Package: fastPLS 0.4

Stefano Cacciatore

fastPLS: Fast Partial Least Squares for High-Dimensional Data

Fast implementations of partial least squares models for high-dimensional regression and classification. The 'fastPLS' software provides compiled implementations of PLS-SVD, a SIMPLS-family estimator, OPLS and kernel PLS, together with truncated singular value decomposition backends, discriminant classifiers, cross-validation utilities and optional 'CUDA' or Apple 'Metal' acceleration when the required system libraries are available. Compact latent prediction and memory-aware numerical routes support analyses with large predictor or multivariate-response matrices.

Authors:Stefano Cacciatore [aut, cre], Dupe Ojo [aut], Leonardo Tenori [aut], Alessia Vignoli [aut]

fastPLS_0.4.tar.gz
fastPLS_0.4.zip(r-4.7-x86_64)fastPLS_0.4.zip(r-4.7-arm64)fastPLS_0.4.zip(r-4.6-x86_64)fastPLS_0.4.zip(r-4.6-arm64)
fastPLS_0.4.tar.gz(r-4.7-arm64)fastPLS_0.4.tar.gz(r-4.7-x86_64)fastPLS_0.4.tar.gz(r-4.6-arm64)fastPLS_0.4.tar.gz(r-4.6-x86_64)
fastPLS_0.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
fastPLS/json (API)

# Install 'fastPLS' in R:
install.packages('fastPLS', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tkcaccia/fastpls/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

softwaredimensionreductionclassificationregressiongenepredictiongeneexpressionmetabolomicssinglecellgpuopenblascpp

3.78 score 1 packages 8 scripts 446 downloads 10 exports 2 dependencies

Last updated from:da8516d58c. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK222
linux-devel-x86_64OK244
source / vignettesOK333
linux-release-arm64OK278
linux-release-x86_64OK309
windows-devel-arm64OK282
windows-devel-x86_64OK259
windows-release-arm64OK256
windows-release-x86_64OK220
wasm-releaseOK158

Exports:cuda_infoevaluatefastcorfastPLS_blasfastsvdplot.permutationplspls.double.cvpls.single.cvViP

Dependencies:floatgpuinfo

fastPLS User Guide
Overview | Choosing a Model | Installation and Compilation | macOS | Ubuntu and Debian | Fedora | Windows x86-64 | Verify the Compiled Libraries | Backend and CPU Configuration | Supported Computational Routes | Public API and Data Inputs | Choosing a Starting Model | Classification Tasks | Fit And Predict A Classifier | Evaluate Classification Predictions | Classification Heads | Kernel PLS For Classification | Classification Score Plots | Regression Tasks | Univariate Regression | Float64 and Float32 | Evaluate Regression Predictions | Multivariate Regression | OPLS For Regression | Cross-Validation | Ordinary k-fold CV | Grouped k-fold CV with constrain | Leave-one-out CV and leave-one-group-out CV | Component and hyperparameter optimization CV | Interpreting R2Y, Q2Y, and RMSD | Permutation-test p-values | Nested or double CV | SVD Utility | CUDA and Metal Availability | Helper Functions | Methodological Scope | Method References | Session Information
Rendered fromfastPLS.Rmdusingknitr::rmarkdown

Last update: 2026-10-20
Started: 2026-09-28

Installing fastPLS
Installation model | Standard installation | macOS | Ubuntu and Debian | Fedora | Windows x86-64 | Windows ARM64 | NVIDIA CUDA on Linux or Windows x86-64 | Verify the installed capabilities | Runtime backend and CPU-core selection | Build-time environment variables | Troubleshooting | The package is already in use | An x64 library is linked into an ARM64 build | CUDA or Metal is unavailable after installation | The vignette is not found after a GitHub installation | Record a reproducible installation | Session information
Rendered frominstallation.Rmdusingknitr::rmarkdown

Last update: 2026-10-20
Started: 2026-09-28