
Result: ERROR
Running ‘testthat.R’ [126s/82s]
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 0.0195.
> test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-attrmismatch.R: Finished MCMLE.
> test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check
> test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function.
> test-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 0.0181.
> 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. 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. 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 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 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 Optimizing with step length 0.7865.
> test-predict.ergm.ego.R: The log-likelihood improved by 1.8825.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: Iteration 2 of at most 2:
> test-predict.ergm.ego.R: 1
> test-predict.ergm.ego.R: Optimizing with step length 1.0000.
> test-predict.ergm.ego.R: The log-likelihood improved by 0.3775.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-predict.ergm.ego.R: Finished MCMLE.
> test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check
> test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function.
> test-table_ppop.R: Constructing pseudopopulation network.
> test-table_ppop.R: Starting simulated annealing (SAN)
> test-table_ppop.R: Iteration 1 of at most 4
> test-table_ppop.R: Iteration 2 of at most 4
> test-table_ppop.R: Iteration 3 of at most 4
> test-table_ppop.R: Iteration 4 of at most 4
> test-table_ppop.R: Finished simulated annealing
> test-table_ppop.R: Unable to match target stats. Using MCMLE estimation.
> test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-table_ppop.R: Obtaining the responsible dyads.
> test-table_ppop.R: Evaluating the predictor and response matrix.
> test-table_ppop.R: Maximizing the pseudolikelihood.
> test-table_ppop.R: Finished MPLE.
> test-table_ppop.R: Constructing pseudopopulation network.
> test-table_ppop.R: Starting simulated annealing (SAN)
> test-table_ppop.R: Iteration 1 of at most 4
> test-table_ppop.R: Iteration 2 of at most 4
> test-table_ppop.R: Iteration 3 of at most 4
> test-table_ppop.R: Iteration 4 of at most 4
> test-table_ppop.R: Finished simulated annealing
> test-table_ppop.R: Unable to match target stats. Using MCMLE estimation.
> test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-table_ppop.R: Obtaining the responsible dyads.
> test-table_ppop.R: Evaluating the predictor and response matrix.
> test-table_ppop.R: Maximizing the pseudolikelihood.
> test-table_ppop.R: Finished MPLE.
Saving _problems/test-table_ppop-39.R
> test-gof.ergm.ego.R: Constructing pseudopopulation network.
> test-gof.ergm.ego.R: Starting simulated annealing (SAN)
> test-gof.ergm.ego.R: Iteration 1 of at most 4
> test-gof.ergm.ego.R: Finished simulated annealing
> test-gof.ergm.ego.R: Unable to match target stats. Using MCMLE estimation.
> test-gof.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.ergm.ego.R: Obtaining the responsible dyads.
> test-gof.ergm.ego.R: Evaluating the predictor and response matrix.
> test-gof.ergm.ego.R: Maximizing the pseudolikelihood.
> test-gof.ergm.ego.R: Finished MPLE.
> test-gof.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gof.ergm.ego.R: Iteration 1 of at most 2:
> test-gof.ergm.ego.R: 1 Optimizing with step length 1.0000.
> test-gof.ergm.ego.R: The log-likelihood improved by 1.6103.
> test-gof.ergm.ego.R: Estimating equations are not within tolerance region.
> test-gof.ergm.ego.R: Iteration 2 of at most 2:
> test-gof.ergm.ego.R: 1 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-debian-clang
Version: 1.1.3
Check: tests
Result: ERROR
Running ‘testthat.R’ [83s/55s]
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-attrmismatch.R: Constructing pseudopopulation network.
> test-EgoStat.R: Iteration 3 of at most 4
> test-EgoStat.R: Finished simulated annealing
> test-attrmismatch.R: Starting simulated annealing (SAN)
> test-attrmismatch.R: Iteration 1 of at most 4
> test-attrmismatch.R: 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 0.0030.
> 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 0.0031.
> test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-attrmismatch.R: Finished MCMLE.
> test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check
> test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function.
> test-boot_jack.R: Constructing pseudopopulation network.
> test-boot_jack.R: Starting simulated annealing (SAN)
> test-boot_jack.R: Iteration 1 of at most 4
> test-boot_jack.R: Finished simulated annealing
> test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
> test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-boot_jack.R: Obtaining the responsible dyads.
> test-boot_jack.R: Evaluating the predictor and response matrix.
> test-boot_jack.R: Maximizing the pseudolikelihood.
> test-boot_jack.R: Finished MPLE.
> test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-boot_jack.R: Iteration 1 of at most 60:
> test-boot_jack.R: 1 Optimizing with step length 1.0000.
> test-boot_jack.R: The log-likelihood improved by 0.0223.
> test-boot_jack.R: Convergence test p-value: 0.0016. Converged with 99% confidence.
> test-boot_jack.R: Finished MCMLE.
> test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check
> test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
> test-boot_jack.R: Constructing pseudopopulation network.
> test-boot_jack.R: Starting simulated annealing (SAN)
> test-boot_jack.R: Iteration 1 of at most 4
> test-boot_jack.R: Iteration 2 of at most 4
> test-boot_jack.R: Iteration 3 of at most 4
> test-boot_jack.R: Iteration 4 of at most 4
> test-boot_jack.R: Finished simulated annealing
> test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
> test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-boot_jack.R: Obtaining the responsible dyads.
> test-boot_jack.R: Evaluating the predictor and response matrix.
> test-boot_jack.R: Maximizing the pseudolikelihood.
> test-boot_jack.R: Finished MPLE.
> test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-boot_jack.R: Iteration 1 of at most 60:
> test-boot_jack.R: 1
> test-boot_jack.R: Optimizing with step length 1.0000.
