CRAN Package Check Results for Package missSBM

Last updated on 2020-03-28 21:47:51 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.2.1 OK
r-devel-linux-x86_64-debian-gcc 0.2.1 49.61 112.17 161.78 OK
r-devel-linux-x86_64-fedora-clang 0.2.1 288.76 NOTE
r-devel-linux-x86_64-fedora-gcc 0.2.1 262.48 NOTE
r-devel-windows-ix86+x86_64 0.2.1 103.00 262.00 365.00 OK
r-devel-windows-ix86+x86_64-gcc8 0.2.1 105.00 312.00 417.00 OK
r-patched-linux-x86_64 0.2.1 54.29 147.15 201.44 OK
r-patched-solaris-x86 0.2.1 374.90 OK
r-release-linux-x86_64 0.2.1 56.88 146.41 203.29 OK
r-release-windows-ix86+x86_64 0.2.1 177.00 359.00 536.00 OK
r-release-osx-x86_64 0.2.1 OK
r-oldrel-windows-ix86+x86_64 0.2.1 154.00 323.00 477.00 ERROR
r-oldrel-osx-x86_64 0.2.1 ERROR

Check Details

Version: 0.2.1
Check: top-level files
Result: NOTE
    ‘configure’: /bin/bash is not portable
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc

Version: 0.2.1
Check: running tests for arch ‘i386’
Result: ERROR
     Running 'testthat.R' [62s]
    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
Flavor: r-oldrel-windows-ix86+x86_64

Version: 0.2.1
Check: running tests for arch ‘x64’
Result: ERROR
     Running 'testthat.R' [63s]
    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
Flavor: r-oldrel-windows-ix86+x86_64

Version: 0.2.1
Check: tests
Result: ERROR
     Running ‘testthat.R’ [42s/42s]
    Running the tests in ‘tests/testthat.R’ failed.
    Last 13 lines of output:
     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
Flavor: r-oldrel-osx-x86_64