NEWS

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BigDataStatMeth 2.0.1 (2026-05-17)

• Fixed vignette build failure on Debian: replaced external CSV file with reproducible synthetic data in the hdf5_import example.

BigDataStatMeth 2.0.0 (2026-05-14)

This is a major release of BigDataStatMeth. It introduces a new HDF5Matrix user-facing interface, standard S3 methods for HDF5-backed matrices, and an extended C++ infrastructure for block-wise statistical computing with HDF5 files.

Major changes

• Added the HDF5Matrix interface for working with matrices stored in HDF5 files.
• Added S3 methods that allow HDF5-backed matrices to be used with familiar R calls such as dim(), [, [<-, %*%, crossprod(), tcrossprod(), scale(), cor(), svd(), prcomp(), qr(), chol(), and solve().
• Reorganized the package around a standard R interface backed by a C++ computational infrastructure for block-wise statistical computing.
• Added global configuration through hdf5matrix_options() for common settings such as parallel execution, number of threads, block size, and HDF5 compression.

User-facing functionality

• Added or extended support for creating, opening, inspecting, and closing HDF5-backed matrices.
• Added support for subsetting, assignment, dimension names, and conversion to in-memory R objects.
• Added S3 methods for element-wise arithmetic operations on HDF5Matrix objects.
• Added S3 support for matrix algebra operations, including %*%, crossprod(), tcrossprod(), cbind(), and rbind().
• Added aggregation and summary methods, including colSums(), rowSums(), colMeans(), rowMeans(), colVars(), rowVars(), colSds(), rowSds(), colMins(), rowMins(), colMaxs(), rowMaxs(), mean(), var(), and sd().
• Added support for statistical transformations including scale(), sweep(), and cor().
• Added or extended methods for matrix decompositions and factorizations, including svd(), prcomp(), qr(), chol(), solve(), eigen(), and pseudoinverse().
• Added diagonal operations and utilities, including diag(), diag<-(), diag_op(), and diag_scale().
• Added split, reduce, and apply utilities for HDF5-backed workflows.
• Retained additional high-level bd* utilities for specialized workflows that do not map directly to standard R generics.

HDF5 storage and resource management

• Added list_datasets() for inspecting datasets stored in HDF5 files.
• Added hdf5_close_all() for closing open HDF5 handles managed by the package.
• Improved HDF5 handle and pointer management, including safer behavior when overwriting datasets and when unloading or reloading the package.
• Added HDF5 compression handling and propagation of compression settings across output datasets.
• Added support for HDF5 file space management so that free space released by deleted datasets can be reused by subsequent writes in files created by the package.

Performance and numerical methods

• Added support for block-wise and parallel execution settings through hdf5matrix_options().
• Added TSQR support for tall-skinny QR decompositions, with automatic method selection when appropriate.
• Improved SVD and PCA handling for HDF5-backed matrices, including support for truncated outputs and block-wise computation.
• Improved QR, Cholesky, inverse, eigen decomposition, and pseudoinverse workflows for HDF5-backed matrices.
• Removed redundant OpenMP critical sections where HDF5 locking already provides the required synchronization.
• Improved thread handling for CRAN-compatible execution.

C++ infrastructure

• Extended the C++ backend with classes and routines for managing HDF5 files, groups, and datasets.
• Exposed reusable block-wise computational infrastructure through the package headers for developers implementing new Rcpp-based methods.
• Improved integration between the R interface and the C++ backend for matrix operations, decompositions, and HDF5 resource management.
• Added or improved C++ implementations used by the R/S3 interface for matrix algebra, decompositions, transformations, and HDF5-backed statistical operations.

Documentation and examples

• Reworked the main package vignette around the HDF5Matrix interface and standard R methods.
• Improved the package-level help page opened by help("BigDataStatMeth").
• Cleaned examples to use temporary files and avoid writing to the user's working directory.
• Removed obsolete vignette material and outdated installation examples.
• Moved example data to the package extdata directory.
• Improved documentation consistency for HDF5 terminology and package usage.

Bug fixes and reliability

• Fixed error propagation in matrix multiplication and crossproduct paths.
• Fixed signed integer overflow in Cholesky block-size computation: minimumBlockSize promoted from int to long in Cholesky_decomposition_intermediate_hdf5 and Inverse_of_Cholesky_decomposition_intermediate_hdf5 to prevent overflow before promotion to double for sqrt(). Detected by gcc-ASAN on R-hub.
• Fixed index bug in block SVD nzeros threshold loop (matrixSvdBlock.hpp): wrong singular value was evaluated in the rank-truncation check, causing incorrect nzeros counts.
• Fixed thread-safety crash in multi-level block SVD (H5SL_insert from H5I_register) when the number of hierarchical levels q >= 2: Next_level_SvdBlock_decomposition_hdf5 is now sequential; the outer block loop already provides sufficient parallelism.
• Fixed nev parameter not being applied to per-block truncation in First_level_SvdBlock_decomposition_hdf5: the parameter was declared in the function signature but never used, causing unnecessarily large intermediate matrices and a final SVD over more components than requested.
• Fixed several HDF5 pointer, handle, and finalizer edge cases.
• Fixed portability issues related to platform-specific memory queries.
• Fixed several edge cases in diagonal operations, matrix-vector operations, sparse multiplication internals, and SVD/PCA parameter handling.
• Improved cleanup of HDF5 handles during package unloading.
• Improved behavior when repeatedly creating, overwriting, closing, and reopening HDF5-backed datasets.

BigDataStatMeth 1.0.3 (2025-12-22)

• Minor documentation and example updates.
• Version previously available on CRAN before the 2.0.0 redesign.

BigDataStatMeth 1.0.2 (2025-11-29)

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 (\eqn{}, \deqn{})
• 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.