> test-boot_jack.R: The log-likelihood improved by 0.0009.
> test-boot_jack.R: Convergence test p-value: 0.0006. Converged with 99% confidence.
> test-boot_jack.R: Finished MCMLE.
> test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check
> test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
> test-boot_jack.R: Constructing pseudopopulation network.
> test-boot_jack.R: Starting simulated annealing (SAN)
> test-boot_jack.R: Iteration 1 of at most 4
> test-boot_jack.R: Finished simulated annealing
> test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
> test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-boot_jack.R: Obtaining the responsible dyads.
> test-boot_jack.R: Evaluating the predictor and response matrix.
> test-boot_jack.R: Maximizing the pseudolikelihood.
> test-boot_jack.R: Finished MPLE.
> test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-boot_jack.R: Iteration 1 of at most 60:
> test-boot_jack.R: 1 Optimizing with step length 1.0000.
> test-boot_jack.R: The log-likelihood improved by 0.0488.
> test-boot_jack.R: Convergence test p-value: 0.0024. Converged with 99% confidence.
> test-boot_jack.R: Finished MCMLE.
> test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check
> test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
> test-boot_jack.R: Constructing pseudopopulation network.
> test-boot_jack.R: Starting simulated annealing (SAN)
> test-boot_jack.R: Iteration 1 of at most 4
> test-boot_jack.R: Finished simulated annealing
> test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
> test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-boot_jack.R: Obtaining the responsible dyads.
> test-boot_jack.R: Evaluating the predictor and response matrix.
> test-boot_jack.R: Maximizing the pseudolikelihood.
> test-boot_jack.R: Finished MPLE.
> test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-boot_jack.R: Iteration 1 of at most 60:
> test-boot_jack.R: 1 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-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-EgoStat.R: Iteration 2 of at most 4
> test-boot_jack.R: Iteration 4 of at most 4
> test-boot_jack.R: Finished simulated annealing
> test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
> test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-boot_jack.R: Obtaining the responsible dyads.
> test-boot_jack.R: Evaluating the predictor and response matrix.
> test-boot_jack.R: Maximizing the pseudolikelihood.
> test-boot_jack.R: Finished MPLE.
> test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-boot_jack.R: Iteration 1 of at most 60:
> test-EgoStat.R: Iteration 3 of at most 4
> test-EgoStat.R: Finished simulated annealing
> 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 Optimizing with step length 1.0000.
> test-predict.ergm.ego.R: The log-likelihood improved by 0.3775.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-predict.ergm.ego.R: Finished MCMLE.
> test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check
> test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function.
> test-table_ppop.R: Constructing pseudopopulation network.
> test-table_ppop.R: Starting simulated annealing (SAN)
> test-table_ppop.R: Iteration 1 of at most 4
> test-table_ppop.R: Iteration 2 of at most 4
> test-table_ppop.R: Iteration 3 of at most 4
> test-table_ppop.R: Iteration 4 of at most 4
> test-table_ppop.R: Finished simulated annealing
> test-table_ppop.R: Unable to match target stats. Using MCMLE estimation.
> test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-table_ppop.R: Obtaining the responsible dyads.
> test-table_ppop.R: Evaluating the predictor and response matrix.
> test-table_ppop.R: Maximizing the pseudolikelihood.
> test-table_ppop.R: Finished MPLE.
> test-table_ppop.R: Constructing pseudopopulation network.
> test-table_ppop.R: Starting simulated annealing (SAN)
> test-table_ppop.R: Iteration 1 of at most 4
> test-table_ppop.R: Iteration 2 of at most 4
> test-table_ppop.R: Iteration 3 of at most 4
> test-table_ppop.R: Iteration 4 of at most 4
> test-table_ppop.R: Finished simulated annealing
> test-table_ppop.R: Unable to match target stats. Using MCMLE estimation.
> test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-table_ppop.R: Obtaining the responsible dyads.
> test-table_ppop.R: Evaluating the predictor and response matrix.
> test-table_ppop.R: Maximizing the pseudolikelihood.
> test-table_ppop.R: Finished MPLE.
Saving _problems/test-table_ppop-39.R
> test-gof.ergm.ego.R: Constructing pseudopopulation network.
> test-gof.ergm.ego.R: Starting simulated annealing (SAN)
> test-gof.ergm.ego.R: Iteration 1 of at most 4
> test-gof.ergm.ego.R: Finished simulated annealing
> test-gof.ergm.ego.R: Unable to match target stats. Using MCMLE estimation.
> test-gof.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.ergm.ego.R: Obtaining the responsible dyads.
> test-gof.ergm.ego.R: Evaluating the predictor and response matrix.
> test-gof.ergm.ego.R: Maximizing the pseudolikelihood.
> test-gof.ergm.ego.R: Finished MPLE.
> test-gof.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gof.ergm.ego.R: Iteration 1 of at most 2:
> test-gof.ergm.ego.R: 1 Optimizing with step length 1.0000.
> test-gof.ergm.ego.R: The log-likelihood improved by 1.6103.
> test-gof.ergm.ego.R: Estimating equations are not within tolerance region.
> test-gof.ergm.ego.R: Iteration 2 of at most 2:
> test-gof.ergm.ego.R: 1 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-debian-gcc
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’ [200s/283s]
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
> test-attrmismatch.R: Optimizing with step length 1.0000.
> test-attrmismatch.R: The log-likelihood improved by 0.0203.
> 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 0.0263.
> 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. 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-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: 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. 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: Sampling =============================>- 98% | ETA: 0s
> 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-drop.R:
> test-predict.ergm.ego.R: Constructing pseudopopulation network.
