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DESCRIPTION

Version: 1.0.2 # Era 1.0.1

NEWS.md - Añadir al inicio

BigDataStatMeth 1.0.2

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

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• Deprecated functions and APIs have been replaced by the new block-wise framework.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.