parallel::mclapply)set_parallel_plan()New module R/parallel_config.R:
set_parallel_plan(): Configure parallel strategy (sequential, multisession, multicore, cluster)is_parallel_enabled(): Check parallel statusshould_parallelize(): Auto-detect when to parallelize based on thresholdget_n_workers(): Query number of parallel workersreset_parallel_plan(): Reset to sequential modeNew module R/progress_utils.R:
with_progress(): Execute expressions with progress reportingcreate_progressor(): Create progress reporters for loopsconfigure_progress(): Configure progress handlersis_progress_available(): Check progressr availabilityTangentImageHandler (R/tangent_handler.R):
compute_tangents(): Parallel tangent space projectionscompute_vecs(): Parallel vectorizationcompute_conns(): Parallel exponential mapsset_reference_point(): Parallel tangent relocationprogress parameterParquetBackend (R/parquet_backend.R):
get_all_matrices(): Parallel matrix loading from Parquet filesget_matrices_parallel(): New method for batch parallel loadingCore 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 parameterprogress parameterCSample Class (R/sample.R):
compute_tangents(): Pass-through progress supportcompute_vecs(): Pass-through progress supportcompute_unvecs(): Parallel unvectorizati with progresscompute_conns(): Pass-through progress supportcompute_fmean(): Pass-through progress supportchange_ref_pt(): Pass-through progress supportload_connectomes_batched(): NEW - Batch loading with memory management for large Parquet datasetsrelocate() function signature changed: added optional progress parameter (default: FALSE)
compute_frechet_mean() function signature changed: added optional progress parameter
future to Imports (cross-platform parallel backend)furrr to Imports (future-based parallel map functions)progressr to Suggests (optional progress reporting)parallel::mclapply (platform-specific, Windows incompatible)DataBackend, ListBackend, ParquetBackend) to decouple storage from business logicListBackend: Wraps existing list-based storage for backwards compatibilityParquetBackend: Lazy-loads matrices from Parquet files with LRU caching (default cache size: 10 matrices)write_connectomes_to_parquet(): Export matrices to Parquet format with metadatavalidate_parquet_directory(): Validate Parquet directory structurecreate_parquet_backend(): Convenience function to create ParquetBackendCSample now accepts backend parameter for flexible storage optionsvalidate_backend(): Validates backend objectsvalidate_parquet_dir(): Validates Parquet directory structureCSample to use backend abstraction internallyCSample$connectomes active binding to support lazy loadingCSuperSample works transparently with all backend typesarrow to Imports for Parquet supportsuper_sample.R, introducing the CSuperSample class for handling and analyzing collections of CSample objects.CSuperSample in test-csupersample.R.Matrix package from Depends to Imports in the DESCRIPTION file.roxygen2.LICENSE file with the MIT license.