> test-predict.ergm.ego.R: Starting simulated annealing (SAN)
> test-predict.ergm.ego.R: Iteration 1 of at most 4
> test-predict.ergm.ego.R: Iteration 2 of at most 4
> test-predict.ergm.ego.R: Iteration 3 of at most 4
> test-predict.ergm.ego.R: Iteration 4 of at most 4
> test-predict.ergm.ego.R: Finished simulated annealing
> test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation.
> test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.ego.R: Obtaining the responsible dyads.
> test-predict.ergm.ego.R: Evaluating the predictor and response matrix.
> test-predict.ergm.ego.R: Maximizing the pseudolikelihood.
> test-predict.ergm.ego.R: Finished MPLE.
> test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-predict.ergm.ego.R: Iteration 1 of at most 2:
> test-predict.ergm.ego.R: 1
> test-predict.ergm.ego.R: Optimizing with step length 0.5473.
> test-predict.ergm.ego.R: The log-likelihood improved by 1.8671.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: Iteration 2 of at most 2:
> test-predict.ergm.ego.R: 1
> test-predict.ergm.ego.R: Optimizing with step length 1.0000.
> test-predict.ergm.ego.R: The log-likelihood improved by 1.0036.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-predict.ergm.ego.R: Finished MCMLE.
> test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check
> test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function.
> test-predict.ergm.ego.R: Constructing pseudopopulation network.
> test-predict.ergm.ego.R: Starting simulated annealing (SAN)
> test-predict.ergm.ego.R: Iteration 1 of at most 4
> test-predict.ergm.ego.R: Iteration 2 of at most 4
> test-predict.ergm.ego.R: Iteration 3 of at most 4
> test-predict.ergm.ego.R: Iteration 4 of at most 4
> test-predict.ergm.ego.R: Finished simulated annealing
> test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation.
> test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.ego.R: Obtaining the responsible dyads.
> test-predict.ergm.ego.R: Evaluating the predictor and response matrix.
> test-predict.ergm.ego.R: Maximizing the pseudolikelihood.
> test-predict.ergm.ego.R: Finished MPLE.
> test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-predict.ergm.ego.R: Iteration 1 of at most 2:
> test-predict.ergm.ego.R: 1
> test-predict.ergm.ego.R: Optimizing with step length 0.7865.
> test-predict.ergm.ego.R: The log-likelihood improved by 1.8825.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: Iteration 2 of at most 2:
> test-predict.ergm.ego.R: 1
> test-predict.ergm.ego.R: Optimizing with step length 1.0000.
> test-predict.ergm.ego.R: The log-likelihood improved by 0.3775.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-predict.ergm.ego.R: Finished MCMLE.
> test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check
> test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function.
> test-table_ppop.R: Constructing pseudopopulation network.
> test-table_ppop.R: Starting simulated annealing (SAN)
> test-table_ppop.R: Iteration 1 of at most 4
> test-table_ppop.R: Iteration 2 of at most 4
> test-table_ppop.R: Iteration 3 of at most 4
> test-table_ppop.R: Iteration 4 of at most 4
> test-table_ppop.R: Finished simulated annealing
> test-table_ppop.R: Unable to match target stats. Using MCMLE estimation.
> test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-table_ppop.R: Obtaining the responsible dyads.
> test-table_ppop.R: Evaluating the predictor and response matrix.
> test-table_ppop.R: Maximizing the pseudolikelihood.
> test-table_ppop.R: Finished MPLE.
> test-table_ppop.R: Constructing pseudopopulation network.
> test-table_ppop.R: Starting simulated annealing (SAN)
> test-table_ppop.R: Iteration 1 of at most 4
> test-table_ppop.R: Iteration 2 of at most 4
> test-table_ppop.R: Iteration 3 of at most 4
> test-table_ppop.R: Iteration 4 of at most 4
> test-table_ppop.R: Finished simulated annealing
> test-table_ppop.R: Unable to match target stats. Using MCMLE estimation.
> test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-table_ppop.R: Obtaining the responsible dyads.
> test-table_ppop.R: Evaluating the predictor and response matrix.
> test-table_ppop.R: Maximizing the pseudolikelihood.
> test-table_ppop.R: Finished MPLE.
Saving _problems/test-table_ppop-39.R
> test-gof.ergm.ego.R: Constructing pseudopopulation network.
> test-gof.ergm.ego.R: Starting simulated annealing (SAN)
> test-gof.ergm.ego.R: Iteration 1 of at most 4
> test-gof.ergm.ego.R: Finished simulated annealing
> test-gof.ergm.ego.R: Unable to match target stats. Using MCMLE estimation.
> test-gof.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.ergm.ego.R: Obtaining the responsible dyads.
> test-gof.ergm.ego.R: Evaluating the predictor and response matrix.
> test-gof.ergm.ego.R: Maximizing the pseudolikelihood.
> test-gof.ergm.ego.R: Finished MPLE.
> test-gof.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gof.ergm.ego.R: Iteration 1 of at most 2:
> test-gof.ergm.ego.R: 1
> test-gof.ergm.ego.R: Optimizing with step length 1.0000.
> test-gof.ergm.ego.R: The log-likelihood improved by 1.6103.
> test-gof.ergm.ego.R: Estimating equations are not within tolerance region.
> test-gof.ergm.ego.R: Iteration 2 of at most 2:
> test-gof.ergm.ego.R: 1
> test-gof.ergm.ego.R: Optimizing with step length 1.0000.
> test-gof.ergm.ego.R: The log-likelihood improved by 0.0094.
> test-gof.ergm.ego.R: Convergence test p-value: 0.3069. Not converged with 99% confidence; increasing sample size.
