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###
### Running command:
###
###   F:\biocbuild\bbs-3.20-bioc\R\bin\R.exe CMD check --no-multiarch --install=check:treeclimbR.install-out.txt --library=F:\biocbuild\bbs-3.20-bioc\R\library --no-vignettes --timings treeclimbR_1.1.0.tar.gz
###
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* using log directory 'F:/biocbuild/bbs-3.20-bioc/meat/treeclimbR.Rcheck'
* using R version 4.4.0 RC (2024-04-16 r86468 ucrt)
* using platform: x86_64-w64-mingw32
* R was compiled by
    gcc.exe (GCC) 13.2.0
    GNU Fortran (GCC) 13.2.0
* running under: Windows Server 2022 x64 (build 20348)
* using session charset: UTF-8
* using option '--no-vignettes'
* checking for file 'treeclimbR/DESCRIPTION' ... OK
* checking extension type ... Package
* this is package 'treeclimbR' version '1.1.0'
* 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 'treeclimbR' 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 code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* 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 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 ... 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 files in 'vignettes' ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                  user system elapsed
diffcyt_workflow 14.46   0.22   14.71
TreeHeatmap       5.54   0.14    5.69
* checking for unstated dependencies in 'tests' ... OK
* checking tests ...
  Running 'testthat.R'
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: OK

##############################################################################
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###
### Running command:
###
###   F:\biocbuild\bbs-3.20-bioc\R\bin\R.exe CMD INSTALL treeclimbR
###
##############################################################################
##############################################################################


* installing to library 'F:/biocbuild/bbs-3.20-bioc/R/library'
* installing *source* package 'treeclimbR' ...
** using staged installation
** R
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (treeclimbR)

R version 4.4.0 RC (2024-04-16 r86468 ucrt) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
> # * https://testthat.r-lib.org/articles/special-files.html
> 
> library(testthat)
> library(treeclimbR)
> 
> test_check("treeclimbR")
Loading required package: SingleCellExperiment
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats

Attaching package: 'MatrixGenerics'

The following objects are masked from 'package:matrixStats':

    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
    colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
    colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
    colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
    colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
    colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
    colWeightedMeans, colWeightedMedians, colWeightedSds,
    colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
    rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
    rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
    rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
    rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
    rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
    rowWeightedSds, rowWeightedVars

Loading required package: GenomicRanges
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Loading required package: BiocGenerics

Attaching package: 'BiocGenerics'

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    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, rank, rbind, rownames, sapply, setdiff, table, tapply,
    union, unique, unsplit, which.max, which.min

Loading required package: S4Vectors

Attaching package: 'S4Vectors'

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Loading required package: IRanges

Attaching package: 'IRanges'

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Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: 'Biobase'

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Loading required package: Biostrings
Loading required package: XVector

Attaching package: 'Biostrings'

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Attaching package: 'TreeSummarizedExperiment'

The following object is masked from 'package:treeclimbR':

    findChild

ggtree v3.13.0 For help: https://yulab-smu.top/treedata-book/

If you use the ggtree package suite in published research, please cite
the appropriate paper(s):

Guangchuang Yu, David Smith, Huachen Zhu, Yi Guan, Tommy Tsan-Yuk Lam.
ggtree: an R package for visualization and annotation of phylogenetic
trees with their covariates and other associated data. Methods in
Ecology and Evolution. 2017, 8(1):28-36. doi:10.1111/2041-210X.12628

Guangchuang Yu.  Data Integration, Manipulation and Visualization of
Phylogenetic Trees (1st edition). Chapman and Hall/CRC. 2022,
doi:10.1201/9781003279242

LG Wang, TTY Lam, S Xu, Z Dai, L Zhou, T Feng, P Guo, CW Dunn, BR
Jones, T Bradley, H Zhu, Y Guan, Y Jiang, G Yu. treeio: an R package
for phylogenetic tree input and output with richly annotated and
associated data. Molecular Biology and Evolution. 2020, 37(2):599-603.
doi: 10.1093/molbev/msz240

Attaching package: 'ggtree'

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    collapse

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Adding another scale for fill, which will replace the existing scale.

Attaching package: 'ape'

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unwrap data ... 
Finding the pseudo-leaf level for all features ...
1 out of 2 features finished
2 out of 2 features finished
Calculating the number of pseudo-leaves of each nodefor all features ...
1 out of 2 features finished
2 out of 2 features finished
Evaluating candidates ... 
Working on 1 out of 24 candidates 
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Working on 23 out of 24 candidates 
Working on 24 out of 24 candidates 
Multiple-hypothesis correction on the best candidate ...
output the results ...
Searching candidates on t = 0.01 ...
Searching candidates on t = 0.05 ...
Searching candidates on t = 0.1 ...
Searching candidates on t = 0.25 ...
Searching candidates on t = 0.75 ...
Loading required package: viridisLite

Attaching package: 'viridis'

The following object is masked from 'package:scales':

    viridis_pal


Attaching package: 'dplyr'

