Package check result: ERROR

Check: CRAN incoming feasibility, Result: 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)

Check: examples, Result: 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