bigPCAcpp: Principal Component Analysis for bigmemory Matrices

The bigPCAcpp package provides high-performance principal component analysis routines that work directly with bigmemory::big.matrix objects. Data are streamed through BLAS and LAPACK kernels so large, file-backed matrices can be analysed without materialising dense copies in R. Companion helpers compute scores, loadings, correlations, and contributions, including streaming variants that write results to bigmemory::big.matrix destinations used by file-based pipelines.

See also

pca_bigmatrix(), pca_stream_bigmatrix()

Author

Maintainer: Frederic Bertrand frederic.bertrand@lecnam.net

Examples

# \donttest{
library(bigmemory)
mat <- as.big.matrix(matrix(rnorm(20), nrow = 5))
result <- pca_bigmatrix(mat)
result$sdev
#> [1] 2.0348820 1.0310207 0.5643723 0.1060603
# }