> test-gof.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-gof.ergm.ego.R: Finished MCMLE.
> test-gof.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gof.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function.
Saving _problems/test-gof.ergm.ego-17.R
Saving _problems/test-gof.ergm.ego-32.R
Saving _problems/test-gof.ergm.ego-48.R
[ FAIL 4 | WARN 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
Version: 1.1.3
Check: tests
Result: ERROR
Running 'testthat.R' [76s]
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 0.0150.
> 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 0.0130.
> 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 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-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 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-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: 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-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 0.6160.
> test-gof.ergm.ego.R: The log-likelihood improved by 2.8670.
> 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 0.7726.
> 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.
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-windows-x86_64
Version: 1.1.3
Check: tests
Result: ERROR
Running ‘testthat.R’ [122s/81s]
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-attrmismatch.R: Constructing pseudopopulation network.
> test-EgoStat.R: Iteration 3 of at most 4
> test-attrmismatch.R: Starting simulated annealing (SAN)
> test-EgoStat.R: Finished simulated annealing
> 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 0.0232.
> 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 0.0210.
> test-attrmismatch.R: Convergence test p-value: 0.0002. Converged with 99% confidence.
> test-attrmismatch.R: Finished MCMLE.
> test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check
> test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function.
> test-boot_jack.R: Constructing pseudopopulation network.
> test-boot_jack.R: Starting simulated annealing (SAN)
> test-boot_jack.R: Iteration 1 of at most 4
> test-boot_jack.R: Finished simulated annealing
> test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
> test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-boot_jack.R: Obtaining the responsible dyads.
> test-boot_jack.R: Evaluating the predictor and response matrix.
> test-boot_jack.R: Maximizing the pseudolikelihood.
> test-boot_jack.R: Finished MPLE.
> test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-boot_jack.R: Iteration 1 of at most 60:
> test-boot_jack.R: 1 Optimizing with step length 1.0000.
> test-boot_jack.R: The log-likelihood improved by 0.0223.
> test-boot_jack.R: Convergence test p-value: 0.0016. Converged with 99% confidence.
> test-boot_jack.R: Finished MCMLE.
> test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check
> test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
> test-boot_jack.R: Constructing pseudopopulation network.
> test-boot_jack.R: Starting simulated annealing (SAN)
> test-boot_jack.R: Iteration 1 of at most 4
> test-boot_jack.R: Iteration 2 of at most 4
> test-boot_jack.R: Iteration 3 of at most 4
> test-boot_jack.R: Iteration 4 of at most 4
> test-boot_jack.R: Finished simulated annealing
> test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
> test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-boot_jack.R: Obtaining the responsible dyads.
> test-boot_jack.R: Evaluating the predictor and response matrix.
> test-boot_jack.R: Maximizing the pseudolikelihood.
> test-boot_jack.R: Finished MPLE.
> test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-boot_jack.R: Iteration 1 of at most 60:
> test-boot_jack.R: 1 Optimizing with step length 1.0000.
> test-boot_jack.R: The log-likelihood improved by 0.0009.
> test-boot_jack.R: Convergence test p-value: 0.0006. Converged with 99% confidence.
> test-boot_jack.R: Finished MCMLE.
> test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check
> test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
> test-boot_jack.R: Constructing pseudopopulation network.
> test-boot_jack.R: Starting simulated annealing (SAN)
> test-boot_jack.R: Iteration 1 of at most 4
> test-boot_jack.R: Finished simulated annealing
> test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
> test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-boot_jack.R: Obtaining the responsible dyads.
> test-boot_jack.R: Evaluating the predictor and response matrix.
> test-boot_jack.R: Maximizing the pseudolikelihood.
> test-boot_jack.R: Finished MPLE.
> test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-boot_jack.R: Iteration 1 of at most 60:
> test-boot_jack.R: 1
> 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 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 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-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 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 Optimizing with step length 0.7865.
> test-predict.ergm.ego.R: The log-likelihood improved by 1.8825.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: Iteration 2 of at most 2:
> test-predict.ergm.ego.R: 1
> test-predict.ergm.ego.R: Optimizing with step length 1.0000.
> test-predict.ergm.ego.R: The log-likelihood improved by 0.3775.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-predict.ergm.ego.R: Finished MCMLE.
> test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check
> test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function.
> test-table_ppop.R: Constructing pseudopopulation network.
> test-table_ppop.R: Starting simulated annealing (SAN)
> test-table_ppop.R: Iteration 1 of at most 4
> test-table_ppop.R: Iteration 2 of at most 4
> test-table_ppop.R: Iteration 3 of at most 4
> test-table_ppop.R: Iteration 4 of at most 4
> test-table_ppop.R: Finished simulated annealing
> test-table_ppop.R: Unable to match target stats. Using MCMLE estimation.
> test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-table_ppop.R: Obtaining the responsible dyads.
> test-table_ppop.R: Evaluating the predictor and response matrix.
> test-table_ppop.R: Maximizing the pseudolikelihood.
> test-table_ppop.R: Finished MPLE.
> test-table_ppop.R: Constructing pseudopopulation network.
> test-table_ppop.R: Starting simulated annealing (SAN)
> test-table_ppop.R: Iteration 1 of at most 4
> test-table_ppop.R: Iteration 2 of at most 4
> test-table_ppop.R: Iteration 3 of at most 4
> test-table_ppop.R: Iteration 4 of at most 4
> test-table_ppop.R: Finished simulated annealing
> test-table_ppop.R: Unable to match target stats. Using MCMLE estimation.
> test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-table_ppop.R: Obtaining the responsible dyads.
> test-table_ppop.R: Evaluating the predictor and response matrix.
