Running 'EgoStat.tests.R' [7s]
  Running 'boot_jack.R' [18s]
  Running 'table_ppop.R' [84s]
  Running 'testthat.R' [96s]
Running the tests in 'tests/EgoStat.tests.R' failed.
Complete output:
  > #  File tests/EgoStat.tests.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 2015-2020 Statnet Commons
  > #######################################################################
  > library(ergm.ego)
  Loading required package: ergm
  Loading required package: network
  
  'network' 1.17.1 (2021-06-12), 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' 4.0.0 (2021-06-14), 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 release that introduces a fair number of
  backwards-incompatible changes. Please type 'news(package="ergm")' for
  a list of major changes.
  
  
  'ergm.ego' 0.6.1 (2020-11-19), 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 object is masked from 'package:base':
  
      sample
  
  > library(ergm)
  > 
  > n <- 100
  > e <- 150
  > ds <- c(10,15,5,20)
  > 
  > y <- network.initialize(n, directed=FALSE)
  > y %v% "a" <- sample(1:3+6,n,replace=TRUE)
  > y %v% "b" <- sample(letters[1:4],n,replace=TRUE)
  > y %v% "c" <- sample(runif(10),n,replace=TRUE)
  > y %v% "d" <- runif(n)
  > y <- san(y~edges+degree(0:3), target.stats=c(e,ds))
  > 
  > y.e <- as.egodata(y)
  > 
  > f <- ~ edges +
  +   nodecov("a") + nodecov(c("a", "c")) + nodecov(c("a", "c", "d")) + nodecov(~c^2 + sin(d)) + offset(nodecov(~c^2 + sin(d))) + 
  +   
  +   nodefactor("a", levels=NULL) + nodefactor("a", levels=-1) + nodefactor("a", levels=1) + nodefactor("a", levels=-2) + nodefactor("b", levels=c("c")) + 
  +   nodefactor("b", levels=c("c", "a")) + nodefactor("b", levels=c("c", "e", "a")) + nodefactor("b", levels=c("c", "e")) + nodefactor(~a, levels = 2:3) + 
  +   nodefactor(c("b", "c"), levels = -(5:10)) + offset(nodefactor(c("b", "c"), levels = -(5:10))) + 
  +   
  +   nodematch("a") + nodematch("a", TRUE) + nodematch("a", TRUE, levels=2) + nodematch(c("a", "b"), TRUE, levels = 5:10) + nodematch(~2*a) + offset(nodematch(~a^2)) + 
  +   
  +   absdiff("a") + absdiff("a", 2) + absdiff(~a + 2*c - exp(d)) + 
  +   absdiff(function(x) if(is(x, "data.frame")) x[["a"]] + 2*x[["c"]] - exp(x[["d"]]) else (x %v% "a") + 2*(x %v% "c") - exp(x %v% "d")) + 
  +   
  +   degree(0) + degree(3) + degree(0:6) +
  +   degree(0, by="a") + degree(3, by="a") + degree(0:6, by="a") +
  +   degree(0, by="a", homophily=TRUE) + degree(3, by="a", homophily=TRUE) + degree(0:6, by="a", homophily=TRUE) + degree(0:6, by="a", homophily=FALSE, levels = -1) + 
  +   degree(0:6, by="a", homophily=FALSE, levels=1) + degree(0:6, by=c("a", "b"), homophily=FALSE, levels = -1) + degree(0:6, by=c("a", "b"), homophily=FALSE, levels=1) +
  +   
  +   degrange(0) + degrange(3) + degrange(0:6) +
  +   degrange(0) + degrange(3) + degrange(0:6) +
