Running ‘spelling.R’ [0s/1s]
  Running ‘testthat.R’ [5s/8s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(missSBM)
  
  Attaching package: 'missSBM'
  
  The following objects are masked from 'package:stats':
  
      simulate, smooth
  
  The following object is masked from 'package:base':
  
      sample
  
  > 
  > test_check("missSBM")
  Tested sampling:
   - dyad
  sampling: dyad
  
   Adjusting Variational EM for Stochastic Block Model
  
  	Imputation assumes a 'dyad' network-sampling process
  
   Initialization of model with 3 blocks. 
  
   Adjusting Variational EM for Stochastic Block Model
  
  	Imputation assumes a 'covar-dyad' network-sampling process
  
   Initialization of model with 2 blocks. 
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error (test-INIT-clustering.R:19:1): (code run outside of `test_that()`) ────
  Error: no applicable method for 'isSymmetric' applied to an object of class "c('double', 'numeric')"
  Backtrace:
      █
   1. └─missSBM::observeNetwork(A_full, "dyad", psi) test-INIT-clustering.R:19:0
   2.   └─base::isSymmetric(adjacencyMatrix)
  ── Error (test-MISSSBM-fit-with-covariates.R:37:3): missSBM with covariates and dyad sampling works ──
  Error: no applicable method for 'isSymmetric' applied to an object of class "c('double', 'numeric')"
  Backtrace:
      █
   1. └─missSBM::observeNetwork(...) test-MISSSBM-fit-with-covariates.R:37:2
   2.   └─base::isSymmetric(adjacencyMatrix)
  ── Error (test-MISSSBM-fit-with-covariates.R:105:3): miss SBM with covariates and node sampling works ──
  Error: no applicable method for 'isSymmetric' applied to an object of class "c('double', 'numeric')"
  Backtrace:
      █
   1. └─missSBM::observeNetwork(...) test-MISSSBM-fit-with-covariates.R:105:2
   2.   └─base::isSymmetric(adjacencyMatrix)
  ── Error (test-MISSSBM-fit-without-covariate.R:38:5): missSBM-fit works and is consistent for all samplings ──
  Error: no applicable method for 'isSymmetric' applied to an object of class "c('double', 'numeric')"
  Backtrace:
      █
   1. └─missSBM::observeNetwork(...) test-MISSSBM-fit-without-covariate.R:38:4
   2.   └─base::isSymmetric(adjacencyMatrix)
  ── Error (test-MISSSBM_collection.R:17:3): missSBMcollection works ─────────────
  Error: no applicable method for 'isSymmetric' applied to an object of class "c('double', 'numeric')"
  Backtrace:
      █
   1. └─missSBM::observeNetwork(A, "dyad", 0.5, clusters = mySBM$memberships) test-MISSSBM_collection.R:17:2
   2.   └─base::isSymmetric(adjacencyMatrix)
  ── Error (test-MISSSBM_collection.R:46:3): More smoothing tests ────────────────
  Error: no applicable method for 'isSymmetric' applied to an object of class "c('double', 'numeric')"
  Backtrace:
      █
   1. └─missSBM::observeNetwork(A, "dyad", 0.5, clusters = mySBM$memberships) test-MISSSBM_collection.R:46:2
   2.   └─base::isSymmetric(adjacencyMatrix)
  ── Error (test-SAMPLING-fit-with-covariates.R:20:3): Parameter estimation in dyad-centered sampling with covariates ──
  Error: no applicable method for 'isSymmetric' applied to an object of class "c('double', 'numeric')"
  Backtrace:
      █
   1. └─missSBM::observeNetwork(...) test-SAMPLING-fit-with-covariates.R:20:2
   2.   └─base::isSymmetric(adjacencyMatrix)
  ── Error (test-SAMPLING-fit-with-covariates.R:42:3): Parameter estimation in dyad-centered sampling with covariates but ignoring them ──
  Error: no applicable method for 'isSymmetric' applied to an object of class "c('double', 'numeric')"
  Backtrace:
      █
   1. └─missSBM::observeNetwork(...) test-SAMPLING-fit-with-covariates.R:42:2
   2.   └─base::isSymmetric(adjacencyMatrix)
  ── Error (test-SAMPLING-fit-with-covariates.R:66:3): Parameter estimation in node-centered sampling with covariates ──
