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>.
| Version: | 0.9.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | Rcpp (≥ 1.0.12), methods, withr |
| LinkingTo: | Rcpp, bigmemory, BH |
| Suggests: | knitr, rmarkdown, bigmemory, ggplot2, testthat (≥ 3.0.0) |
| Published: | 2025-10-20 |
| DOI: | 10.32614/CRAN.package.bigPCAcpp (may not be active yet) |
| Author: | Frederic Bertrand [aut, cre] |
| Maintainer: | Frederic Bertrand <frederic.bertrand at lecnam.net> |
| BugReports: | https://github.com/fbertran/bigPCAcpp/issues/ |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://fbertran.github.io/bigPCAcpp/, https://github.com/fbertran/bigPCAcpp/ |
| NeedsCompilation: | yes |
| Citation: | bigPCAcpp citation info |
| Materials: | README, NEWS |
| CRAN checks: | bigPCAcpp results |
| Reference manual: | bigPCAcpp.html , bigPCAcpp.pdf |
| Vignettes: |
Benchmarking bigPCAcpp Workflows (source, R code) Fast Principal Component Analysis for Big Data with bigPCAcpp (source, R code) |
| Package source: | bigPCAcpp_0.9.0.tar.gz |
| Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: not available |
| macOS binaries: | r-release (arm64): bigPCAcpp_0.9.0.tgz, r-oldrel (arm64): bigPCAcpp_0.9.0.tgz, r-release (x86_64): bigPCAcpp_0.9.0.tgz, r-oldrel (x86_64): bigPCAcpp_0.9.0.tgz |
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