The NPLStoolbox allows researchers to use the N-way
Partial Least Squares method for their multi-way data.
ncrossreg() allows the user to identify the appropriate
number of NPLS 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 transgender persons starting gender-affirming hormone therapy,
containing longitudinally measured tongue microbiome, salivary
microbiome, salivary cytokine, salivary biochemistry, and circulatory
hormone levels (doi TBD).Jakobsen2025: an observational cohort of 169
mother-infant dyads investigating the effect of maternal obesity on
human milk and the infant gut microbiome https://doi.org/10.21203/rs.3.rs-6244750/v1.A basic introduction to the package using the example dataset is
given in vignette("Introduction").
This vignette and all function documentation can be found here.
The NPLStoolbox package can be installed from CRAN
using:
install.packages("NPLStoolbox")You can install the development version of NPLStoolbox from GitHub with:
# install.packages("pak")
pak::pak("GRvanderPloeg/NPLStoolbox")library(parafac4microbiome)
library(NPLStoolbox)
set.seed(123)
# Process one of the data cubes from Cornejo2025
processedTongue = processDataCube(Cornejo2025$Tongue_microbiome, sparsityThreshold=0.5, considerGroups=TRUE, groupVariable="GenderID", centerMode=1, scaleMode=2)
# Prepare Y: binarized gender identity
Y = as.numeric(as.factor(Cornejo2025$Tongue_microbiome$mode1$GenderID))
Ycnt = Y - mean(Y)
# Make a one-component NPLS model
model = triPLS1(processedTongue$data, Ycnt, 1)If you encounter an unexpected error or a clear bug, please file an issue with a minimal reproducible example here on Github. For questions or other types of feedback, feel free to send an email.