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> #  File tests/sim_gf_sum.R in package tergm, 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 2008-2025 Statnet Commons
> ################################################################################
> library(tergm)
Loading required package: ergm
Loading required package: network

'network' 1.19.0 (2024-12-08), 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' 4.9.0 (2025-06-09), 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: networkDynamic

'networkDynamic' 0.11.5 (2024-11-21), part of the Statnet Project
* 'news(package="networkDynamic")' for changes since last version
* 'citation("networkDynamic")' for citation information
* 'https://statnet.org' for help, support, and other information

Registered S3 method overwritten by 'tergm':
  method                   from
  simulate_formula.network ergm

'tergm' 4.2.2 (2025-06-15), part of the Statnet Project
* 'news(package="tergm")' for changes since last version
* 'citation("tergm")' for citation information
* 'https://statnet.org' for help, support, and other information


Attaching package: 'tergm'

The following object is masked from 'package:ergm':

    snctrl

> 
> logit<-function(p)log(p/(1-p))
> 
> coef.form.f<-function(coef.diss,density) -log(((1+exp(coef.diss))/(density/(1-density)))-1)
> 
> simtest <- function(S, edges, dur, n, dir=FALSE, bip=0){
+   target.stats<-edges
+   coef.diss<-logit(1-1/dur)
+   
+   dc <- suppressWarnings(matrix(rnorm(n*n),if(bip) bip else n, if(bip) n-bip else n))
+   if(!dir && !bip) dc <- dc+t(dc)
+   # Note that if nw1!=nw2, then
+   # summary(nw1~edgecov(dc)+edgecov.ages(dc))!=summary(nw2~edgecov(dc)+edgecov.ages(dc))
+   # with probability 1. Thus, they offer a sort of a checksum of a network.
+   
+   g0<-network.initialize(n, dir=dir, bipartite=bip)
+   g0 %n% "dc" <- dc
+   g0 %n% "time" <- 0
+ 
+   dyads <- network.dyadcount(g0)
+   density<-edges/dyads
+   
+   coef.form<-coef.form.f(coef.diss,density)
+   
+   # Get a reasonably close starting network.
+   g1<-san(g0~edges,target.stats=target.stats,verbose=TRUE)
+ 
+   g1 %n% "lasttoggle" <- cbind(as.edgelist(g1), -rgeom(network.edgecount(g1),1/4))
+   
+   print(coef.form)
+   print(coef.diss)
+   
+   # Simulate. Starting from an ordinary network:
+   dynsim<-simulate(g1 ~ Form(~edges) + Persist(~edges),coef=c(coef.form,coef.diss),time.slices=S,verbose=TRUE,monitor=~edgecov("dc")+edgecov.ages("dc"), dynamic=TRUE)
+   
+   sim.stats <- attr(dynsim, "stats")
+   
+   # Resuming from a networkDynamic:
+   dynsim2<-simulate(dynsim ~ Form(~edges) + Persist(~edges),coef=c(coef.form,coef.diss),time.slices=S,verbose=TRUE,monitor=~edgecov("dc")+edgecov.ages("dc"), dynamic=TRUE)
+   
+   sim.stats <- rbind(sim.stats, attr(dynsim2, "stats"))
+   
+   # Resuming from a resumed networkDynamic:
+   dynsim3<-simulate(dynsim2 ~ Form(~edges) + Persist(~edges),coef=c(coef.form,coef.diss),time.slices=S,verbose=TRUE,monitor=~edgecov("dc")+edgecov.ages("dc"), dynamic=TRUE)
+ 
+   sim.stats <- rbind(sim.stats, attr(dynsim3, "stats"))
+   
+   # Replay the simulation using a networkDynamic:
+   gf1.stats <- as.matrix(tergm.godfather(dynsim3~edgecov("dc")+edgecov.ages("dc"), start=0, end=S*3))
+   
+   # Replay the sim using networkDynamic with no explicit time params (should be using net.obs.period)
+   gf1b.stats <- as.matrix(tergm.godfather(dynsim3~edgecov("dc")+edgecov.ages("dc")))
