Package: bigPCAcpp
Type: Package
Title: Principal Component Analysis for 'bigmemory' Matrices
Version: 0.9.1
Date: 2026-03-25
Authors@R: person("Frederic", "Bertrand", role = c("aut", "cre"),
       email = "frederic.bertrand@lecnam.net")
Author: Frederic Bertrand [aut, cre]
Maintainer: Frederic Bertrand <frederic.bertrand@lecnam.net>
Description: High performance principal component analysis routines
        that operate directly on bigmemory::big.matrix() objects. The
        package avoids materialising large matrices in memory by
        streaming data through 'BLAS' and 'LAPACK' kernels and provides
        helpers to derive scores, loadings, correlations, and
        contribution diagnostics, including utilities that stream
        results into 'bigmemory'-backed matrices for file-based
        workflows. Additional interfaces expose 'scalable' singular
        value decomposition, robust PCA, and robust SVD algorithms so
        that users can explore large matrices while tempering the
        influence of outliers. 'Scalable' principal component analysis
        is also implemented, Elgamal, Yabandeh, Aboulnaga, Mustafa, and
        Hefeeda (2015) <doi:10.1145/2723372.2751520>.
License: GPL (>= 2)
Depends: R (>= 3.5.0)
Imports: Rcpp (>= 1.0.12), methods, withr
Suggests: bench, bigmemory, ggplot2, irlba, knitr, rmarkdown, testthat
        (>= 3.0.0)
LinkingTo: Rcpp, bigmemory, BH
Encoding: UTF-8
VignetteBuilder: knitr
LazyLoad: yes
NeedsCompilation: yes
URL: https://fbertran.github.io/bigPCAcpp/,
        https://github.com/fbertran/bigPCAcpp
BugReports: https://github.com/fbertran/bigPCAcpp/issues
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.3
Config/testthat/edition: 3
Repository: https://fbertran.r-universe.dev
Date/Publication: 2026-03-25 01:21:11 UTC
RemoteUrl: https://github.com/fbertran/bigpcacpp
RemoteRef: HEAD
RemoteSha: a8ed2d5f3ea7ada98b419e1e7ee3bc83425efb00
Packaged: 2026-06-23 07:07:20 UTC; root