Multiple platform build/check report for BioC 3.22 - CHECK results for DNEA on kjohnson3

Hostname	OS	Arch (*)	R version	Installed pkgs
nebbiolo2	Linux (Ubuntu 24.04.2 LTS)	x86_64	4.5.0 (2025-04-11) -- "How About a Twenty-Six"	4810
palomino8	Windows Server 2022 Datacenter	x64	4.5.0 (2025-04-11 ucrt) -- "How About a Twenty-Six"	4548
kjohnson3	macOS 13.7.1 Ventura	arm64	4.5.0 Patched (2025-04-21 r88169) -- "How About a Twenty-Six"	4528
taishan	Linux (openEuler 24.03 LTS)	aarch64	4.5.0 (2025-04-11) -- "How About a Twenty-Six"	4493
Click on any hostname to see more info about the system (e.g. compilers) () as reported by 'uname -p', except on Windows and Mac OS X*

CHECK results for DNEA on kjohnson3

To the developers/maintainers of the DNEA package:
- Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/DNEA.git to reflect on this report. See Troubleshooting Build Report for more information.
- Use the following Renviron settings to reproduce errors and warnings.
- If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information.

raw results

Summary

Package: DNEA

Version: 0.99.12

Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:DNEA.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings DNEA_0.99.12.tar.gz

StartedAt: 2025-06-18 19:01:25 -0400 (Wed, 18 Jun 2025)

EndedAt: 2025-06-18 19:04:57 -0400 (Wed, 18 Jun 2025)

EllapsedTime: 212.6 seconds

RetCode: 0

Status: OK

CheckDir: DNEA.Rcheck

Warnings: 0

Command output

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###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:DNEA.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings DNEA_0.99.12.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.22-bioc/meat/DNEA.Rcheck’
* using R version 4.5.0 Patched (2025-04-21 r88169)
* using platform: aarch64-apple-darwin20
* R was compiled by
    Apple clang version 16.0.0 (clang-1600.0.26.6)
    GNU Fortran (GCC) 14.2.0
* running under: macOS Ventura 13.7.1
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘DNEA/DESCRIPTION’ ... OK
* this is package ‘DNEA’ version ‘0.99.12’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... INFO
Package which this enhances but not available for checking: ‘massdataset’
* 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 for sufficient/correct file permissions ... OK
* checking whether package ‘DNEA’ 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 ... NOTE
Found the following Rd file(s) with Rd \link{} targets missing package
anchors:
  BICtune-methods.Rd: BiocParallel
  adjacencyGraph-methods.Rd: igraph
  clusterNet.Rd: igraph
  consensusClusteringResults-class.Rd: igraph
  getNetworks.Rd: BiocParallel
Please provide package anchors for all Rd \link{} targets not in the
package itself and the base packages.
* 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 files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                    user system elapsed
stabilitySelection 7.649  1.710   4.475
BICtune-methods    7.852  0.969   4.517
* 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: 1 NOTE
See
  ‘/Users/biocbuild/bbs-3.22-bioc/meat/DNEA.Rcheck/00check.log’
for details.

Installation output

DNEA.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL DNEA
###
##############################################################################
##############################################################################


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library’
* installing *source* package ‘DNEA’ ...
** this is package ‘DNEA’ version ‘0.99.12’
** using staged installation
** R
** data
** 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 (DNEA)

Tests output

DNEA.Rcheck/tests/testthat.Rout


R version 4.5.0 Patched (2025-04-21 r88169) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20

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(DNEA)
> 
> test_check("DNEA")
Optimizing the lambda hyperparameter using Bayesian-Information Criterion outlined in Guo et al. (2011)
A Link to this reference can be found in the function documentation by running ?BICtune() in the console.
The log_scaled_data expression data will be used for analysis.

Provided lambda values will be used for optimization...
Optimizing the lambda hyperparameter using Bayesian-Information Criterion outlined in Guo et al. (2011)
A Link to this reference can be found in the function documentation by running ?BICtune() in the console.
The log_scaled_data expression data will be used for analysis.

Provided lambda values will be used for optimization...
The raw peak intensity data was used for aggregation
The aggregated log-scaled data is in the @assay slot
(The orginal DNEA object can be found in the @original_experiment slot)
Data has been normalized for further analysis. New data can be found in the log_scaled_data assay!
Data diagnostics was performed on log_scaled_data assay. To check a different assay, please specify the assay parameter.
Diagnostic criteria are as follows: 
DNEAinputSummary
  Number of Samples  -  322
  Number of Features  -  83
               min_eigen condition_num
all_data    9.870990e-02  7.028033e+01
DM:control  8.577548e-02  8.229973e+01
DM:case    -2.647761e-15  2.441922e+18
The log_scaled_data expression data will be used for analysis.
The log_scaled_data expression data will be used for analysis.
The log_scaled_data expression data will be used for analysis.
The log_scaled_data expression data will be used for analysis.
Optimizing the lambda hyperparameter using Bayesian-Information Criterion outlined in Guo et al. (2011)
A Link to this reference can be found in the function documentation by running ?BICtune() in the console
NOTE: if your dataset contains fewer than ~500 samples per experimental condition, consider setting  "aprox=TRUE". This will provide more reliable results

TUNING LAMBDA FOR DM:control!:
--------------------------------------------------

Estimating optimal c constant range for asymptotic lambda...
Fine-tuning Lambda...
The optimal Lambda hyper-parameter has been set to: 0.0199548849358947!

