Tools and Infrastructure for Developing 'Scalable' 'HDF5'-Based Methods
| Package name | Version | Title | Date | Size | License | |
|---|---|---|---|---|---|---|
| BigDataStatMeth | 1.0.2 | Tools and Infrastructure for Developing 'Scalable' 'HDF5'-Based Methods | Sat Nov 29 2025 | 1153.89kB | MIT + file LICENSE | |
| BigDataStatMeth | 0.99.32 | Statistical Methods and Algorithms for Big Data | Tue Mar 29 2022 | 1518.90kB | MIT + file LICENSE | |
| BigDataStatMeth | 0.99.14 | Statistical Methods and Algorithms for Big Data | Fri Dec 17 2021 | 1701.49kB | MIT + file LICENSE |
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.