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riemtan 0.2.5

New Features

Futureverse/furrr Parallel Processing

• Added comprehensive parallel processing support via the futureverse framework (future + furrr packages)
• Cross-platform parallelization: Works on all platforms including Windows (replaces platform-specific parallel::mclapply)
• Intelligent auto-detection: Automatically uses parallel processing when beneficial based on data size
• Flexible configuration: Users can control parallelization via set_parallel_plan()
• Progress reporting: Optional progress bars via progressr package integration

Parallel Processing Infrastructure

• New module R/parallel_config.R:
  • set_parallel_plan(): Configure parallel strategy (sequential, multisession, multicore, cluster)
  • is_parallel_enabled(): Check parallel status
  • should_parallelize(): Auto-detect when to parallelize based on threshold
  • get_n_workers(): Query number of parallel workers
  • reset_parallel_plan(): Reset to sequential mode
• New module R/progress_utils.R:
  • with_progress(): Execute expressions with progress reporting
  • create_progressor(): Create progress reporters for loops
  • configure_progress(): Configure progress handlers
  • is_progress_available(): Check progressr availability

Enhanced Components with Parallel Support

• TangentImageHandler (R/tangent_handler.R):
  • compute_tangents(): Parallel tangent space projections
  • compute_vecs(): Parallel vectorization
  • compute_conns(): Parallel exponential maps
  • set_reference_point(): Parallel tangent relocation
  • All methods support optional progress parameter
• ParquetBackend (R/parquet_backend.R):
  • get_all_matrices(): Parallel matrix loading from Parquet files
  • get_matrices_parallel(): New method for batch parallel loading
  • Automatic parallelization for I/O-bound operations
• Core Algorithms (R/other_utils.R):
  • relocate(): Now uses furrr::future_map() instead of parallel::mclapply (cross-platform!)
  • compute_frechet_mean(): Parallel processing support with progress parameter
  • Both functions support progress parameter
• CSample Class (R/sample.R):
  • compute_tangents(): Pass-through progress support
  • compute_vecs(): Pass-through progress support
  • compute_unvecs(): Parallel unvectorizati with progress
  • compute_conns(): Pass-through progress support
  • compute_fmean(): Pass-through progress support
  • change_ref_pt(): Pass-through progress support
  • load_connectomes_batched(): NEW - Batch loading with memory management for large Parquet datasets

Performance Improvements

• 5-10x speedup for loading large Parquet datasets (parallel I/O)
• 3-8x speedup for tangent computations on large samples (scales with workers)
• 2-5x speedup for Frechet mean computation (n > 100)
• Reduced overhead for small datasets via intelligent threshold-based auto-detection

Breaking Changes

• relocate() function signature changed: added optional progress parameter (default: FALSE)
  • Existing code continues to work without modification
• compute_frechet_mean() function signature changed: added optional progress parameter
  • Existing code continues to work without modification

Dependencies

• Added future to Imports (cross-platform parallel backend)
• Added furrr to Imports (future-based parallel map functions)
• Added progressr to Suggests (optional progress reporting)

Documentation

• Updated vignettes with parallel processing examples
• New performance benchmarking guidance
• Comprehensive roxygen2 documentation for all new functions

Internal Changes

• Removed dependency on parallel::mclapply (platform-specific, Windows incompatible)
• All parallel operations now use futureverse framework for consistency
• Progress reporting infrastructure integrated throughout codebase

riemtan 0.2.4

New Features

Apache Arrow/Parquet Support

• Added support for Apache Arrow Parquet format for efficient storage and lazy loading of large connectome datasets
• Introduced backend abstraction layer (DataBackend, ListBackend, ParquetBackend) to decouple storage from business logic
• ListBackend: Wraps existing list-based storage for backwards compatibility
• ParquetBackend: Lazy-loads matrices from Parquet files with LRU caching (default cache size: 10 matrices)
• New I/O functions:
  • write_connectomes_to_parquet(): Export matrices to Parquet format with metadata
  • validate_parquet_directory(): Validate Parquet directory structure
  • create_parquet_backend(): Convenience function to create ParquetBackend
• CSample now accepts backend parameter for flexible storage options
• Full backwards compatibility: existing code using lists continues to work unchanged
• Metadata support: Store subject IDs, data provenance, and custom metadata with Parquet datasets
• New vignette: “Using Parquet Storage for Large Datasets”

Validation Functions

• Added validate_backend(): Validates backend objects
• Added validate_parquet_dir(): Validates Parquet directory structure

Internal Changes

• Modified CSample to use backend abstraction internally
• Updated CSample$connectomes active binding to support lazy loading
• CSuperSample works transparently with all backend types

Dependencies

• Added arrow to Imports for Parquet support

Documentation

• Updated package documentation with roxygen2
• Added comprehensive test suite for Parquet backend (50+ tests)
• New vignette explaining Parquet backend usage

riemtan 0.2.0

New Features

• Added super_sample.R, introducing the CSuperSample class for handling and analyzing collections of CSample objects.
• Added comprehensive tests for CSuperSample in test-csupersample.R.

Dependency Changes

• Moved the Matrix package from Depends to Imports in the DESCRIPTION file.

riemtan 0.1.0

New Features

• Implements various Riemannian metrics for symmetric positive definite matrices, including:
  • Affine Invariant Riemannian Metric (AIRM)
  • Log-Euclidean
  • Euclidean
  • Log-Cholesky
  • Bures-Wasserstein metrics
• Provides functions for:
  • Computing logarithmic and exponential maps
  • Vectorization

Documentation

• Added comprehensive documentation for all functions using roxygen2.
• Included vignettes demonstrating the usage of the package.

Dependencies

• Depends on R (>= 4.3.0)
• Imports the following packages:
  • Matrix
  • methods
  • expm
  • R6
  • purrr
  • MASS
  • furrr
• Suggests the following packages for testing and documentation:
  • testthat (>= 3.0.0)
  • knitr
  • rmarkdown

Miscellaneous

• Added a LICENSE file with the MIT license.
• Maintainer: Nicolas Escobar nescoba@iu.edu

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.