{ "_id": "6abc63a699f7aa8c6aa4a92b", "Package": "bigPLSR", "Version": "0.8.0", "Date": "2026-09-29", "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) , Song, Wang,\nand Bai (2024) . Includes\nkernel 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)", "LazyData": "true", "Config/testthat/edition": "3", "SystemRequirements": "C++17, Optional CBLAS (detected at compile time)", "NeedsCompilation": "yes", "Config/roxygen2/version": "8.0.0", "Repository": "https://fbertran.r-universe.dev", "Date/Publication": "2026-09-29 23:57:17 UTC", "RemoteUrl": "https://github.com/fbertran/bigplsr", "RemoteRef": "HEAD", "RemoteSha": "2789ca52273ba5c9c69392b5e63bd84523df7a8f", "Packaged": { "Date": "2026-09-30 01:10:11 UTC", "User": "root" }, "_user": "fbertran", "_type": "src", "_file": "bigPLSR_0.8.0.tar.gz", "_fileid": "https://r2.ropensci.org/ac3fd11ce4f19f70ee2f82b0009471df4ac019ecc64ad1a31c1406fe42a4b2b0", "_filesize": 3169693, "_sha256": "ac3fd11ce4f19f70ee2f82b0009471df4ac019ecc64ad1a31c1406fe42a4b2b0", "_expires": "2027-01-08T01:19:32.000Z", "_created": "2026-09-30T01:10:11.000Z", "_published": "2026-09-30T01:19:34.792Z", "_jobs": [ { "job": 109694080277, "time": 217, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "11071078917" }, { "job": 109694080210, "time": 307, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11071588313" }, { "job": 109694080207, "time": 271, "config": "linux-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "11071563295" }, { "job": 109694080155, "time": 313, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11071727893" }, { "job": 109694080160, "time": 149, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "11070784127" }, { "job": 109694080134, "time": 336, "config": "macos-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "11071623223" }, { "job": 109694080115, "time": 175, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "11070724203" }, { "job": 109694080150, "time": 342, "config": "macos-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11072020730" }, { "job": 109692339123, "time": 423, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "11071672447" }, { "job": 109694080126, "time": 216, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "11070864174" }, { "job": 109694080246, "time": 436, "config": "windows-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "11071454124" }, { "job": 109694080190, "time": 325, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11070774458" }, { "job": 109694080286, "time": 319, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "11070994052" }, { "job": 109694080434, "time": 373, "config": "windows-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "11071364413" }, { "job": 109694080192, "time": 294, "config": "windows-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11071618202" } ], "_host": "GitHub-Actions", "_buildurl": "https://github.com/r-universe/fbertran/actions/runs/36653355818", "_status": "success", "_upstream": "https://github.com/fbertran/bigplsr", "_commit": { "id": "2789ca52273ba5c9c69392b5e63bd84523df7a8f", "author": "Frederic Bertrand ", "committer": "Frederic Bertrand ", "message": "* Fixed RKHS score prediction to center cross-kernels with the fitted training\n statistics. 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