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bigPCAcpp  

Principal Component Analysis for 'bigmemory' Matrices
View on CRAN: Click here


Download and install bigPCAcpp package within the R console
Install from CRAN:
install.packages("bigPCAcpp")

Install from Github:
library("remotes")
install_github("cran/bigPCAcpp")

Install by package version:
library("remotes")
install_version("bigPCAcpp", "0.9.0")



Attach the package and use:
library("bigPCAcpp")
Maintained by
Frederic Bertrand
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-10-20
Latest Update: 2025-10-20
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>.
How to cite:
Frederic Bertrand (2025). bigPCAcpp: Principal Component Analysis for 'bigmemory' Matrices. R package version 0.9.0, https://cran.r-project.org/web/packages/bigPCAcpp. Accessed 11 Mar. 2026.
Previous versions and publish date:
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Complete documentation for bigPCAcpp
Functions, R codes and Examples using the bigPCAcpp R package
Full bigPCAcpp package functions and examples
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