Principal Component Analysis for 'bigmemory' Matrices R package bigPCAcpp version 0.9.1 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>. Software Depends: R (>= 3.5.0) Imports: Rcpp (>= 1.0.12), methods, withr LinkingTo: Rcpp, bigmemory, BH Suggests: bench, bigmemory, ggplot2, irlba, knitr, rmarkdown, testthat (>= 3.0.0) Frederic Bertrand <frederic.bertrand@lecnam.net> Comprehensive R Archive Network (CRAN) Frederic Bertrand [aut, cre] GPL (>= 2) 2026-03-25 application/tgz https://CRAN.R-project.org/package=bigPCAcpp doi:10.32614/CRAN.package.bigPCAcpp