Tools and Infrastructure for Developing 'Scalable' 'HDF5'-Based Methods
Weighted matrix–vector products and cross-products
Apply function to different datasets inside a group
Bind matrices by rows or columns
Hdf5 datasets multiplication
Block matrix multiplication for sparse matrices
Block-Based Matrix Multiplication
HDF5 dataset subtraction
Block-Based Matrix Subtraction
HDF5 dataset addition
Block-Based Matrix Addition
Check Matrix Suitability for Eigenvalue Decomposition with Spectra
Cholesky Decomposition for HDF5-Stored Matrices
Apply Vector Operations to HDF5 Matrix
Compute correlation matrix for matrices stored in HDF5 format
Compute correlation matrix for in-memory matrices (unified function)
Create Diagonal Matrix or Vector in HDF5 File
Create an empty HDF5 dataset (no data written)
Create Group in an HDF5 File
Create hdf5 data file and write data to it
Crossprod with hdf5 matrix
Efficient Matrix Cross-Product Computation
Add Diagonal Elements from HDF5 Matrices or Vectors
Divide Diagonal Elements from HDF5 Matrices or Vectors
Multiply Diagonal Elements from HDF5 Matrices or Vectors
Apply Scalar Operations to Diagonal Elements
Subtract Diagonal Elements from HDF5 Matrices or Vectors
Eigenvalue Decomposition for HDF5-Stored Matrices using Spectra
List Datasets in HDF5 Group
Get Matrix Diagonal from HDF5
Get HDF5 Dataset Dimensions
Compute Matrix Standard Deviation and Mean in HDF5
Import data from URL or file to HDF5 format
Import Text File to HDF5
Impute Missing SNP Values in HDF5 Dataset
Matrix Inversion using Cholesky Decomposition for HDF5-Stored Matrices
Test whether an HDF5 file is locked (in use)
Move HDF5 Dataset
Normalize dataset in HDF5 file
Principal Component Analysis for HDF5-Stored Matrices
Compute Matrix Pseudoinverse (HDF5-Stored)
Compute Matrix Pseudoinverse (In-Memory)
QR Decomposition for HDF5-Stored Matrices
QR Decomposition for In-Memory Matrices
Reduce Multiple HDF5 Datasets
Remove Elements from HDF5 File
Remove Low-Representation SNPs from HDF5 Dataset
Remove SNPs Based on Minor Allele Frequency
Matrix–scalar weighted product
Solve Linear System AX = B (HDF5-Stored)
Solve Linear System AX = B (In-Memory)
Sort HDF5 Dataset Using Predefined Order
Split HDF5 Dataset into Submatrices
Create Subset of HDF5 Dataset
Singular Value Decomposition for HDF5-Stored Matrices
Transposed cross product with HDF5 matrices
Efficient Matrix Transposed Cross-Product Computation
Write dimnames to an HDF5 dataset
Write Matrix Diagonal to HDF5
Write Upper/Lower Triangular Matrix
BigDataStatMeth package documentation
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.