  +   degrange(0, by="a") + degrange(3, by="a") + degrange(0:6, by="a") + degrange(0:6, by=~b) + degrange(0:6, by= function(x) if(is(x, "data.frame")) x[["b"]] else x %v% "b") + 
  +   degrange(0, by="a", homophily=TRUE) + degrange(3, by="a", homophily=TRUE) + degrange(0:6, by="a", homophily=TRUE) + offset(degrange(0:6, by="a", homophily=TRUE)) + 
  +   degrange(0:6, by="a", homophily=FALSE, levels=2) + offset(degrange(0:6, by="a", homophily=FALSE, levels=-2)) + 
  +   degrange(0:6, by=c("a","b"), homophily=FALSE, levels=3) + offset(degrange(0:6, by=c("a","b"), homophily=FALSE, levels=-3)) + 
  +   
  +   degrange(0,2) + degrange(3,5) + degrange(0:6,7) +  
  +   degrange(0,2) + degrange(3,5) + degrange(0:6,7) +
  +   degrange(0,2, by="a") + degrange(3,5, by="a") + degrange(0:6,7, by="a") + degrange(0:6,7, by=~b) + 
  +   degrange(0,2, by="a", homophily=TRUE) + degrange(3,5, by="a", homophily=TRUE) + degrange(0:6,7, by="a", homophily=TRUE) +
  +   
  +   concurrent + concurrent("a") + concurrent("a", levels=2:3) + concurrent("a", levels=3) + concurrent(c("a", "b"), levels=2:3) + 
  +   concurrent(c("a", "b"), levels=4) + offset(concurrent(~a)) + 
  +   
  +   concurrentties + concurrentties("a") + offset(concurrentties(function(x) if(is(x, "data.frame")) x[["a"]]else (x %v% "a"))) + 
  +   concurrentties("a", levels=2:3) + concurrentties("a", levels=-1) + concurrentties("b", levels=c("a", "c")) + concurrentties("b", levels=c("a", "b", "e")) +
  +   concurrentties(c("a", "b"), levels=2:3) + concurrentties(c("a", "b"), levels=-3) +
  +   
  +   degree1.5 + offset(degree1.5) + 
  +   
  +   nodemix("a") + nodemix("a", levels2=-1) + nodemix("a", levels2=1) + nodemix("b", levels = c("b"), levels2=1) + nodemix("a", levels2=-2) + nodemix("a", levels2=-(2:3)) + 
  +   nodemix(c("a", "b"), levels = -(2:3)) + nodemix(c("a", "b"), levels = -(2:3), levels2 = -(3:4)) + nodemix("b", levels = -2, levels2 = -3) + 
  +   offset(nodemix("b", levels = -2, levels2 = -3)) + 
  +   
  +   mm("a") + mm("a", levels2=~-1) + mm("a", levels2=-2) + mm("a", levels2=-(2:3)) + mm(~a>7) + mm(a~b) + mm(.~a) + offset(mm(.~a)) + mm("a", levels2 = 1) + 
  +   mm("b", levels = c("a", "c", "e")) + mm("b", levels = c("a", "c", "e"), levels2 = 3) +
  + 
  +   meandeg
  > 
  > f.y <- statnet.common::nonsimp_update.formula(f, y~.)
  > environment(f.y) <- globalenv()
  > f.y.e <- statnet.common::nonsimp_update.formula(f, y.e~.)
  > environment(f.y.e) <- globalenv()
  > 
  > stopifnot(all.equal(summary(f.y),summary(f.y.e)))
  Error: summary(f.y) and summary(f.y.e) are not equal:
    Names: 187 string mismatches
    Numeric: lengths (784, 792) differ
  Execution halted
Running the tests in 'tests/table_ppop.R' failed.
Complete output:
  > #  File tests/table_ppop.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 2015-2020 Statnet Commons
  > #######################################################################
  > library(ergm.ego)
  Loading required package: ergm
  Loading required package: network
  
