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.
| Version: |
0.3 |
| Depends: |
R (≥ 4.6.0) |
| Imports: |
methods, float |
| Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2026-09-28 |
| DOI: |
10.32614/CRAN.package.fastPLS |
| Author: |
Stefano Cacciatore
 [aut, cre],
Dupe Ojo [aut],
Leonardo Tenori
[aut],
Alessia Vignoli
[aut] |
| Maintainer: |
Stefano Cacciatore <tkcaccia at gmail.com> |
| BugReports: |
https://github.com/tkcaccia/fastPLS/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/tkcaccia/fastPLS |
| NeedsCompilation: |
yes |
| SystemRequirements: |
Optional OpenBLAS development libraries for faster
CPU matrix operations; optional NVIDIA CUDA Toolkit with CUDA
Runtime, cuBLAS, cuSOLVER and cuRAND plus a compatible
separately managed NVIDIA driver; optional Apple Metal
framework on macOS. CPU-only builds do not require GPU
software. |
| Materials: |
README, NEWS, INSTALL |
| CRAN checks: |
fastPLS results [issues need fixing before 2026-10-20] |
Documentation:
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=fastPLS
to link to this page.