BigDataStatMeth: Scalable Statistical Computing with HDF5-Backed Matrices
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.
| Version: |
2.0.2 |
| 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 |
| Published: |
2026-06-08 |
| DOI: |
10.32614/CRAN.package.BigDataStatMeth |
| Author: |
Dolors Pelegri-Siso
 [aut, cre],
Juan R. Gonzalez
[aut] |
| Maintainer: |
Dolors Pelegri-Siso <dolors.pelegri at isglobal.org> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
yes |
| SystemRequirements: |
GNU make, C++17 |
| Materials: |
README, NEWS |
| CRAN checks: |
BigDataStatMeth results |
Documentation:
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