  'network' 1.17.1 (2021-06-12), 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' 4.0.0 (2021-06-14), 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 release that introduces a fair number of
  backwards-incompatible changes. Please type 'news(package="ergm")' for
  a list of major changes.
  
  
  'ergm.ego' 0.6.1 (2020-11-19), 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 object is masked from 'package:base':
  
      sample
  
  > data(faux.mesa.high)
  > fmh.ego <- as.egodata(faux.mesa.high)
  Network does not have vertex attribute 'vertex.names' to use as ego ID; using 1..N.
  > 
  > set.seed(0)
  > ppop <- rbind(fmh.ego$egos, fmh.ego$egos)
  > egofit <- ergm.ego(fmh.ego~edges+degree(0:3)+nodefactor("Race")+nodematch("Race")
  +                    +nodefactor("Sex")+nodematch("Sex")+absdiff("Grade"), 
  +                    popsize=network.size(faux.mesa.high),
  +                    control=control.ergm.ego(ppopsize=ppop))
  Constructing pseudopopulation network.
  Unable to match target stats. Using MCMLE estimation.
  Starting maximum pseudolikelihood estimation (MPLE):
  Evaluating the predictor and response matrix.
  Maximizing the pseudolikelihood.
  Finished MPLE.
  Starting Monte Carlo maximum likelihood estimation (MCMLE):
  Iteration 1 of at most 60:
  Optimizing with step length 0.3570.
  The log-likelihood improved by 2.1714.
  Estimating equations are not within tolerance region.
  Iteration 2 of at most 60:
  Optimizing with step length 0.5759.
  The log-likelihood improved by 1.0701.
  Estimating equations are not within tolerance region.
  Iteration 3 of at most 60:
  Optimizing with step length 1.0000.
  The log-likelihood improved by 0.8537.
  Estimating equations are not within tolerance region.
  Iteration 4 of at most 60:
  Optimizing with step length 1.0000.
  The log-likelihood improved by 0.1476.
  Estimating equations are not within tolerance region.
  Iteration 5 of at most 60:
  Optimizing with step length 1.0000.
  The log-likelihood improved by 0.3074.
  Estimating equations are not within tolerance region.
  Iteration 6 of at most 60:
  Optimizing with step length 1.0000.
  The log-likelihood improved by 0.2674.
  Estimating equations are not within tolerance region.
  Iteration 7 of at most 60:
  Optimizing with step length 1.0000.
  The log-likelihood improved by 0.2579.
  Estimating equations are not within tolerance region.
  Iteration 8 of at most 60:
  Optimizing with step length 1.0000.
  The log-likelihood improved by 0.2117.
  Estimating equations are not within tolerance region.
  Iteration 9 of at most 60:
  Optimizing with step length 1.0000.
  The log-likelihood improved by 0.1747.
  Estimating equations are not within tolerance region.
  Iteration 10 of at most 60:
  Optimizing with step length 1.0000.
  The log-likelihood improved by 0.3931.
  Estimating equations are not within tolerance region.
  Iteration 11 of at most 60:
  Optimizing with step length 1.0000.
  The log-likelihood improved by 0.2240.
  Estimating equations are not within tolerance region.
  Iteration 12 of at most 60:
  Optimizing with step length 1.0000.
  The log-likelihood improved by 0.1837.
  Estimating equations are not within tolerance region.
  Iteration 13 of at most 60:
  Optimizing with step length 1.0000.
  The log-likelihood improved by 0.4803.
  Estimating equations are not within tolerance region.
  Estimating equations did not move closer to tolerance region more than 1 time(s) in 4 steps; increasing sample size.
  Iteration 14 of at most 60:
  Optimizing with step length 1.0000.
  The log-likelihood improved by 0.7461.
  Estimating equations are not within tolerance region.
  Iteration 15 of at most 60:
  Optimizing with step length 1.0000.
  The log-likelihood improved by 0.0973.
  Convergence test p-value: 0.7747. Not converged with 99% confidence; increasing sample size.
  Iteration 16 of at most 60:
  Optimizing with step length 1.0000.
  The log-likelihood improved by 0.0747.
  Convergence test p-value: 0.8271. Not converged with 99% confidence; increasing sample size.
  Iteration 17 of at most 60:
  Optimizing with step length 1.0000.
  The log-likelihood improved by 0.0835.
  Convergence test p-value: 0.0779. Not converged with 99% confidence; increasing sample size.
  Iteration 18 of at most 60:
  Optimizing with step length 1.0000.
  The log-likelihood improved by 0.0325.
  Convergence test p-value: 0.0657. Not converged with 99% confidence; increasing sample size.
  Iteration 19 of at most 60:
  Optimizing with step length 1.0000.
  The log-likelihood improved by 0.0239.
  Convergence test p-value: 0.0958. Not converged with 99% confidence; increasing sample size.
  Iteration 20 of at most 60:
  Optimizing with step length 1.0000.
  The log-likelihood improved by 0.0176.
  Convergence test p-value: < 0.0001. Converged with 99% confidence.
  Finished MCMLE.
  This model was fit using MCMC.  To examine model diagnostics and check
  for degeneracy, use the mcmc.diagnostics() function.
  > 
  > stopifnot(isTRUE(all.equal(c(## -0.8407,
  +                              2.3393, 1.4686, 0.6323, 0.5287, -1.3603, -1.0454,
  +                              -2.4998, -0.7207, 0.833, -0.1823, 0.6357, -1.3513),
  +                            unname(coef(egofit))[-(1:2)],tolerance=.1)))
  > 
  > ppop <- fmh.ego$egos[sample.int(nrow(fmh.ego$egos), nrow(fmh.ego$egos)*1.5, replace=TRUE),]
  > egosim <- simulate(egofit, popsize=ppop)
  Error in simulate.formula_lhs(object = fmh.ego ~ offset(netsize.adj) +  : 
    No applicable method for LHS of type 'ergm_state_full', 'ergm_state_send', 'ergm_state_receive', 'ergm_state'.
  Calls: simulate ... eval -> eval -> <Anonymous> -> simulate.formula_lhs
  Execution halted
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 2015-2020 Statnet Commons
  > #######################################################################
  > library(testthat)
  > 
  > test_check("testthat")
  Starting 2 test processes
  == Failed tests ================================================================
  -- Failure (test-drop.R:20:3): dropped ergm terms ------------------------------
  coef(ergm(nw ~ edges + nodematch("a"))) (`actual`) not equal to `out.coef` (`expected`).
  
    `actual`: -0.00 -Inf
  `expected`: -0.02 -Inf
  -- Failure (test-predict.ergm.ego.R:21:3): it just works for model without offsets --
  `p <- predict(egofit)` produced warnings.
  
  [ FAIL 2 | WARN 0 | SKIP 0 | PASS 9 ]
  Error: Test failures
  Execution halted