> test-table_ppop.R: Maximizing the pseudolikelihood.
> test-table_ppop.R: Finished MPLE.
Saving _problems/test-table_ppop-39.R
> test-gof.ergm.ego.R: Constructing pseudopopulation network.
> test-gof.ergm.ego.R: Starting simulated annealing (SAN)
> test-gof.ergm.ego.R: Iteration 1 of at most 4
> test-gof.ergm.ego.R: Finished simulated annealing
> test-gof.ergm.ego.R: Unable to match target stats. Using MCMLE estimation.
> test-gof.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.ergm.ego.R: Obtaining the responsible dyads.
> test-gof.ergm.ego.R: Evaluating the predictor and response matrix.
> test-gof.ergm.ego.R: Maximizing the pseudolikelihood.
> test-gof.ergm.ego.R: Finished MPLE.
> test-gof.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gof.ergm.ego.R: Iteration 1 of at most 2:
> test-gof.ergm.ego.R: 1 Optimizing with step length 1.0000.
> test-gof.ergm.ego.R: The log-likelihood improved by 1.6103.
> test-gof.ergm.ego.R: Estimating equations are not within tolerance region.
> test-gof.ergm.ego.R: Iteration 2 of at most 2:
> test-gof.ergm.ego.R: 1
> test-gof.ergm.ego.R: Optimizing with step length 1.0000.
> test-gof.ergm.ego.R: The log-likelihood improved by 0.0094.
> test-gof.ergm.ego.R: Convergence test p-value: 0.3069. Not converged with 99% confidence; increasing sample size.
> test-gof.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-gof.ergm.ego.R: Finished MCMLE.
> test-gof.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gof.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function.
Saving _problems/test-gof.ergm.ego-17.R
Saving _problems/test-gof.ergm.ego-32.R
Saving _problems/test-gof.ergm.ego-48.R
[ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ]
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test-table_ppop.R:39:3'): estimation and simulation work ──────────
Expected `(egosim <- simulate(egofit_scl, popsize = ppop))` to run silently.
Actual noise: messages.
── Failure ('test-gof.ergm.ego.R:15:3'): GOF='model' works ─────────────────────
Expected `z <- gof(fmhfit, GOF = "model")` to run silently.
Actual noise: messages.
── Failure ('test-gof.ergm.ego.R:30:3'): GOF='degree' works ────────────────────
Expected `z <- gof(fmhfit, GOF = "degree")` to run silently.
Actual noise: messages.
── Failure ('test-gof.ergm.ego.R:46:3'): GOF='espartners' works ────────────────
Expected `z <- gof(fmhfit, GOF = "espartners")` to run silently.
Actual noise: messages.
[ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ]
Error:
! Test failures.
Execution halted
Flavor: r-patched-linux-x86_64
Version: 1.1.3
Check: tests
Result: ERROR
Running ‘testthat.R’ [122s/80s]
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-EgoStat.R: Finished simulated annealing
> 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 0.0118.
> test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-attrmismatch.R: Finished MCMLE.
> test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check
> test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function.
> test-attrmismatch.R: Constructing pseudopopulation network.
> test-attrmismatch.R: Starting simulated annealing (SAN)
> test-attrmismatch.R: Iteration 1 of at most 4
> test-attrmismatch.R: Iteration 2 of at most 4
> test-attrmismatch.R: Iteration 3 of at most 4
> test-attrmismatch.R: Iteration 4 of at most 4
> test-attrmismatch.R: Finished simulated annealing
> test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation.
> test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-attrmismatch.R: Obtaining the responsible dyads.
> test-attrmismatch.R: Evaluating the predictor and response matrix.
> test-attrmismatch.R: Maximizing the pseudolikelihood.
> test-attrmismatch.R: Finished MPLE.
> test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-attrmismatch.R: Iteration 1 of at most 60:
> test-attrmismatch.R: 1 Optimizing with step length 1.0000.
> test-attrmismatch.R: The log-likelihood improved by 0.0219.
> test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
> test-attrmismatch.R: Finished MCMLE.
> test-attrmismatch.R: This model was fit using MCMC. To examine model diagnostics and check
> test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function.
> test-boot_jack.R: Constructing pseudopopulation network.
> test-boot_jack.R: Starting simulated annealing (SAN)
> test-boot_jack.R: Iteration 1 of at most 4
> test-boot_jack.R: Finished simulated annealing
> test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
> test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-boot_jack.R: Obtaining the responsible dyads.
> test-boot_jack.R: Evaluating the predictor and response matrix.
> test-boot_jack.R: Maximizing the pseudolikelihood.
> test-boot_jack.R: Finished MPLE.
> test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-boot_jack.R: Iteration 1 of at most 60:
> test-boot_jack.R: 1 Optimizing with step length 1.0000.
> test-boot_jack.R: The log-likelihood improved by 0.0223.
> test-boot_jack.R: Convergence test p-value: 0.0016. Converged with 99% confidence.
> test-boot_jack.R: Finished MCMLE.
> test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check
> test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
> test-boot_jack.R: Constructing pseudopopulation network.
> test-boot_jack.R: Starting simulated annealing (SAN)
> test-boot_jack.R: Iteration 1 of at most 4
> test-boot_jack.R: Iteration 2 of at most 4
> test-boot_jack.R: Iteration 3 of at most 4
> test-boot_jack.R: Iteration 4 of at most 4
> test-boot_jack.R: Finished simulated annealing
> test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
> test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-boot_jack.R: Obtaining the responsible dyads.
> test-boot_jack.R: Evaluating the predictor and response matrix.
> test-boot_jack.R: Maximizing the pseudolikelihood.
> test-boot_jack.R: Finished MPLE.
