CRAN Package Check Results for Maintainer ‘Alexis Sarda <alexis.sarda at gmail.com>’

Last updated on 2018-09-26 01:46:57 CEST.

Package ERROR NOTE
dtwclust 1 11

Package dtwclust

Current CRAN status: ERROR: 1, NOTE: 11

Additional issues

clang-UBSAN gcc-UBSAN

Version: 5.5.0
Check: whether the namespace can be unloaded cleanly
Result: WARN
    ---- unloading
    Error in .mergeMethodsTable(generic, mtable, tt, attach) :
     trying to get slot "defined" from an object of a basic class ("environment") with no slots
    Calls: unloadNamespace ... <Anonymous> -> .updateMethodsInTable -> .mergeMethodsTable
    Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 5.5.0
Check: for GNU extensions in Makefiles
Result: NOTE
    GNU make is a SystemRequirements.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-windows-ix86+x86_64, r-release-linux-x86_64, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64

Version: 5.5.0
Check: examples
Result: ERROR
    Running examples in ‘dtwclust-Ex.R’ failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: tsclust
    > ### Title: Time series clustering
    > ### Aliases: tsclust
    >
    > ### ** Examples
    >
    > #' NOTE: More examples are available in the vignette. Here are just some miscellaneous
    > #' examples that might come in handy. They should all work, but some don't run
    > #' automatically.
    >
    > # Load data
    > data(uciCT)
    >
    > # ====================================================================================
    > # Simple partitional clustering with Euclidean distance and PAM centroids
    > # ====================================================================================
    >
    > # Reinterpolate to same length
    > series <- reinterpolate(CharTraj, new.length = max(lengths(CharTraj)))
    >
    > # Subset for speed
    > series <- series[1:20]
    > labels <- CharTrajLabels[1:20]
    >
    > # Making many repetitions
    > pc.l2 <- tsclust(series, k = 4L,
    + distance = "L2", centroid = "pam",
    + seed = 3247, trace = TRUE,
    + control = partitional_control(nrep = 10L))
    
     Precomputing distance matrix...
    
    Repetition 1 for k = 4
    Iteration 1: Changes / Distsum = 20 / 106.6446
    Iteration 2: Changes / Distsum = 2 / 53.2297
    Iteration 3: Changes / Distsum = 0 / 49.6027
    
    Repetition 2 for k = 4
    Iteration 1: Changes / Distsum = 20 / 130.5215
    Iteration 2: Changes / Distsum = 0 / 116.7966
    
    Repetition 3 for k = 4
    Iteration 1: Changes / Distsum = 20 / 106.2209
    Iteration 2: Changes / Distsum = 0 / 99.09456
    
    Repetition 4 for k = 4
    Iteration 1: Changes / Distsum = 20 / 82.09483
    Iteration 2: Changes / Distsum = 1 / 68.2079
    Iteration 3: Changes / Distsum = 0 / 68.2079
    
    Repetition 5 for k = 4
    Iteration 1: Changes / Distsum = 20 / 84.34789
    Iteration 2: Changes / Distsum = 2 / 50.56039
    Iteration 3: Changes / Distsum = 0 / 49.6027
    
    Repetition 6 for k = 4
    Iteration 1: Changes / Distsum = 20 / 76.41461
    Iteration 2: Changes / Distsum = 2 / 49.6027
    Iteration 3: Changes / Distsum = 0 / 49.6027
    
    Repetition 7 for k = 4
    Iteration 1: Changes / Distsum = 20 / 78.83074
    Iteration 2: Changes / Distsum = 2 / 49.6027
    Iteration 3: Changes / Distsum = 0 / 49.6027
    
    Repetition 8 for k = 4
    Iteration 1: Changes / Distsum = 20 / 74.16051
    Iteration 2: Changes / Distsum = 1 / 49.6027
    Iteration 3: Changes / Distsum = 0 / 49.6027
    
    Repetition 9 for k = 4
    Iteration 1: Changes / Distsum = 20 / 77.99961
    Iteration 2: Changes / Distsum = 2 / 50.56039
    Iteration 3: Changes / Distsum = 0 / 49.6027
    
    Repetition 10 for k = 4
    Iteration 1: Changes / Distsum = 20 / 101.6274
    Iteration 2: Changes / Distsum = 0 / 101.1829
    
     Elapsed time is 0.187 seconds.
    
