BigDataStatMeth
Tools and Infrastructure for Developing 'Scalable' 'HDF5'-Based Methods
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.
- Version1.0.3
- R version≥ 4.1.0
- LicenseMIT
- LicenseLICENSE
- Needs compilation?Yes
- Last release12/22/2025
Documentation
Team
Dolors Pelegri-Siso
MaintainerShow author detailsJuan R. Gonzalez
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