> test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-boot_jack.R: Iteration 1 of at most 60:
> test-boot_jack.R: 1
> test-boot_jack.R: Optimizing with step length 1.0000.
> test-boot_jack.R: The log-likelihood improved by 0.0009.
> test-boot_jack.R: Convergence test p-value: 0.0006. Converged with 99% confidence.
> test-boot_jack.R: Finished MCMLE.
> test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check
> test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
> test-boot_jack.R: Constructing pseudopopulation network.
> test-boot_jack.R: Starting simulated annealing (SAN)
> test-boot_jack.R: Iteration 1 of at most 4
> test-boot_jack.R: Finished simulated annealing
> test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
> test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-boot_jack.R: Obtaining the responsible dyads.
> test-boot_jack.R: Evaluating the predictor and response matrix.
> test-boot_jack.R: Maximizing the pseudolikelihood.
> test-boot_jack.R: Finished MPLE.
> test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-boot_jack.R: Iteration 1 of at most 60:
> test-boot_jack.R: 1 Optimizing with step length 1.0000.
> test-boot_jack.R: The log-likelihood improved by 0.0488.
> test-boot_jack.R: Convergence test p-value: 0.0024. Converged with 99% confidence.
> test-boot_jack.R: Finished MCMLE.
> test-boot_jack.R: This model was fit using MCMC. To examine model diagnostics and check
> test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
> test-boot_jack.R: Constructing pseudopopulation network.
> test-boot_jack.R: Starting simulated annealing (SAN)
> test-boot_jack.R: Iteration 1 of at most 4
> test-boot_jack.R: Finished simulated annealing
> test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
> test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-boot_jack.R: Obtaining the responsible dyads.
> test-boot_jack.R: Evaluating the predictor and response matrix.
> test-boot_jack.R: Maximizing the pseudolikelihood.
> test-boot_jack.R: Finished MPLE.
> test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-boot_jack.R: Iteration 1 of at most 60:
> test-boot_jack.R: 1 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. 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-drop.R:
> test-predict.ergm.ego.R: Constructing pseudopopulation network.
> test-predict.ergm.ego.R: Starting simulated annealing (SAN)
> test-predict.ergm.ego.R: Iteration 1 of at most 4
> test-predict.ergm.ego.R: Iteration 2 of at most 4
> test-predict.ergm.ego.R: Iteration 3 of at most 4
> test-predict.ergm.ego.R: Iteration 4 of at most 4
> test-predict.ergm.ego.R: Finished simulated annealing
> test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation.
> test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.ego.R: Obtaining the responsible dyads.
> test-predict.ergm.ego.R: Evaluating the predictor and response matrix.
> test-predict.ergm.ego.R: Maximizing the pseudolikelihood.
> test-predict.ergm.ego.R: Finished MPLE.
> test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-predict.ergm.ego.R: Iteration 1 of at most 2:
> test-predict.ergm.ego.R: 1
> test-predict.ergm.ego.R: Optimizing with step length 0.5473.
> test-predict.ergm.ego.R: The log-likelihood improved by 1.8671.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: Iteration 2 of at most 2:
> test-predict.ergm.ego.R: 1
> test-predict.ergm.ego.R: Optimizing with step length 1.0000.
> test-predict.ergm.ego.R: The log-likelihood improved by 1.0036.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-predict.ergm.ego.R: Finished MCMLE.
> test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check
> test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function.
> test-predict.ergm.ego.R: Constructing pseudopopulation network.
> test-predict.ergm.ego.R: Starting simulated annealing (SAN)
> test-predict.ergm.ego.R: Iteration 1 of at most 4
> test-predict.ergm.ego.R: Iteration 2 of at most 4
> test-predict.ergm.ego.R: Iteration 3 of at most 4
> test-predict.ergm.ego.R: Iteration 4 of at most 4
> test-predict.ergm.ego.R: Finished simulated annealing
> test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation.
> test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.ego.R: Obtaining the responsible dyads.
> test-predict.ergm.ego.R: Evaluating the predictor and response matrix.
> test-predict.ergm.ego.R: Maximizing the pseudolikelihood.
> test-predict.ergm.ego.R: Finished MPLE.
> test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-predict.ergm.ego.R: Iteration 1 of at most 2:
> test-predict.ergm.ego.R: 1
> test-predict.ergm.ego.R: Optimizing with step length 0.7865.
> test-predict.ergm.ego.R: The log-likelihood improved by 1.8825.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: Iteration 2 of at most 2:
> test-predict.ergm.ego.R: 1
> test-predict.ergm.ego.R: Optimizing with step length 1.0000.
> test-predict.ergm.ego.R: The log-likelihood improved by 0.3775.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-predict.ergm.ego.R: Finished MCMLE.
> test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check
> test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function.
> test-table_ppop.R: Constructing pseudopopulation network.
> test-table_ppop.R: Starting simulated annealing (SAN)
> test-table_ppop.R: Iteration 1 of at most 4
> test-table_ppop.R: Iteration 2 of at most 4
> test-table_ppop.R: Iteration 3 of at most 4
> test-table_ppop.R: Iteration 4 of at most 4
> test-table_ppop.R: Finished simulated annealing
> test-table_ppop.R: Unable to match target stats. Using MCMLE estimation.
> test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-table_ppop.R: Obtaining the responsible dyads.
> test-table_ppop.R: Evaluating the predictor and response matrix.
> test-table_ppop.R: Maximizing the pseudolikelihood.
> test-table_ppop.R: Finished MPLE.
> test-table_ppop.R: Constructing pseudopopulation network.