+   
+   # Replay the simulation using a network + list of changes:
+   gf2.stats <- as.matrix(tergm.godfather(g1~edgecov("dc")+edgecov.ages("dc"), start=0, end=S*3, changes=attr(dynsim3,"changes")))
+   
+   # Evaluate summary statistics directly:
+   summ.stats <- summary(dynsim3~edgecov("dc")+edgecov.ages("dc"), at=1:(S*3))
+   
+   # If they aren't all identical, we are in trouble.
+   stopifnot(all.equal(sim.stats,gf1.stats),
+             all.equal(sim.stats,gf1b.stats),
+             all.equal(sim.stats,gf2.stats),
+             all.equal(sim.stats,summ.stats))
+ }
> 
> set.seed(1)
> cat("Undirected, unipartite:\n")
Undirected, unipartite:
> simtest(20, 10, 10, 10, FALSE, FALSE)
Initializing unconstrained Metropolis-Hastings proposal: 'ergm:MH_SPDyad'.
Initializing model...
Model initialized.
Starting 4 SAN iterations of 524288 steps each.
#1 of 4: Exact match found.
SAN Metropolis-Hastings accepted   0.000% of 32768 proposed steps.
SAN summary statistics:
edges 
   10 
Meanstats Goal:
edges 
   10 
Difference: SAN target.stats - Goal target.stats =
edges 
    0 
New statistics scaling =
[1] 1
Scaled Mahalanobis distance = 0
Target statistics matched exactly.
[1] -3.526361
[1] 2.197225
Returned from STERGM burnin
extracting state of networkDynamic at time  20
Returned from STERGM burnin
Updating networkDynamic with simulated time: ( 2141 ).
extracting state of networkDynamic at time  40
Returned from STERGM burnin
Updating networkDynamic with simulated time: ( 4161 ).
> cat("Directed, unipartite:\n")
Directed, unipartite:
> simtest(20, 10, 10, 10, TRUE, FALSE)
Initializing unconstrained Metropolis-Hastings proposal: 'ergm:MH_SPDyad'.
Initializing model...
Model initialized.
Starting 4 SAN iterations of 524288 steps each.
#1 of 4: Exact match found.
SAN Metropolis-Hastings accepted   0.000% of 32768 proposed steps.
SAN summary statistics:
edges 
   10 
Meanstats Goal:
edges 
   10 
Difference: SAN target.stats - Goal target.stats =
edges 
    0 
New statistics scaling =
[1] 1
Scaled Mahalanobis distance = 0
Target statistics matched exactly.
[1] -4.369448
[1] 2.197225
Returned from STERGM burnin
extracting state of networkDynamic at time  20
Returned from STERGM burnin
Updating networkDynamic with simulated time: ( 2141 ).
extracting state of networkDynamic at time  40
Returned from STERGM burnin
Updating networkDynamic with simulated time: ( 4161 ).
> cat("Undirected, biipartite:\n")
Undirected, biipartite:
> simtest(20, 10, 10, 10, FALSE, 3)
Initializing unconstrained Metropolis-Hastings proposal: 'ergm:MH_TNT'.
Initializing model...
Model initialized.
Starting 4 SAN iterations of 524288 steps each.
#1 of 4: Exact match found.
SAN Metropolis-Hastings accepted   0.000% of 32768 proposed steps.
SAN summary statistics:
edges 
   10 
Meanstats Goal:
edges 
   10 
Difference: SAN target.stats - Goal target.stats =
edges 
    0 
New statistics scaling =
[1] 1
Scaled Mahalanobis distance = 0
Target statistics matched exactly.
[1] -2.302585
[1] 2.197225
Returned from STERGM burnin
extracting state of networkDynamic at time  20
Returned from STERGM burnin
Updating networkDynamic with simulated time: ( 2141 ).
extracting state of networkDynamic at time  40
Returned from STERGM burnin
Updating networkDynamic with simulated time: ( 4161 ).
> 
> proc.time()
   user  system elapsed 
   6.42    0.46    6.87