Running 'missing.R' [18s]
  Running 'nofixed.R' [23s]
  Running 'nolatent.R' [14s]
  Running 'nomcmc.R' [10s]
  Running 'nomixing.R' [6s]
  Running 'normal.R' [0s]
  Running 'onlymcmc.R' [0s]
Running the tests in 'tests/missing.R' failed.
Complete output:
  > #  File tests/missing.R in package latentnet, 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 2003-2022 Statnet Commons
  > ################################################################################
  > library(latentnet)
  Loading required package: network
  
  'network' 1.17.2 (2022-05-20), 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
  
  Loading required package: ergm
  
  'ergm' 4.2.2 (2022-06-01), 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.
  
  
  'latentnet' 2.10.6 (2022-05-11), part of the Statnet Project
  * 'news(package="latentnet")' for changes since last version
  * 'citation("latentnet")' for citation information
  * 'https://statnet.org' for help, support, and other information
  NOTE: BIC calculation prior to latentnet 2.7.0 had a bug in the calculation of the effective number of parameters. See help(summary.ergmm) for details.
  NOTE: Prior to version 2.8.0, handling of fixed effects for directed networks had a bug. See help("ergmm-terms") for details.
  > data(sampson)
  > ym<-as.matrix(samplike)
  > ym[1,5]<-NA
  > yg<-as.network(ym,matrix.type="adjacency")
  > samp.fit<-ergmm(yg~euclidean(d=2))
  > 
  > print(summary(samp.fit))
  NOTE: It is not certain whether it is appropriate to use latentnet's BIC to select latent space dimension, whether or not to include actor-specific random effects, and to compare clustered models with the unclustered model.
  
  ==========================
  Summary of model fit
  ==========================
  
  Formula:   yg ~ euclidean(d = 2)
  Attribute: edges
  Model:     Bernoulli 
  MCMC sample of size 4000, draws are 10 iterations apart, after burnin of 10000 iterations.
  Covariate coefficients posterior means:
              Estimate   2.5%  97.5% 2*min(Pr(>0),Pr(<0))    
  (Intercept)   2.1694 1.3433 3.0568            < 2.2e-16 ***
  ---
  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
  
  Overall BIC:        379.591 
  Likelihood BIC:     251.0537 
  Latent space/clustering BIC:     128.5373 
  
  Covariate coefficients MKL:
              Estimate
  (Intercept) 1.104877
  
  
  > plot(samp.fit,labels=TRUE)
  > 
  > proc.time()
     user  system elapsed 
    16.77    0.26   17.44