> test-table_ppop.R: Starting simulated annealing (SAN)
> test-table_ppop.R: Iteration 1 of at most 4
> test-table_ppop.R: Iteration 2 of at most 4
> test-table_ppop.R: Iteration 3 of at most 4
> test-table_ppop.R: Iteration 4 of at most 4
> test-table_ppop.R: Finished simulated annealing
> test-table_ppop.R: Unable to match target stats. Using MCMLE estimation.
> test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-table_ppop.R: Obtaining the responsible dyads.
> test-table_ppop.R: Evaluating the predictor and response matrix.
> test-table_ppop.R: Maximizing the pseudolikelihood.
> test-table_ppop.R: Finished MPLE.
Saving _problems/test-table_ppop-39.R
> test-gof.ergm.ego.R: Constructing pseudopopulation network.
> test-gof.ergm.ego.R: Starting simulated annealing (SAN)
> test-gof.ergm.ego.R: Iteration 1 of at most 4
> test-gof.ergm.ego.R: Finished simulated annealing
> test-gof.ergm.ego.R: Unable to match target stats. Using MCMLE estimation.
> test-gof.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.ergm.ego.R: Obtaining the responsible dyads.
> test-gof.ergm.ego.R: Evaluating the predictor and response matrix.
> test-gof.ergm.ego.R: Maximizing the pseudolikelihood.
> test-gof.ergm.ego.R: Finished MPLE.
> test-gof.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gof.ergm.ego.R: Iteration 1 of at most 2:
> test-gof.ergm.ego.R: 1 Optimizing with step length 1.0000.
> test-gof.ergm.ego.R: The log-likelihood improved by 1.6103.
> test-gof.ergm.ego.R: Estimating equations are not within tolerance region.
> test-gof.ergm.ego.R: Iteration 2 of at most 2:
> test-gof.ergm.ego.R: 1 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-release-linux-x86_64
Version: 1.1.3
Check: tests
Result: ERROR
Running 'testthat.R' [77s]
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-attrmismatch.R: Constructing pseudopopulation network.
> test-EgoStat.R: Iteration 3 of at most 4
> test-EgoStat.R: Finished simulated annealing
> test-attrmismatch.R: Starting simulated annealing (SAN)
> test-attrmismatch.R: Iteration 1 of at most 4
> test-attrmismatch.R: Iteration 2 of at most 4
> test-attrmismatch.R: Iteration 3 of at most 4
> test-attrmismatch.R: Iteration 4 of at most 4
> test-attrmismatch.R: Finished simulated annealing
> test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation.
> test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-attrmismatch.R: Obtaining the responsible dyads.
> test-attrmismatch.R: Evaluating the predictor and response matrix.
> test-attrmismatch.R: Maximizing the pseudolikelihood.
> test-attrmismatch.R: Finished MPLE.
> test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-attrmismatch.R: Iteration 1 of at most 60:
> test-attrmismatch.R: 1 Optimizing with step length 1.0000.
> test-attrmismatch.R: The log-likelihood improved by 0.0234.
> 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 0.0421.
> 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 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-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 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-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-drop.R:
> test-predict.ergm.ego.R: Constructing pseudopopulation network.
> test-predict.ergm.ego.R: Starting simulated annealing (SAN)
> test-predict.ergm.ego.R: Iteration 1 of at most 4
> test-predict.ergm.ego.R: Iteration 2 of at most 4
> test-predict.ergm.ego.R: Iteration 3 of at most 4
> test-predict.ergm.ego.R: Iteration 4 of at most 4
> test-predict.ergm.ego.R: Finished simulated annealing
> test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation.
> test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.ego.R: Obtaining the responsible dyads.
> test-predict.ergm.ego.R: Evaluating the predictor and response matrix.
> test-predict.ergm.ego.R: Maximizing the pseudolikelihood.
> test-predict.ergm.ego.R: Finished MPLE.
> test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-predict.ergm.ego.R: Iteration 1 of at most 2:
> test-predict.ergm.ego.R: 1
> test-predict.ergm.ego.R: Optimizing with step length 0.5473.
> test-predict.ergm.ego.R: The log-likelihood improved by 1.8671.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: Iteration 2 of at most 2:
> test-predict.ergm.ego.R: 1
> test-predict.ergm.ego.R: Optimizing with step length 1.0000.
> test-predict.ergm.ego.R: The log-likelihood improved by 1.0036.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-predict.ergm.ego.R: Finished MCMLE.
> test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check
> test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function.
> test-predict.ergm.ego.R: Constructing pseudopopulation network.
> test-predict.ergm.ego.R: Starting simulated annealing (SAN)
> test-predict.ergm.ego.R: Iteration 1 of at most 4
> test-predict.ergm.ego.R: Iteration 2 of at most 4
> test-predict.ergm.ego.R: Iteration 3 of at most 4
> test-predict.ergm.ego.R: Iteration 4 of at most 4
> test-predict.ergm.ego.R: Finished simulated annealing
> test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation.
> test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-predict.ergm.ego.R: Obtaining the responsible dyads.
> test-predict.ergm.ego.R: Evaluating the predictor and response matrix.
> test-predict.ergm.ego.R: Maximizing the pseudolikelihood.
> test-predict.ergm.ego.R: Finished MPLE.
> test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-predict.ergm.ego.R: Iteration 1 of at most 2:
> test-predict.ergm.ego.R: 1
> test-predict.ergm.ego.R: Optimizing with step length 0.7865.
> test-predict.ergm.ego.R: The log-likelihood improved by 1.8825.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: Iteration 2 of at most 2:
> test-predict.ergm.ego.R: 1
> test-predict.ergm.ego.R: Optimizing with step length 1.0000.
> test-predict.ergm.ego.R: The log-likelihood improved by 0.3775.
