Running ‘constrain_degrees_edges.R’ [4s/7s]
  Running ‘constrain_dind.R’ [3s/4s]
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  Running ‘ergmMPLEtest.R’ [2s/4s]
  Running ‘ergm_term_doc_test.R’ [5s/7s]
  Running ‘fix.curved.R’ [2s/4s]
  Running ‘gw_sp_tests.R’ [8s/12s]
  Running ‘hamming_termtests.R’ [4s/6s]
  Running ‘miss_tests.CD.R’ [0s/1s]
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  Running ‘mle_offset.R’ [0s/1s]
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  Running ‘stocapprox.R’ [0s/1s]
  Running ‘target_offset.R’ [12s/17s]
  Running ‘termTests.b12nodematch.R’ [2s/3s]
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  Running ‘valued_sim.R’ [6s/9s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > #  File tests/testthat.R in package ergm, 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 2003-2020 Statnet Commons
  > #######################################################################
  > library(testthat)
  > library(ergm)
  Loading required package: network
  network: Classes for Relational Data
  Version 1.16.1 created on 2020-10-06.
  copyright (c) 2005, Carter T. Butts, University of California-Irvine
                      Mark S. Handcock, University of California -- Los Angeles
                      David R. Hunter, Penn State University
                      Martina Morris, University of Washington
                      Skye Bender-deMoll, University of Washington
   For citation information, type citation("network").
   Type help("network-package") to get started.
  
  
  ergm: version 3.11.0, created on 2020-10-14
  Copyright (c) 2020, Mark S. Handcock, University of California -- Los Angeles
                      David R. Hunter, Penn State University
                      Carter T. Butts, University of California -- Irvine
                      Steven M. Goodreau, University of Washington
                      Pavel N. Krivitsky, UNSW Sydney
                      Martina Morris, University of Washington
                      with contributions from
                      Li Wang
                      Kirk Li, University of Washington
                      Skye Bender-deMoll, University of Washington
                      Chad Klumb
                      Michał Bojanowski, Kozminski University
                      Ben Bolker
  Based on "statnet" project software (statnet.org).
  For license and citation information see statnet.org/attribution
  or type citation("ergm").
  
  NOTE: Versions before 3.6.1 had a bug in the implementation of the bd()
  constraint which distorted the sampled distribution somewhat. In
  addition, Sampson's Monks datasets had mislabeled vertices. See the
  NEWS and the documentation for more details.
  
  NOTE: Some common term arguments pertaining to vertex attribute and
  level selection have changed in 3.10.0. See terms help for more
  details. Use 'options(ergm.term=list(version="3.9.4"))' to use old
  behavior.
  
  > 
  > test_check("ergm")
  [1] 350  50 250
  Structural check:
  Mean degree: 1.4 .
  Average degree among nodes with degree 2 or higher: 2.25 .
  Sample statistics summary:
  
  Iterations = 1:1024
  Thinning interval = 1 
  Number of chains = 1 
  Sample size per chain = 1024 
  
  1. Empirical mean and standard deviation for each variable,
     plus standard error of the mean:
  
             Mean    SD Naive SE Time-series SE
  edgecov.a 3.190 2.282  0.07130         0.9579
  edgecov.a 1.024 1.762  0.05505         0.6171
  
  2. Quantiles for each variable:
  
            2.5% 25% 50% 75% 97.5%
  edgecov.a   -2   2   4   5     6
  edgecov.a   -3   0   1   2     4
  
  
  Sample statistics cross-correlations:
            edgecov.a edgecov.a
  edgecov.a 1.0000000 0.8544418
  edgecov.a 0.8544418 1.0000000
  
  Sample statistics auto-correlation:
  Chain 1 
        edgecov.a edgecov.a
  Lag 0 1.0000000 1.0000000
  Lag 1 0.9889689 0.9841949
  Lag 2 0.9786889 0.9693349
  Lag 3 0.9685966 0.9551049
  Lag 4 0.9590676 0.9424500
  Lag 5 0.9495386 0.9291651
  
  Sample statistics burn-in diagnostic (Geweke):
  Chain 1 
  
  Fraction in 1st window = 0.1
  Fraction in 2nd window = 0.5 
  
  edgecov.a edgecov.a 
     -1.250    -1.491 
  
  Individual P-values (lower = worse):
  edgecov.a edgecov.a 
  0.2113476 0.1359306 
  Joint P-value (lower = worse):  0.8684058 .
  
  MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).
  ══ Skipped tests ═══════════════════════════════════════════════════════════════
  ● empty test (2)
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Failure (test-bd.R:27:3): Bounded degree (bd()) maximum constraint for undirected networks ──
  approx.hotelling.diff.test(constr, degr[, -2])$p.value > 0.01 is not TRUE
  
  `actual`:   FALSE
  `expected`: TRUE 
  
  [ FAIL 1 | WARN 6 | SKIP 2 | PASS 168 ]
  Error: Test failures
  Execution halted