Package: bigPLSR
Version: 0.7.2
Date: 2025-11-26
Depends: R (>= 4.0.0)
biocViews:
Imports: Rcpp, bigmemory
LinkingTo: Rcpp, RcppArmadillo, BH, bigmemory
Suggests: bench, dplyr, forcats, future, future.apply, ggplot2, knitr,
        pls, plsRglm, rmarkdown, RhpcBLASctl, svglite, testthat (>=
        3.0.0), tidyr, withr
VignetteBuilder: knitr
Title: Partial Least Squares Regression Models with Big Matrices
Authors@R: c(
  person(given = "Frederic", family= "Bertrand", role = c("cre", "aut"), 
  email = "frederic.bertrand@lecnam.net", comment = c(ORCID = "0000-0002-0837-8281")),
  person(given = "Myriam", family= "Maumy", role = c("aut"), 
  email = "myriam.maumy@ehesp.fr", comment = c(ORCID = "0000-0002-4615-1512")))
Author: Frederic Bertrand [cre, aut]
        (<https://orcid.org/0000-0002-0837-8281>), Myriam Maumy [aut]
        (<https://orcid.org/0000-0002-4615-1512>)
Maintainer: Frederic Bertrand <frederic.bertrand@lecnam.net>
Description: Fast partial least squares (PLS) for dense and out-of-core
        data. Provides SIMPLS (straightforward implementation of a
        statistically inspired modification of the PLS method) and
        NIPALS (non-linear iterative partial least-squares) solvers,
        plus kernel-style PLS variants ('kernelpls' and
        'widekernelpls') with parity to 'pls'. Optimized for
        'bigmemory'-backed matrices with streamed cross-products and
        chunked BLAS (Basic Linear Algebra Subprograms) (XtX/XtY and
        XXt/YX), optional file-backed score sinks, and deterministic
        testing helpers. Includes an auto-selection strategy that
        chooses between XtX SIMPLS, XXt (wide) SIMPLS, and NIPALS based
        on (n, p) and a configurable memory budget. About the package,
        Bertrand and Maumy (2023) <https://hal.science/hal-05352069>,
        and <https://hal.science/hal-05352061> highlighted fitting and
        cross-validating PLS regression models to big data. For more
        details about some of the techniques featured in the package,
        Dayal and MacGregor (1997)
        <doi:10.1002/(SICI)1099-128X(199701)11:1%3C73::AID-CEM435%3E3.0.CO;2-%23>,
        Rosipal & Trejo (2001)
        <https://www.jmlr.org/papers/v2/rosipal01a.html>, Tenenhaus,
        Viennet, and Saporta (2007) <doi:10.1016/j.csda.2007.01.004>,
        Rosipal (2004) <doi:10.1007/978-3-540-45167-9_17>, Rosipal
        (2019) <https://ieeexplore.ieee.org/document/8616346>, Song,
        Wang, and Bai (2024) <doi:10.1016/j.chemolab.2024.105238>.
        Includes kernel logistic PLS with 'C++'-accelerated alternating
        iteratively reweighted least squares (IRLS) updates, streamed
        reproducing kernel Hilbert space (RKHS) solvers with reusable
        centering statistics, and bootstrap diagnostics with graphical
        summaries for coefficients, scores, and cross-validation
        workflows, alongside dedicated plotting utilities for
        individuals, variables, ellipses, and biplots. The streaming
        backend uses far less memory and keeps memory bounded across
        data sizes. For PLS1, streaming is often fast enough while
        preserving a small memory footprint; for PLS2 it remains
        competitive with a bounded footprint. On small problems that
        fit comfortably in RAM (random-access memory), dense in-memory
        solvers are slightly faster; the crossover occurs as n or p
        grow and the Gram/cross-product cost dominates.
License: GPL-3
Encoding: UTF-8
URL: https://fbertran.github.io/bigPLSR/,
        https://github.com/fbertran/bigPLSR
BugReports: https://github.com/fbertran/bigPLSR/issues
Classification/MSC: 62N01, 62N02, 62N03, 62N99
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.3
LazyData: true
Config/testthat/edition: 3
SystemRequirements: C++17, Optional CBLAS (detected at compile time)
NeedsCompilation: yes
Repository: https://fbertran.r-universe.dev
Date/Publication: 2025-11-26 01:49:56 UTC
RemoteUrl: https://github.com/fbertran/bigplsr
RemoteRef: HEAD
RemoteSha: 2033eb023bea3771b67270a4f81fe3d922b587de
Packaged: 2026-05-31 08:35:40 UTC; root