The following object is masked from 'package:ape':

    where

The following objects are masked from 'package:Biostrings':

    collapse, intersect, setdiff, setequal, union

The following object is masked from 'package:XVector':

    slice

The following object is masked from 'package:Biobase':

    combine

The following objects are masked from 'package:GenomicRanges':

    intersect, setdiff, union

The following object is masked from 'package:GenomeInfoDb':

    intersect

The following objects are masked from 'package:IRanges':

    collapse, desc, intersect, setdiff, slice, union

The following objects are masked from 'package:S4Vectors':

    first, intersect, rename, setdiff, setequal, union

The following objects are masked from 'package:BiocGenerics':

    combine, intersect, setdiff, union

The following object is masked from 'package:matrixStats':

    count

The following object is masked from 'package:testthat':

    matches

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Preparing data ... 
Dropping nodes ... 
Preparing data ... 
Dropping nodes ... 
0 nodes are dropped...
0 nodes with missing score are dropped...
Searching candidate nodes... 
Searching the descendant nodes of the candidate nodes... 
Comparing nodes... 

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  |======================================================================| 100%Preparing data ... 
Dropping nodes ... 
0 nodes are dropped...
0 nodes with missing score are dropped...
Searching candidate nodes... 
Searching the descendant nodes of the candidate nodes... 
Comparing nodes... 

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  |======================================================================| 100%0 nodes are ignored, as they don't contain at least 5 cells in at least half of the samples.
Iteration 1: Log-likelihood value: -3707.79061811114
Iteration 2: Log-likelihood value: -3705.14216240896
Iteration 3: Log-likelihood value: -3704.92754722943
Iteration 4: Log-likelihood value: -3704.92506122156
Iteration 5: Log-likelihood value: -3704.92506080307
Iteration 1: Log-likelihood value: -3707.79061811114
Iteration 2: Log-likelihood value: -3705.14216240896
Iteration 3: Log-likelihood value: -3704.92754722943
Iteration 4: Log-likelihood value: -3704.92506122156
Iteration 5: Log-likelihood value: -3704.92506080307
Iteration 1: Log-likelihood value: -10012.1133395212
Iteration 2: Log-likelihood value: -10010.9880336886
Iteration 3: Log-likelihood value: -10010.8649601005
Iteration 4: Log-likelihood value: -10010.8631490746
Iteration 5: Log-likelihood value: -10010.8631484303
0 nodes are ignored, as they don't contain at least 10 cells in at least half of the samples.
1 out of 19 nodes finished
0 nodes are ignored, as they don't contain at least 10 cells in at least half of the samples.
1 out of 19 nodes finished
0 nodes are ignored, as they don't contain at least 5 cells in at least half of the samples.
0 nodes are ignored, as they don't contain at least 5 cells in at least half of the samples.
3 nodes are ignored, as they don't contain at least 13 cells in at least half of the samples.
0 nodes are ignored, as they don't contain at least 1 cells in at least half of the samples.
0 nodes are ignored, as they don't contain at least 1 cells in at least half of the samples.
Iteration 1: Log-likelihood value: -633.210632859034
Iteration 2: Log-likelihood value: -631.489192200762
Iteration 3: Log-likelihood value: -631.155358434505
Iteration 4: Log-likelihood value: -631.132407245137
Iteration 5: Log-likelihood value: -631.132252461193
Iteration 6: Log-likelihood value: -631.132252452367
Iteration 1: Log-likelihood value: -633.210632859034
Iteration 2: Log-likelihood value: -631.489192200762
Iteration 3: Log-likelihood value: -631.155358434505
Iteration 4: Log-likelihood value: -631.132407245137
Iteration 5: Log-likelihood value: -631.132252461193
Iteration 6: Log-likelihood value: -631.132252452367
Iteration 1: Log-likelihood value: -633.210632859034
Iteration 2: Log-likelihood value: -631.489192200762
Iteration 3: Log-likelihood value: -631.155358434505
Iteration 4: Log-likelihood value: -631.132407245137
Iteration 5: Log-likelihood value: -631.132252461193
Iteration 6: Log-likelihood value: -631.132252452367
Iteration 1: Log-likelihood value: -633.210632859034
Iteration 2: Log-likelihood value: -631.489192200762
Iteration 3: Log-likelihood value: -631.155358434505
Iteration 4: Log-likelihood value: -631.132407245137
Iteration 5: Log-likelihood value: -631.132252461193
Iteration 6: Log-likelihood value: -631.132252452367
Iteration 1: Log-likelihood value: -633.210632859034
Iteration 2: Log-likelihood value: -631.489192200762
Iteration 3: Log-likelihood value: -631.155358434505
Iteration 4: Log-likelihood value: -631.132407245137
Iteration 5: Log-likelihood value: -631.132252461193
Iteration 6: Log-likelihood value: -631.132252452367
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
Iteration 1: Log-likelihood value: -2142.56163506266
Iteration 2: Log-likelihood value: -2140.22864492689
Iteration 3: Log-likelihood value: -2139.95865725141
Iteration 4: Log-likelihood value: -2139.95301764506
Iteration 5: Log-likelihood value: -2139.95301439773
[ FAIL 0 | WARN 0 | SKIP 0 | PASS 2085 ]
> 
> proc.time()
   user  system elapsed 
 103.07    4.87  108.17