Package: bigPCAcpp
Type: Package
Title: Principal Component Analysis for 'bigmemory' Matrices
Version: 0.9.0
Date: 2025-10-02
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: knitr, rmarkdown, bigmemory, ggplot2, 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/
RoxygenNote: 7.3.3
Config/testthat/edition: 3
Packaged: 2025-10-14 21:24:55 UTC; bertran7
Repository: CRAN
Date/Publication: 2025-10-20 19:20:07 UTC
Built: R 4.4.3; x86_64-w64-mingw32; 2025-12-19 02:06:29 UTC; windows
Archs: x64
