Package: BigDataStatMeth
Type: Package
Title: Title: Scalable Statistical Computing with HDF5-Backed Matrices
Version: 2.0.1
Date: 2026-05-16
Authors@R: 
    c(
        person( given = "Dolors", 
                family = "Pelegri-Siso", 
                email = "dolors.pelegri@isglobal.org", 
                role=c("aut", "cre"),
                comment = c(ORCID = "0000-0002-5993-3003")),
        person( given = "Juan R.", 
                family = "Gonzalez", 
                email = "juanr.gonzalez@isglobal.org", 
                role = c("aut"), 
                comment = c(ORCID = "0000-0003-3267-2146"))
        )
Description: 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.
License: MIT + file LICENSE
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
SystemRequirements: GNU make, C++17
Encoding: UTF-8
VignetteBuilder: knitr
RoxygenNote: 7.3.3
NeedsCompilation: yes
Author: 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>)
Maintainer: Dolors Pelegri-Siso <dolors.pelegri@isglobal.org>
Packaged: 2026-05-17 18:06:37 UTC; mailos
Repository: CRAN
Date/Publication: 2026-05-17 22:40:30 UTC
Built: R 4.5.3; x86_64-w64-mingw32; 2026-05-21 12:43:22 UTC; windows
Archs: x64
