* using log directory 'd:/Rcompile/CRANpkg/local/3.5/missSBM.Rcheck' * using R version 3.5.3 (2019-03-11) * using platform: x86_64-w64-mingw32 (64-bit) * using session charset: ISO8859-1 * checking for file 'missSBM/DESCRIPTION' ... OK * checking extension type ... Package * this is package 'missSBM' version '0.2.1' * package encoding: UTF-8 * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking whether package 'missSBM' can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking 'build' directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking R files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * loading checks for arch 'i386' ** checking whether the package can be loaded ... OK ** checking whether the package can be loaded with stated dependencies ... OK ** checking whether the package can be unloaded cleanly ... OK ** checking whether the namespace can be loaded with stated dependencies ... OK ** checking whether the namespace can be unloaded cleanly ... OK ** checking loading without being on the library search path ... OK ** checking use of S3 registration ... OK * loading checks for arch 'x64' ** checking whether the package can be loaded ... OK ** checking whether the package can be loaded with stated dependencies ... OK ** checking whether the package can be unloaded cleanly ... OK ** checking whether the namespace can be loaded with stated dependencies ... OK ** checking whether the namespace can be unloaded cleanly ... OK ** checking loading without being on the library search path ... OK ** checking use of S3 registration ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... [7s] OK * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking contents of 'data' directory ... OK * checking data for non-ASCII characters ... OK * checking data for ASCII and uncompressed saves ... OK * checking line endings in shell scripts ... OK * checking line endings in C/C++/Fortran sources/headers ... OK * checking line endings in Makefiles ... OK * checking compilation flags in Makevars ... OK * checking for GNU extensions in Makefiles ... OK * checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK * checking pragmas in C/C++ headers and code ... OK * checking compiled code ... OK * checking installed files from 'inst/doc' ... OK * checking files in 'vignettes' ... OK * checking examples ... ** running examples for arch 'i386' ... [15s] OK ** running examples for arch 'x64' ... [16s] OK * checking for unstated dependencies in 'tests' ... OK * checking tests ... ** running tests for arch 'i386' ... [62s] ERROR Running 'testthat.R' [60s] Running the tests in 'tests/testthat.R' failed. Complete output: > library(testthat) > library(missSBM) Attaching package: 'missSBM' The following objects are masked from 'package:stats': simulate, smooth The following object is masked from 'package:base': sample > > test_check("missSBM") Adjusting Variational EM for Stochastic Block Model Dyads are distributed according to a 'undirected' SBM. Imputation assumes a 'covar-dyad' network-sampling process iteration #: 1 iteration #: 2 iteration #: 3 iteration #: 4 iteration #: 5 iteration #: 6 iteration #: 7 iteration #: 8 iteration #: 9 Adjusting Variational EM for Stochastic Block Model Dyads are distributed according to a 'undirected' SBM. Imputation assumes a 'dyad' network-sampling process iteration #: 1 iteration #: 2 iteration #: 3 iteration #: 4 iteration #: 5 Adjusting Variational EM for Stochastic Block Model Dyads are distributed according to a 'undirected' SBM. Imputation assumes a 'covar-node' network-sampling process iteration #: 1 iteration #: 2 iteration #: 3 iteration #: 4 iteration #: 5 iteration #: 6 iteration #: 7 iteration #: 8 -- 1. Failure: miss SBM with covariates and node sampling works (@test-MISSSBM-f error(missSBM$fittedSBM$covarParam, sbm$covarParam) is not strictly less than `tol_truth`. Difference: 0.000677 Adjusting Variational EM for Stochastic Block Model Dyads are distributed according to a 'undirected' SBM. Imputation assumes a 'node' network-sampling process iteration #: 1 iteration #: 2 iteration #: 3 iteration #: 4 iteration #: 5 Adjusting Variational EM for Stochastic Block Model Imputation assumes a 'dyad' network-sampling process Initialization of model with 1 blocks. Initialization of model with 2 blocks. Initialization of model with 3 blocks. Initialization of model with 4 blocks. Smoothing ICL Going forward +++ Smoothing ICL Going backward +++ Smoothing ICL Going forward +++ Going backward +++ Adjusting Variational EM for Stochastic Block Model Imputation assumes a 'dyad' network-sampling process Initialization of model with 1 blocks. Initialization of model with 2 blocks. Initialization of model with 3 blocks. Initialization of model with 4 blocks. Smoothing ICL Going forward +++ Going forward +++ Smoothing ICL Going backward +++ Going backward +++ Smoothing ICL Going forward +++ Going backward +++ Going forward +++ Going backward +++ Tested sampling: - dyad - node - double-standard - block-node - degree sampling: dyad Adjusting Variational EM for Stochastic Block Model Imputation assumes a 'dyad' network-sampling process Initialization of model with 3 blocks. Performing VEM inference for model with 3 blocks. new better on mixture node Adjusting Variational EM for Stochastic Block Model Imputation assumes a 'node' network-sampling process Initialization of model with 3 blocks. Performing VEM inference for model with 3 blocks. double-standard Adjusting Variational EM for Stochastic Block Model Imputation assumes a 'double-standard' network-sampling process Initialization of model with 3 blocks. Performing VEM inference for model with 3 blocks. new better on connectivity new better on sampling parameters block-node Adjusting Variational EM for Stochastic Block Model Imputation assumes a 'block-node' network-sampling process Initialization of model with 3 blocks. Performing VEM inference for model with 3 blocks. new better on mixture new better on connectivity new better on sampling parameters Adjusting Variational EM for Stochastic Block Model Imputation assumes a 'covar-dyad' network-sampling process Initialization of model with 2 blocks. Performing VEM inference for model with 2 blocks. new better on mixture new better on connectivity Adjusting Variational EM for Stochastic Block Model Imputation assumes a 'covar-node' network-sampling process Initialization of model with 2 blocks. Performing VEM inference for model with 2 blocks. new better on mixture new better on connectivity Sampling: dyad Sampling: node Sampling: double-standard Sampling: block-node Sampling: block-dyad Adjusting Variational EM for Stochastic Block Model iteration #: 1 iteration #: 2 iteration #: 3 iteration #: 4 iteration #: 5 iteration #: 6 iteration #: 7 iteration #: 8 iteration #: 9 iteration #: 10 iteration #: 11 iteration #: 12 iteration #: 13 iteration #: 14 iteration #: 15 iteration #: 16 iteration #: 17 Adjusting Variational EM for Stochastic Block Model Dyads are distributed according to a 'undirected' SBM. Imputation assumes a 'node' network-sampling process iteration #: 1 iteration #: 2 iteration #: 3 iteration #: 4 iteration #: 5 iteration #: 6 iteration #: 7 iteration #: 8 iteration #: 9 iteration #: 10 iteration #: 11 iteration #: 12 iteration #: 13 iteration #: 14 iteration #: 15 iteration #: 16 iteration #: 17 Adjusting Variational EM for Stochastic Block Model Imputation assumes a 'node' network-sampling process Initialization of model with 3 blocks. Performing VEM inference for model with 3 blocks. == testthat results =========================================================== [ OK: 458 | SKIPPED: 0 | WARNINGS: 0 | FAILED: 1 ] 1. Failure: miss SBM with covariates and node sampling works (@test-MISSSBM-fit-with-covariates.R#124) Error: testthat unit tests failed Execution halted ** running tests for arch 'x64' ... [64s] ERROR Running 'testthat.R' [64s] 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") Adjusting Variational EM for Stochastic Block Model Dyads are distributed according to a 'undirected' SBM. Imputation assumes a 'covar-dyad' network-sampling process iteration #: 1 iteration #: 2 iteration #: 3 iteration #: 4 iteration #: 5 iteration #: 6 iteration #: 7 iteration #: 8 iteration #: 9 Adjusting Variational EM for Stochastic Block Model Dyads are distributed according to a 'undirected' SBM. Imputation assumes a 'dyad' network-sampling process iteration #: 1 iteration #: 2 iteration #: 3 iteration #: 4 iteration #: 5 Adjusting Variational EM for Stochastic Block Model Dyads are distributed according to a 'undirected' SBM. Imputation assumes a 'covar-node' network-sampling process iteration #: 1 iteration #: 2 iteration #: 3 iteration #: 4 iteration #: 5 iteration #: 6 iteration #: 7 iteration #: 8 -- 1. Failure: miss SBM with covariates and node sampling works (@test-MISSSBM-f error(missSBM$fittedSBM$covarParam, sbm$covarParam) is not strictly less than `tol_truth`. Difference: 0.000678 Adjusting Variational EM for Stochastic Block Model Dyads are distributed according to a 'undirected' SBM. Imputation assumes a 'node' network-sampling process iteration #: 1 iteration #: 2 iteration #: 3 iteration #: 4 iteration #: 5 Adjusting Variational EM for Stochastic Block Model Imputation assumes a 'dyad' network-sampling process Initialization of model with 1 blocks. Initialization of model with 2 blocks. Initialization of model with 3 blocks. Initialization of model with 4 blocks. Smoothing ICL Going forward +++ Smoothing ICL Going backward +++ Smoothing ICL Going forward +++ Going backward +++ Adjusting Variational EM for Stochastic Block Model Imputation assumes a 'dyad' network-sampling process Initialization of model with 1 blocks. Initialization of model with 2 blocks. Initialization of model with 3 blocks. Initialization of model with 4 blocks. Smoothing ICL Going forward +++ Going forward +++ Smoothing ICL Going backward +++ Going backward +++ Smoothing ICL Going forward +++ Going backward +++ Going forward +++ Going backward +++ Tested sampling: - dyad - node - double-standard - block-node - degree sampling: dyad Adjusting Variational EM for Stochastic Block Model Imputation assumes a 'dyad' network-sampling process Initialization of model with 3 blocks. Performing VEM inference for model with 3 blocks. new better on mixture node Adjusting Variational EM for Stochastic Block Model Imputation assumes a 'node' network-sampling process Initialization of model with 3 blocks. Performing VEM inference for model with 3 blocks. double-standard Adjusting Variational EM for Stochastic Block Model Imputation assumes a 'double-standard' network-sampling process Initialization of model with 3 blocks. Performing VEM inference for model with 3 blocks. new better on connectivity new better on sampling parameters block-node Adjusting Variational EM for Stochastic Block Model Imputation assumes a 'block-node' network-sampling process Initialization of model with 3 blocks. Performing VEM inference for model with 3 blocks. new better on mixture new better on connectivity new better on sampling parameters Adjusting Variational EM for Stochastic Block Model Imputation assumes a 'covar-dyad' network-sampling process Initialization of model with 2 blocks. Performing VEM inference for model with 2 blocks. new better on mixture new better on connectivity Adjusting Variational EM for Stochastic Block Model Imputation assumes a 'covar-node' network-sampling process Initialization of model with 2 blocks. Performing VEM inference for model with 2 blocks. new better on mixture new better on connectivity Sampling: dyad Sampling: node Sampling: double-standard Sampling: block-node Sampling: block-dyad Adjusting Variational EM for Stochastic Block Model iteration #: 1 iteration #: 2 iteration #: 3 iteration #: 4 iteration #: 5 iteration #: 6 iteration #: 7 iteration #: 8 iteration #: 9 iteration #: 10 iteration #: 11 iteration #: 12 iteration #: 13 iteration #: 14 iteration #: 15 iteration #: 16 iteration #: 17 iteration #: 18 iteration #: 19 iteration #: 20 iteration #: 21 Adjusting Variational EM for Stochastic Block Model Dyads are distributed according to a 'undirected' SBM. Imputation assumes a 'node' network-sampling process iteration #: 1 iteration #: 2 iteration #: 3 iteration #: 4 iteration #: 5 iteration #: 6 iteration #: 7 iteration #: 8 iteration #: 9 iteration #: 10 iteration #: 11 iteration #: 12 iteration #: 13 iteration #: 14 iteration #: 15 iteration #: 16 iteration #: 17 iteration #: 18 iteration #: 19 iteration #: 20 iteration #: 21 Adjusting Variational EM for Stochastic Block Model Imputation assumes a 'node' network-sampling process Initialization of model with 3 blocks. Performing VEM inference for model with 3 blocks. == testthat results =========================================================== [ OK: 458 | SKIPPED: 0 | WARNINGS: 0 | FAILED: 1 ] 1. Failure: miss SBM with covariates and node sampling works (@test-MISSSBM-fit-with-covariates.R#124) Error: testthat unit tests failed Execution halted * checking for unstated dependencies in vignettes ... OK * checking package vignettes in 'inst/doc' ... OK * checking re-building of vignette outputs ... [48s] OK * checking PDF version of manual ... OK * DONE Status: 2 ERRORs