> test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
> test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-predict.ergm.ego.R: Finished MCMLE.
> test-predict.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check
> test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function.
> test-table_ppop.R: Constructing pseudopopulation network.
> test-table_ppop.R: Starting simulated annealing (SAN)
> test-table_ppop.R: Iteration 1 of at most 4
> test-table_ppop.R: Iteration 2 of at most 4
> test-table_ppop.R: Iteration 3 of at most 4
> test-table_ppop.R: Iteration 4 of at most 4
> test-table_ppop.R: Finished simulated annealing
> test-table_ppop.R: Unable to match target stats. Using MCMLE estimation.
> test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-table_ppop.R: Obtaining the responsible dyads.
> test-table_ppop.R: Evaluating the predictor and response matrix.
> test-table_ppop.R: Maximizing the pseudolikelihood.
> test-table_ppop.R: Finished MPLE.
> test-table_ppop.R: Constructing pseudopopulation network.
> test-table_ppop.R: Starting simulated annealing (SAN)
> test-table_ppop.R: Iteration 1 of at most 4
> test-table_ppop.R: Iteration 2 of at most 4
> test-table_ppop.R: Iteration 3 of at most 4
> test-table_ppop.R: Iteration 4 of at most 4
> test-table_ppop.R: Finished simulated annealing
> test-table_ppop.R: Unable to match target stats. Using MCMLE estimation.
> test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-table_ppop.R: Obtaining the responsible dyads.
> test-table_ppop.R: Evaluating the predictor and response matrix.
> test-table_ppop.R: Maximizing the pseudolikelihood.
> test-table_ppop.R: Finished MPLE.
Saving _problems/test-table_ppop-39.R
> test-gof.ergm.ego.R: Constructing pseudopopulation network.
> test-gof.ergm.ego.R: Starting simulated annealing (SAN)
> test-gof.ergm.ego.R: Iteration 1 of at most 4
> test-gof.ergm.ego.R: Finished simulated annealing
> test-gof.ergm.ego.R: Unable to match target stats. Using MCMLE estimation.
> test-gof.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE):
> test-gof.ergm.ego.R: Obtaining the responsible dyads.
> test-gof.ergm.ego.R: Evaluating the predictor and response matrix.
> test-gof.ergm.ego.R: Maximizing the pseudolikelihood.
> test-gof.ergm.ego.R: Finished MPLE.
> test-gof.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
> test-gof.ergm.ego.R: Iteration 1 of at most 2:
> test-gof.ergm.ego.R: 1
> test-gof.ergm.ego.R: Optimizing with step length 1.0000.
> test-gof.ergm.ego.R: The log-likelihood improved by 1.6103.
> test-gof.ergm.ego.R: Estimating equations are not within tolerance region.
> test-gof.ergm.ego.R: Iteration 2 of at most 2:
> test-gof.ergm.ego.R: 1
> test-gof.ergm.ego.R: Optimizing with step length 1.0000.
> test-gof.ergm.ego.R: The log-likelihood improved by 0.0094.
> test-gof.ergm.ego.R: Convergence test p-value: 0.3069. Not converged with 99% confidence; increasing sample size.
> test-gof.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
> test-gof.ergm.ego.R: Finished MCMLE.
> test-gof.ergm.ego.R: This model was fit using MCMC. To examine model diagnostics and check
> test-gof.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function.
Saving _problems/test-gof.ergm.ego-17.R
Saving _problems/test-gof.ergm.ego-32.R
Saving _problems/test-gof.ergm.ego-48.R
[ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ]
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test-table_ppop.R:39:3'): estimation and simulation work ──────────
Expected `(egosim <- simulate(egofit_scl, popsize = ppop))` to run silently.
Actual noise: messages.
── Failure ('test-gof.ergm.ego.R:15:3'): GOF='model' works ─────────────────────
Expected `z <- gof(fmhfit, GOF = "model")` to run silently.
Actual noise: messages.
── Failure ('test-gof.ergm.ego.R:30:3'): GOF='degree' works ────────────────────
Expected `z <- gof(fmhfit, GOF = "degree")` to run silently.
Actual noise: messages.
── Failure ('test-gof.ergm.ego.R:46:3'): GOF='espartners' works ────────────────
Expected `z <- gof(fmhfit, GOF = "espartners")` to run silently.
Actual noise: messages.
[ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ]
Error:
! Test failures.
Execution halted
Flavor: r-release-windows-x86_64
Version: 1.1.3
Check: tests
Result: ERROR
Running 'testthat.R' [105s]
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-EgoStat.R: Iteration 3 of at most 4
> test-attrmismatch.R: Starting simulated annealing (SAN)
> test-EgoStat.R: Finished simulated annealing
> 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 0.0068.
> 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
> test-attrmismatch.R: Optimizing with step length 1.0000.
> test-attrmismatch.R: The log-likelihood improved by 0.0004.
> 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-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 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-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 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-oldrel-windows-x86_64
Current CRAN status: OK: 14
Current CRAN status: OK: 14
Current CRAN status: NOTE: 3, OK: 11
Version: 2.12.0
Check: for GNU extensions in Makefiles
Result: NOTE
GNU make is a SystemRequirements.
Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64
Current CRAN status: OK: 14
Current CRAN status: OK: 14
Current CRAN status: NOTE: 14
Version: 4.13.0
Check: R code for possible problems
Result: NOTE
Found the following possibly unsafe calls:
File ‘statnet.common/R/control.utilities.R’:
unlockBinding("snctrl", environment(snctrl))
unlockBinding("snctrl", environment(update_my_snctrl))
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-macos-arm64, r-devel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64
Current CRAN status: OK: 14