    >
    > # Cluster validity indices
    > sapply(pc.l2, cvi, b = labels)
     [,1] [,2] [,3] [,4] [,5]
    ARI 1.000000e+00 0.2002053388 6.149546e-01 6.149546e-01 1.000000e+00
    RI 1.000000e+00 0.5684210526 8.473684e-01 8.473684e-01 1.000000e+00
    J 1.000000e+00 0.2869565217 5.538462e-01 5.538462e-01 1.000000e+00
    FM 1.000000e+00 0.5020790110 7.287987e-01 7.287987e-01 1.000000e+00
    VI 0.000000e+00 0.4610152539 2.048455e-01 2.048455e-01 0.000000e+00
    Sil 6.331221e-01 0.3119630506 3.149087e-01 5.346690e-01 6.331221e-01
    SF 4.606696e-04 0.0003004822 1.752593e-05 3.238172e-04 4.606696e-04
    CH 1.925006e+01 3.1947649639 5.413882e+00 1.258269e+01 1.925006e+01
    DB 4.615583e-01 0.5873421456 1.130403e+00 5.573207e-01 4.615583e-01
    DBstar 6.271176e-01 1.7248522405 1.580351e+00 8.294109e-01 6.271176e-01
    D 7.808069e-01 0.1689797902 2.655638e-01 5.926710e-01 7.808069e-01
    COP 1.207865e-01 0.4285157845 3.098501e-01 1.886585e-01 1.207865e-01
     [,6] [,7] [,8] [,9] [,10]
    ARI 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 5.819730e-01
    RI 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 8.368421e-01
    J 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 5.230769e-01
    FM 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 6.998789e-01
    VI 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 2.235863e-01
    Sil 6.331221e-01 6.331221e-01 6.331221e-01 6.331221e-01 2.417737e-01
    SF 4.606696e-04 4.606696e-04 4.606696e-04 4.606696e-04 4.195698e-06
    CH 1.925006e+01 1.925006e+01 1.925006e+01 1.925006e+01 5.270039e+00
    DB 4.615583e-01 4.615583e-01 4.615583e-01 4.615583e-01 1.321109e+00
    DBstar 6.271176e-01 6.271176e-01 6.271176e-01 6.271176e-01 2.252835e+00
    D 7.808069e-01 7.808069e-01 7.808069e-01 7.808069e-01 1.623149e-01
    COP 1.207865e-01 1.207865e-01 1.207865e-01 1.207865e-01 3.113853e-01
    >
    > # ====================================================================================
    > # Hierarchical clustering with Euclidean distance
    > # ====================================================================================
    >
    > # Re-use the distance matrix from the previous example (all matrices are the same)
    > # Use all available linkage methods for function hclust
    > hc.l2 <- tsclust(series, type = "hierarchical",
    + k = 4L, trace = TRUE,
    + control = hierarchical_control(method = "all",
    + distmat = pc.l2[[1L]]@distmat))
    
    Distance matrix provided...
    Performing hierarchical clustering...
    Extracting centroids...
    
    Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
    Also defined by ‘dtwclust’
    Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
    Also defined by ‘dtwclust’
    Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
    Also defined by ‘dtwclust’
    Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
    Also defined by ‘dtwclust’
    Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
    Also defined by ‘dtwclust’
    Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
    Also defined by ‘dtwclust’
    Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
    Also defined by ‘dtwclust’
    Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
    Also defined by ‘dtwclust’
    Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
    Also defined by ‘dtwclust’
    Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
    Also defined by ‘dtwclust’
    Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
    Also defined by ‘dtwclust’
    Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
    Also defined by ‘dtwclust’
    Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
    Also defined by ‘dtwclust’
    Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
    Also defined by ‘dtwclust’
    Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
    Also defined by ‘dtwclust’
    Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
    Also defined by ‘dtwclust’
    Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
    Also defined by ‘dtwclust’
     Elapsed time is 0.21 seconds.
    
