{ "_id": "6a1bf5071d7bb097a0a25ca7", "Package": "bigPLSR", "Version": "0.7.2", "Date": "2025-11-26", "biocViews": "", "VignetteBuilder": "knitr", "Title": "Partial Least Squares Regression Models with Big Matrices", "Authors@R": "c(\nperson(given = \"Frederic\", family= \"Bertrand\", role = c(\"cre\", \"aut\"),\nemail = \"frederic.bertrand@lecnam.net\", comment = c(ORCID = \"0000-0002-0837-8281\")),\nperson(given = \"Myriam\", family= \"Maumy\", role = c(\"aut\"),\nemail = \"myriam.maumy@ehesp.fr\", comment = c(ORCID = \"0000-0002-4615-1512\")))", "Author": "Frederic Bertrand [cre, aut]\n(), Myriam Maumy [aut]\n()", "Maintainer": "Frederic Bertrand ", "Description": "Fast partial least squares (PLS) for dense and out-of-core\ndata. Provides SIMPLS (straightforward implementation of a\nstatistically inspired modification of the PLS method) and\nNIPALS (non-linear iterative partial least-squares) solvers,\nplus kernel-style PLS variants ('kernelpls' and\n'widekernelpls') with parity to 'pls'. Optimized for\n'bigmemory'-backed matrices with streamed cross-products and\nchunked BLAS (Basic Linear Algebra Subprograms) (XtX/XtY and\nXXt/YX), optional file-backed score sinks, and deterministic\ntesting helpers. Includes an auto-selection strategy that\nchooses between XtX SIMPLS, XXt (wide) SIMPLS, and NIPALS based\non (n, p) and a configurable memory budget. About the package,\nBertrand and Maumy (2023) ,\nand highlighted fitting and\ncross-validating PLS regression models to big data. For more\ndetails about some of the techniques featured in the package,\nDayal and MacGregor (1997)\n,\nRosipal & Trejo (2001)\n, Tenenhaus,\nViennet, and Saporta (2007) ,\nRosipal (2004) , Rosipal\n(2019) , Song,\nWang, and Bai (2024) .\nIncludes kernel logistic PLS with 'C++'-accelerated alternating\niteratively reweighted least squares (IRLS) updates, streamed\nreproducing kernel Hilbert space (RKHS) solvers with reusable\ncentering statistics, and bootstrap diagnostics with graphical\nsummaries for coefficients, scores, and cross-validation\nworkflows, alongside dedicated plotting utilities for\nindividuals, variables, ellipses, and biplots. The streaming\nbackend uses far less memory and keeps memory bounded across\ndata sizes. For PLS1, streaming is often fast enough while\npreserving a small memory footprint; for PLS2 it remains\ncompetitive with a bounded footprint. On small problems that\nfit comfortably in RAM (random-access memory), dense in-memory\nsolvers are slightly faster; the crossover occurs as n or p\ngrow and the Gram/cross-product cost dominates.", "License": "GPL-3", "Encoding": "UTF-8", "URL": "https://fbertran.github.io/bigPLSR/,\nhttps://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": { "Date": "2026-05-31 08:35:40 UTC", "User": "root" }, "MD5sum": "c3ffcb38d7d7703caac087b344521f7f", "_user": 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