BigDataStatMeth 1.0.2

Major changes

• Reduced example execution time for CRAN compliance
• Fixed bdblockmult_hdf5 example (< 5 seconds now)

Bug fixes

• Fixed Makevars configuration issue causing compilation warnings

BigDataStatMeth 1.0.1

CRAN Resubmission Fixes

• Thread management: Implemented respect for OMP_THREAD_LIMIT environment variable
• Default threads: Limited to maximum 2 threads on CRAN servers to prevent excessive CPU usage
• Documentation: Replaced all Unicode characters with proper LaTeX macros (, )
• HTML validation: Corrected invalid HTML tags in all documentation files
• ATLAS compatibility: Tested with standard BLAS/LAPACK configurations

Bug Fixes

• Fixed thread safety in parallel HDF5 operations
• Improved file locking mechanisms for concurrent access
• Corrected dimension handling in transposed operations
• Enhanced error messages for invalid inputs

Documentation Improvements

• Updated all function examples with proper mathematical notation
• Added comprehensive CRAN submission notes
• Improved vignette with clearer explanations
• Fixed formatting issues in Rd files

BigDataStatMeth 1.0.0

Major changes

• Complete rewrite of the package after the archived version 0.99.32.
• New block-wise computing framework for large matrices stored in HDF5.
• New C++ backend integrated with R through Rcpp and Rhdf5lib.
• API redesigned; not backwards compatible with previous versions.
• Package now focuses on providing scalable building blocks to develop new statistical methods for large datasets.

New features

• Block-wise matrix multiplication for in-memory and HDF5-backed matrices.
• Block-wise SVD and PCA implementations.
• Block-wise QR decomposition.
• Block-wise crossproduct and matrix operations using HDF5 storage.
• Canonical Correlation Analysis (CCA) for HDF5 matrices via bdCCA_hdf5_rcpp().
• Improved HDF5 import utilities, including support for large text files.
• Support for parallel computation (OpenMP) when available.
• Low memory footprint for large matrices exceeding system RAM.

Improvements

• Substantial performance improvements in matrix operations on HDF5 data.
• More robust dimension handling and HDF5 metadata management.
• Unified interface for in-memory and on-disk data.
• Better error handling and validation on input dimensions and block sizes.

Removed

• Old implementations from the 0.99.x series have been removed.
• Deprecated functions and APIs have been replaced by the new block-wise framework.