  Error: no applicable method for 'isSymmetric' applied to an object of class "c('double', 'numeric')"
  Backtrace:
      █
   1. └─missSBM::observeNetwork(...) test-SAMPLING-fit-with-covariates.R:66:2
   2.   └─base::isSymmetric(adjacencyMatrix)
  ── Error (test-SAMPLING-fit-with-covariates.R:90:3): Parameter estimation in node-centered sampling with covariates but ignoring them ──
  Error: no applicable method for 'isSymmetric' applied to an object of class "c('double', 'numeric')"
  Backtrace:
      █
   1. └─missSBM::observeNetwork(sbm$netMatrix, "node", 0.9, covariates = covariates_node) test-SAMPLING-fit-with-covariates.R:90:2
   2.   └─base::isSymmetric(adjacencyMatrix)
  ── Error (test-SAMPLING-fit-with-covariates.R:110:3): Parameter estimation in degree sampling ──
  Error: no applicable method for 'isSymmetric' applied to an object of class "c('double', 'numeric')"
  Backtrace:
      █
   1. └─missSBM::observeNetwork(sbm$netMatrix, "degree", psi) test-SAMPLING-fit-with-covariates.R:110:2
   2.   └─base::isSymmetric(adjacencyMatrix)
  ── Error (test-SAMPLING-fit-without-covariate.R:32:5): Consistency of sampling fit ──
  Error: no applicable method for 'isSymmetric' applied to an object of class "c('double', 'numeric')"
  Backtrace:
      █
   1. └─missSBM::observeNetwork(...) test-SAMPLING-fit-without-covariate.R:32:4
   2.   └─base::isSymmetric(adjacencyMatrix)
  ── Error (test-SAMPLING-sampler.R:86:3): Consistency of double-standard sampling ──
  Error: non-numeric matrix extent
  Backtrace:
      █
   1. └─missSBM:::doubleStandardSampler$new(psi, A, directed) test-SAMPLING-sampler.R:86:2
   2.   └─missSBM:::initialize(...)
   3.     └─super$initialize(...)
   4.       └─base::matrix(NA, private$N, private$N)
  ── Error (test-SAMPLING-sampler.R:112:3): Consistency of degree network sampling ──
  Error: 'x' must be an array of at least two dimensions
  Backtrace:
      █
   1. ├─missSBM:::degreeSampler$new(psi, rowSums(A), directed) test-SAMPLING-sampler.R:112:2
   2. │ └─missSBM:::initialize(...)
   3. │   └─super$initialize("degree", parameters, length(degrees), directed)
   4. └─base::rowSums(A)
  ── Error (test-SAMPLING-sampler.R:139:3): Consistency of snowball sampling ─────
  Error: invalid arguments
  Backtrace:
      █
   1. └─missSBM:::snowballSampler$new(param, A, directed) test-SAMPLING-sampler.R:139:2
   2.   └─missSBM:::initialize(...)
   3.     └─stats::runif(n)
  ── Error (test-SBM-sbm-fit-with-covariates.R:29:1): (code run outside of `test_that()`) ──
  Error: non-numeric matrix extent
  Backtrace:
      █
   1. └─base::matrix(NA, nrow(A), ncol(A)) test-SBM-sbm-fit-with-covariates.R:29:0
  ── Error (test-SBM-sbm-fit-without-covariate.R:18:1): (code run outside of `test_that()`) ──
  Error: only matrix diagonals can be replaced
  Backtrace:
      █
   1. └─base::`diag<-`(`*tmp*`, value = 0) test-SBM-sbm-fit-without-covariate.R:18:0
  ── Error (test-TOP-LEVEL-consistency.R:21:5): check consistency against Tim's code for dyad, node, double standard and block sampling ──
  Error: length(dimLabels) == 1 is not TRUE
  Backtrace:
       █
    1. └─missSBM::estimateMissSBM(...) test-TOP-LEVEL-consistency.R:21:4
    2.   └─missSBM_collection$new(...)
    3.     └─missSBM:::initialize(...)
    4.       └─parallel::mcmapply(...)
    5.         └─base::.mapply(FUN, dots, MoreArgs)
    6.           └─(function (nBlock, cl0) ...
    7.             └─missSBM_fit$new(partlyObservedNet, nBlock, sampling, cl0, useCov)
    8.               └─missSBM:::initialize(...)
    9.                 └─SimpleSBM_fit_missSBM$new(private$imputedNet, clusterInit, covariates)
   10.                   └─missSBM:::initialize(...)
   11.                     └─super$initialize(...)
   12.                       └─super$initialize(...)
   13.                         └─base::stopifnot(length(dimLabels) == 1)
