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TPLSr 1.0.3

R Package for using Thresholded Partial Least Squares (TPLS) for big data regression and classification. It is developed with whole-brain neuroimaging (fMRI) MVPA predictors in mind. TPLS uses analytical calulations of partial least squares to dramatically speed-up the training of models with large number of features (~millions).

Citation: Lee, S., Bradlow, E. T., & Kable, J. W. (2021). Thresholded Partial Least Squares: Fast Construction of Interpretable Whole-brain Decoders. BioRXiv. doi: https://doi.org/10.1101/2021.02.09.430524

CRAN: https://CRAN.R-project.org/package=TPLSr
GITHUB: https://github.com/sangillee/TPLSr

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Version

Install

install.packages('TPLSr')

Monthly Downloads

174

Version

1.0.3

License

GPL-3

Maintainer

Sangil Lee

Last Published

April 8th, 2021

Functions in TPLSr (1.0.3)

TPLSpredict

Make predictions about given data testX by using an extracted TPLSmdl with compval components and threshval threshold.
plotTuningSurface

Plots the tuning surface of TPLS
TPLS

Fit a TPLS model to data
TPLS_cv

Fit a TPLS model to data with cross validation
evalTuningParam

Evaluate TPLS tuning parameters using cross validation
TPLSdat

Sample participant data from a left-right button press task
makePredictor

Extracts a predictor (betamap and intercept) from a TPLS model at a given number of components and given threshold value