TUNING LAMBDA FOR DM:case!:
--------------------------------------------------

Estimating optimal c constant range for asymptotic lambda...
Fine-tuning Lambda...
The optimal Lambda hyper-parameter has been set to: 0.0495095483188967!
selection_probabilites from stability selection will be used in glasso model!

Estimating model for DM:control...using 0.0199548849358947 for lambda...
model estimated!

Estimating model for DM:case...using 0.0495095483188967 for lambda...
model estimated!

DM:control network specific edges: 463
DM:case network specific edges: 423
-----------------------------------
Number of edges shared by both networks: 434
Total number of edges in dataset: 1320
The log_scaled_data expression data will be used for analysis.
Optimizing the lambda hyperparameter using Bayesian-Information Criterion outlined in Guo et al. (2011)
A Link to this reference can be found in the function documentation by running ?BICtune() in the console
NOTE: if your dataset contains fewer than ~500 samples per experimental condition, consider setting  "aprox=TRUE". This will provide more reliable results

TUNING LAMBDA FOR DM:control!:
--------------------------------------------------

Estimating optimal c constant range for asymptotic lambda...
Fine-tuning Lambda...
The optimal Lambda hyper-parameter has been set to: 0.0199548849358947!

TUNING LAMBDA FOR DM:case!:
--------------------------------------------------

Estimating optimal c constant range for asymptotic lambda...
Fine-tuning Lambda...
The optimal Lambda hyper-parameter has been set to: 0.0495095483188967!
selection_probabilites from stability selection will be used in glasso model!

Estimating model for DM:control...using 0.0199548849358947 for lambda...
model estimated!

Estimating model for DM:case...using 0.0495095483188967 for lambda...
model estimated!

DM:control network specific edges: 463
DM:case network specific edges: 423
-----------------------------------
Number of edges shared by both networks: 434
Total number of edges in dataset: 1320
The log_input_data expression data will be used for analysis.
The log_input_data expression data will be used for analysis.
Vectors to compare are not the same length!
Vectors to compare are not the same length!
DM:control network specific edges: 11
DM:case network specific edges: 7
-----------------------------------
Number of edges shared by both networks: 10
Total number of edges in dataset: 28
DM:control network specific edges: 11
DM:case network specific edges: 7
-----------------------------------
Number of edges shared by both networks: 10
Total number of edges in dataset: 28
[ FAIL 0 | WARN 1 | SKIP 0 | PASS 53 ]

[ FAIL 0 | WARN 1 | SKIP 0 | PASS 53 ]
> 
> proc.time()
   user  system elapsed 
220.199   9.645 106.868

Example timings

DNEA.Rcheck/DNEA-Ex.timings

name	user	system	elapsed
BICscores-methods	0.081	0.009	0.092
BICtune-methods	7.852	0.969	4.517
CCsummary-methods	0.078	0.006	0.084
DNEA-class	0.258	0.027	0.285
DNEAinputSummary-class	0.256	0.017	0.274
addExpressionData	0.281	0.017	0.298
adjacencyGraph-methods	0.082	0.003	0.085
adjacencyMatrix-methods	0.103	0.063	0.166
aggregateFeatures	0.669	0.020	0.696
clusterNet	1.208	0.021	1.230
collapsed_DNEA-class	0.657	0.019	0.677
createDNEAobject	0.267	0.007	0.276
datasetSummary-methods	0.257	0.008	0.267
diagnostics-methods	0.263	0.007	0.271
edgeList-methods	0.077	0.003	0.080
expressionData-methods	0.365	0.240	0.609
featureNames-methods	0.263	0.005	0.267
filterNetworks-methods	0.096	0.003	0.099
getNetworkFiles	0.079	0.003	0.083
getNetworks	0.165	0.005	0.170
includeMetadata	0.259	0.005	0.265
lambdas2Test-methods	0.566	0.060	0.626
massDataset2DNEA	0.027	0.003	0.030
metaData-methods	0.259	0.007	0.266
netGSAresults-methods	0.078	0.003	0.081
networkGroupIDs-methods	0.254	0.009	0.263
networkGroups-methods	0.258	0.006	0.264
nodeList-methods	0.254	0.008	0.263
numFeatures-methods	0.258	0.007	0.265
numSamples-methods	0.259	0.003	0.263
optimizedLambda-methods	0.267	0.008	0.273
plotNetworks	0.109	0.003	0.112
projectName-methods	0.258	0.004	0.261
runNetGSA	0.841	0.075	0.917
sampleNames-methods	0.263	0.006	0.270
selectionProbabilities-methods	0.116	0.073	0.189
selectionResults-methods	0.108	0.047	0.156
stabilitySelection	7.649	1.710	4.475
subnetworkMembership-methods	0.085	0.011	0.096
sumExp2DNEA	1.112	0.084	1.199