  ── Error (test-TOP-LEVEL-consistency.R:94:5): check consistency against Tim's code for dyad and node sampling with covariates ──
  Error: length(dimLabels) == 1 is not TRUE
  Backtrace:
       █
    1. └─missSBM::estimateMissSBM(...) test-TOP-LEVEL-consistency.R:94:4
    2.   └─missSBM_collection$new(...)
    3.     └─missSBM:::initialize(...)
    4.       └─parallel::mcmapply(...)
    5.         └─base::.mapply(FUN, dots, MoreArgs)
    6.           └─(function (nBlock, cl0) ...
    7.             └─missSBM_fit$new(partlyObservedNet, nBlock, sampling, cl0, useCov)
    8.               └─missSBM:::initialize(...)
    9.                 └─SimpleSBM_fit_missSBM$new(private$imputedNet, clusterInit, covariates)
   10.                   └─missSBM:::initialize(...)
   11.                     └─super$initialize(...)
   12.                       └─super$initialize(...)
   13.                         └─base::stopifnot(length(dimLabels) == 1)
  ── Error (test-TOP-LEVEL-estimate.R:16:1): (code run outside of `test_that()`) ──
  Error: only matrix diagonals can be replaced
  Backtrace:
      █
   1. └─base::`diag<-`(`*tmp*`, value = 0) test-TOP-LEVEL-estimate.R:16:0
  ── Error (test-TOP-LEVEL-sampleSBM.R:27:3): Consistency of dyad-centered sampling ──
  Error: no applicable method for 'isSymmetric' applied to an object of class "c('double', 'numeric')"
  Backtrace:
      █
   1. └─missSBM::observeNetwork(sbm$netMatrix, "dyad", 0.1) test-TOP-LEVEL-sampleSBM.R:27:2
   2.   └─base::isSymmetric(adjacencyMatrix)
  ── Error (test-TOP-LEVEL-sampleSBM.R:61:3): Consistency of node-centered network sampling ──
  Error: no applicable method for 'isSymmetric' applied to an object of class "c('double', 'numeric')"
  Backtrace:
      █
   1. └─missSBM::observeNetwork(sbm$netMatrix, "node", 0.1) test-TOP-LEVEL-sampleSBM.R:61:2
   2.   └─base::isSymmetric(adjacencyMatrix)
  ── Error (test-TOP-LEVEL-sampleSBM.R:92:3): Consistency of block-node network sampling ──
  Error: no applicable method for 'isSymmetric' applied to an object of class "c('double', 'numeric')"
  Backtrace:
      █
   1. └─missSBM::observeNetwork(...) test-TOP-LEVEL-sampleSBM.R:92:2
   2.   └─base::isSymmetric(adjacencyMatrix)
  ── Error (test-TOP-LEVEL-sampleSBM.R:110:3): Consistency of block-node network sampling ──
  Error: no applicable method for 'isSymmetric' applied to an object of class "c('double', 'numeric')"
  Backtrace:
      █
   1. └─missSBM::observeNetwork(...) test-TOP-LEVEL-sampleSBM.R:110:2
   2.   └─base::isSymmetric(adjacencyMatrix)
  ── Error (test-TOP-LEVEL-sampleSBM.R:129:3): Consistency of double-standard sampling ──
  Error: no applicable method for 'isSymmetric' applied to an object of class "c('double', 'numeric')"
  Backtrace:
      █
   1. └─missSBM::observeNetwork(...) test-TOP-LEVEL-sampleSBM.R:129:2
   2.   └─base::isSymmetric(adjacencyMatrix)
  ── Error (test-TOP-LEVEL-sampleSBM.R:147:3): Consistency of degree network sampling ──
  Error: no applicable method for 'isSymmetric' applied to an object of class "c('double', 'numeric')"
  Backtrace:
      █
   1. └─missSBM::observeNetwork(sbm$netMatrix, "degree", c(0.01, 0.01)) test-TOP-LEVEL-sampleSBM.R:147:2
   2.   └─base::isSymmetric(adjacencyMatrix)
  ── Error (test-consistency-on-fully-observed-network.R:27:3): SimpleSBM_fit_missSBM and missSBMfit are coherent ──
  Error: length(dimLabels) == 1 is not TRUE
  Backtrace:
      █
   1. └─missSBM:::SimpleSBM_fit_missSBM$new(adjacencyMatrix = A, clusterInit = cl0) test-consistency-on-fully-observed-network.R:27:2
   2.   └─missSBM:::initialize(...)
   3.     └─super$initialize(...)
   4.       └─super$initialize(...)
   5.         └─base::stopifnot(length(dimLabels) == 1)
  
  [ FAIL 27 | WARN 0 | SKIP 0 | PASS 40 ]
  Error: Test failures
  Execution halted