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> #  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 2003-2024 Statnet Commons
> ################################################################################
> library(latentnet)
Loading required package: network

'network' 1.18.2 (2023-12-04), 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' 4.6.0 (2023-12-17), 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.


'latentnet' 2.11.0 (2024-02-19), 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: the covariate matrix was transposed.
> 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)   2.1476 1.3528 3.026            < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Overall BIC:        379.4306 
Likelihood BIC:     251.675 
Latent space/clustering BIC:     127.7556 

Covariate coefficients MKL:
            Estimate
(Intercept) 1.083133


> plot(samp.fit,labels=TRUE)
> 
> proc.time()
   user  system elapsed 
  12.75    0.53   13.29