* using log directory ‘/home/hornik/tmp/CRAN_special_donttest/MSclassifR.Rcheck’
* using R Under development (unstable) (2025-12-07 r89119)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc-15 (Debian 15.2.0-9) 15.2.0
    GNU Fortran (Debian 15.2.0-9) 15.2.0
* running under: Debian GNU/Linux forky/sid
* using session charset: UTF-8
* using option ‘--run-donttest’
* checking for file ‘MSclassifR/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘MSclassifR’ version ‘0.5.0’
* package encoding: UTF-8
* checking CRAN incoming feasibility ... [5s/6s] NOTE
Maintainer: ‘Alexandre Godmer <alexandre.godmer@aphp.fr>’

Possibly misspelled words in DESCRIPTION:
  Desorption (11:199)
  MALDI (11:257, 11:623)
  Spectrometry (11:243)
  TOF (11:263, 11:629)
  spectrometry (11:787)
* checking package namespace information ... OK
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* checking whether package ‘MSclassifR’ can be installed ... [24s/24s] OK
* used C++ compiler: ‘g++-15 (Debian 15.2.0-9) 15.2.0’
* checking package directory ... OK
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* checking compiled code ... OK
* checking examples ... [8s/8s] ERROR
Running examples in ‘MSclassifR-Ex.R’ failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: PredictLogReg
> ### Title: Prediction of the category to which a mass spectrum belongs
> ### Aliases: PredictLogReg
> 
> ### ** Examples
> 
> ## No test: 
> library(MSclassifR)
> library(MALDIquant)
> 
> ## 1) Preprocess and detect peaks
> data("CitrobacterRKIspectra", "CitrobacterRKImetadata", package = "MSclassifR")
> spectra <- SignalProcessing(CitrobacterRKIspectra)
[SignalProcessingUltra] N=14; workers=1; align=lowess; OS=unix
1) Transform: sqrt
2) Smooth: Wavelet
3) Baseline: SNIP
4) Calibrate: TIC
5) Building reference peaks (minFrequency=0.5, method=strict)
6) Align: lowess
> peaks   <- MSclassifR::PeakDetection(x = spectra, averageMassSpec = FALSE)
Detecting peaks (method=MAD, SNR=3) [serial]
Aligning peaks in discrete bins
> 
> ## 2) Build X and Y (sample-by-peak intensities + labels)
> ##    Option A: if you prefer the helper and a sparse return:
> Y <- factor(CitrobacterRKImetadata$Species)
> xy <- build_XY_from_peaks(peaks, labels = Y, normalize = "max", sparse = FALSE)
> X <- xy$X
> Y <- xy$Y
> 
> ##    Option B: via MALDIquant::intensityMatrix (as in the original examples)
> ##IntMat <- MALDIquant::intensityMatrix(peaks)
> ##rownames(IntMat) <- paste(CitrobacterRKImetadata$Strain_name_spot)
> ##IntMat[is.na(IntMat)] <- 0
> ##IntMat <- t(apply(IntMat, 1, function(x) x / max(x)))  # per-spectrum max norm
> ##X <- t(IntMat)                                         # features in columns
> ##Y <- factor(CitrobacterRKImetadata$Species)
> 
> ## 3) Select discriminant m/z with "cvp" method
> a <- MSclassifR::SelectionVar(
+   X, Y,
+   MethodSelection = "cvp",
+   MethodValidation = "cv",
+   PreProcessing = c("center","scale","nzv","corr"),
+   NumberCV = 2,
+   Metric = "Kappa"
+ )
No sampling method selected
Selection variables with cvp method
> sel_moz <- a$sel_moz
> 
> ## 4) Train several models on the shortlisted m/z
> model_lm  <- MSclassifR::LogReg(X = X, moz = sel_moz, Y = Y, number = 2,
+  repeats = 2, Metric = "Kappa", kind = "linear")
LogReg function according to the following parameters:
No sampling method selected
Error in .check_ncores(length(names)) : 31 simultaneous processes spawned
Calls: <Anonymous> -> <Anonymous> -> makePSOCKcluster -> .check_ncores
Execution halted
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* DONE
Status: 1 ERROR, 1 NOTE