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, and QR decomposition) written in C++ and R, along with building
blocks from which higher-level multivariate methods such as canonical
correlation analysis can be constructed. 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: |
2.0.4 |
| 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: |
2026-07-19 |
| 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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