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TheNPLStoolbox allows researchers to use the N-way Partial LeastSquares method for their multi-way data.

• ncrossreg() allows the user to identify the appropriate number ofNPLS components for their data.
• triPLS1() allows the user to create an NPLS model.
• npred() allows the user to predict y for new data.

This package also comes with two example datasets:

• Cornejo2025: a clinical observational cohort study of 39 transgenderpersons starting gender-affirming hormone therapy, containinglongitudinally measured tongue microbiome, salivary microbiome,salivary cytokine, salivary biochemistry, and circulatory hormonelevels (doi TBD).
• Jakobsen2025: an observational cohort of 169 mother-infant dyadsinvestigating the effect of maternal obesity on human milk and theinfant gut microbiomehttps://doi.org/10.21203/rs.3.rs-6244750/v1.

A basic introduction to the package using the example dataset is giveninvignette("Introduction").

This vignette and all function documentation can be foundhere.

TheNPLStoolbox package can be installed from CRAN using:

install.packages("NPLStoolbox")

You can install the development version of NPLStoolbox fromGitHub with:

# install.packages("pak")pak::pak("GRvanderPloeg/NPLStoolbox")

library(parafac4microbiome)library(NPLStoolbox)set.seed(123)# Process one of the data cubes from Cornejo2025processedTongue= processDataCube(Cornejo2025$Tongue_microbiome,sparsityThreshold=0.5,considerGroups=TRUE,groupVariable="GenderID",centerMode=1,scaleMode=2)# Prepare Y: binarized gender identityY= as.numeric(as.factor(Cornejo2025$Tongue_microbiome$mode1$GenderID))Ycnt=Y- mean(Y)# Make a one-component NPLS modelmodel= triPLS1(processedTongue$data,Ycnt,1)

If you encounter an unexpected error or a clear bug, please file anissue with a minimal reproducible example here onGithub. Forquestions or other types of feedback, feel free to send an email.