Scalable Statistical Computing with HDF5-Backed Matrices R package BigDataStatMeth version 2.0.2 A framework for 'scalable' statistical computing on large on-disk matrices stored in 'HDF5' files. It provides efficient block-wise implementations of core linear-algebra operations (matrix multiplication, SVD, PCA, QR decomposition, and canonical correlation analysis) written in C++ and R. These building blocks are designed not only for direct use, but also as foundational components for developing new statistical methods that must operate on datasets too large to fit in memory. The package supports data provided either as 'HDF5' files or standard R objects, and is intended for high-dimensional applications such as 'omics' and precision-medicine research. Software Depends: R (>= 4.1.0) Imports: data.table, Rcpp (>= 1.0.6), RCurl, utils, R6 LinkingTo: Rcpp, RcppEigen, Rhdf5lib Suggests: Matrix, BiocStyle, knitr, rmarkdown, ggplot2, MASS Dolors Pelegri-Siso <dolors.pelegri@isglobal.org> Comprehensive R Archive Network (CRAN) Dolors Pelegri-Siso [aut, cre] (ORCID: <https://orcid.org/0000-0002-5993-3003>), Juan R. Gonzalez [aut] (ORCID: <https://orcid.org/0000-0003-3267-2146>) MIT + file LICENSE (https://CRAN.R-project.org/package=BigDataStatMeth/LICENSE) 2026-06-08 application/tgz https://CRAN.R-project.org/package=BigDataStatMeth doi:10.32614/CRAN.package.BigDataStatMeth