    >
    > # Plot the best dendrogram according to variation of information
    > plot(hc.l2[[which.min(sapply(hc.l2, cvi, b = labels, type = "VI"))]])
    Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
    Also defined by ‘dtwclust’
    Error in plot.hclust(x, ...) : invalid dendrogram
    Calls: plot -> plot -> .local -> <Anonymous> -> plot.hclust
    Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 5.5.0
Check: tests
Result: ERROR
     Running ‘testthat.R’ [171s/123s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(dtwclust)
     Loading required package: proxy
    
     Attaching package: 'proxy'
    
     The following objects are masked from 'package:stats':
    
     as.dist, dist
    
     The following object is masked from 'package:base':
    
     as.matrix
    
     Loading required package: dtw
     Loaded dtw v1.20-1. See ?dtw for help, citation("dtw") for use in publication.
    
     dtwclust:
     Setting random number generator to L'Ecuyer-CMRG (see RNGkind()).
     To read the included vignettes type: browseVignettes("dtwclust").
     See news(package = "dtwclust") after package updates.
     > library(foreach)
     > library(testthat)
     >
     > # coverage for multi-threading might not be possible (?)
     > if (nzchar(Sys.getenv("R_COVR"))) RcppParallel::setThreadOptions(1L)
     > # old reporter for CMD checks
     > options(testthat.default_reporter = "summary")
     >
     > #' To test in a local machine:
     > #' Sys.setenv(NOT_CRAN = "true"); test_dir("tests/testthat/")
     > #' OR
     > #' devtools::test() # broken, can't figure out why
     > #'
     > #' To disable parallel tests, before calling test() run:
     > #'
     > #' options(dtwclust_skip_par_tests = TRUE)
     > #'
     > testthat::test_check("dtwclust")
     ── 1. Error: Methods for TSClusters objects are dispatched correctly. ─────────
     invalid dendrogram
     1: expect_silent(plot(hierarchical_object, type = "dendrogram"))
     2: quasi_capture(enquo(object), evaluate_promise)
     3: capture(act$val <- eval_bare(get_expr(quo), get_env(quo)))
     4: withr::with_output_sink(temp, withCallingHandlers(withVisible(code), warning = handle_warning,
     message = handle_message))
     5: force(code)
     6: withCallingHandlers(withVisible(code), warning = handle_warning, message = handle_message)
     7: withVisible(code)
     8: eval_bare(get_expr(quo), get_env(quo))
     9: plot(hierarchical_object, type = "dendrogram")
     10: plot(hierarchical_object, type = "dendrogram")
     11: .local(x, y, ...)
     12: graphics::plot(x, ...)
     13: plot.hclust(x, ...)
     14: stop(msg)
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     OK: 1886 SKIPPED: 3 FAILED: 1
     1. Error: Methods for TSClusters objects are dispatched correctly.
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 5.5.0
Check: re-building of vignette outputs
Result: WARN
    Error in re-building vignettes:
     ...
    Quitting from lines 1037-1040 (dtwclust.Rnw)
    Error: processing vignette ‘dtwclust.Rnw’ failed with diagnostics:
    invalid dendrogram
    Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 5.5.1
Check: for GNU extensions in Makefiles
Result: NOTE
    GNU make is a SystemRequirements.
Flavors: r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-linux-x86_64, r-patched-solaris-x86

Version: 5.5.1
Check: installed package size
Result: NOTE
     installed size is 9.0Mb
     sub-directories of 1Mb or more:
     doc 2.5Mb
     libs 5.3Mb
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 5.5.0
Check: installed package size
Result: NOTE
     installed size is 5.8Mb
     sub-directories of 1Mb or more:
     doc 2.5Mb
     libs 2.1Mb
Flavors: r-devel-windows-ix86+x86_64, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64