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### Running command:
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###   /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_0.99.14.tar.gz
###
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* using log directory ‘/Users/biocbuild/bbs-3.22-bioc/meat/DNEA.Rcheck’
* using R version 4.5.1 (2025-06-13)
* using platform: x86_64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 14.2.0
* running under: macOS Monterey 12.7.6
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘DNEA/DESCRIPTION’ ... OK
* this is package ‘DNEA’ version ‘0.99.14’
* 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 ... NOTE
Problems with news in ‘NEWS.md’:
  Cannot extract version info from the following section titles:
    DNEA v.0.99.12
* 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 25.181  6.077  12.332
BICtune-methods    23.280  2.942  13.735
* 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: 2 NOTEs
See
  ‘/Users/biocbuild/bbs-3.22-bioc/meat/DNEA.Rcheck/00check.log’
for details.

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### Running command:
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###   /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL DNEA
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* installing to library ‘/Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library’
* installing *source* package ‘DNEA’ ...
** this is package ‘DNEA’ version ‘0.99.14’
** 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)

R version 4.5.1 (2025-06-13) -- "Great Square Root"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-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    -2.833480e-15  2.560226e+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 
611.231  42.738 280.700