elcf4R 0.4.0

• Added scaffolded download/read support for IDEAL through elcf4r_download_ideal() and elcf4r_read_ideal(), focused on extracted hourly aggregate-electricity summaries from auxiliarydata.zip.
• Added scaffolded download/read support for GX through elcf4r_download_gx() and elcf4r_read_gx(), with support for either the official SQLite database or flat exports normalized into the common panel schema.
• Added offline tests for IDEAL and GX download-resolution helpers and normalization readers, including GX SQLite-table detection.
• Updated package docs and the dataset vignette to describe IDEAL as an unshipped household-level scaffold under the current CC BY 4.0 source record, and GX as an unshipped secondary transformer/community-level scaffold with explicit licence re-verification guidance before redistribution.
• Removed implicit RETICULATE_PYTHON mutation from the LSTM backend probe and added explicit, user-driven Python selection through elcf4r_use_tensorflow_env().
• Tightened CRAN-facing package metadata and examples, including a shorter elcf4r_benchmark() help example that runs on a toy single-entity panel.

elcf4R 0.3.0

• Replaced the previous KWF baseline with a wavelet-based implementation using wavelets, deterministic calendar groups, kernel weighting and approximation/detail mean correction.
• Replaced the unused src/kwf_core.cpp placeholder with compiled KWF helper routines for distances, kernel weights, group restriction and mean-corrected forecasts, and wired the R KWF path to those accelerators.
• Added a first-class clustered KWF workflow with thermosensitivity classification, wavelet-feature clustering helpers, cluster assignment, and a dedicated elcf4r_fit_kwf_clustered() model path.
• Generalized dataset ingestion around a common normalized panel schema and added dataset adapters for iFlex, StoreNet, Low Carbon London and REFIT.
• Implemented elcf4r_download_storenet() with figshare API resolution for known household article IDs and an archive fallback for broader StoreNet retrieval.
• Added a generic rolling-origin benchmark API through elcf4r_build_benchmark_index() and elcf4r_benchmark(), with saved predictions, backend metadata and support for gam, mars, kwf, kwf_clustered and lstm.
• Completed benchmark metric coverage so shipped benchmark artifacts now carry populated NMAE, NRMSE, sMAPE and MASE values for all shipped result rows.
• Added shipped example panels and saved benchmark-result datasets for StoreNet, Low Carbon London and REFIT, complementing the existing iFlex example and benchmark artifacts.
• Expanded the shipped benchmark cohorts to stronger rolling windows: iFlex now uses 15 households with 28 train days and 7 test days; the shipped LCL and REFIT benchmark cohorts are now filtered to thermosensitive seasonal windows so kwf_clustered rows are benchmarked rather than skipped.
• Reworked dataset-facing documentation to describe the supported reader surface, shipped artifacts and reproducible data-raw/ rebuild scripts.
• Clarified the dataset roadmap around IDEAL and GX: IDEAL is a future candidate dataset with a currently verified CC BY 4.0 source record, while GX is treated as a secondary transformer-level benchmark candidate that requires explicit licence re-verification before any shipped subset is added.

elcf4R 0.2.0

• Added an iFlex preprocessing pipeline with normalized panel readers, daily-segment builders, compact shipped example data, and saved benchmark result datasets.
• Added package documentation and vignettes for the shipped iFlex workflows and benchmark outputs, and documented the bundled elcf4r_elmas_toy dataset.
• Replaced the placeholder KWF/LSTM paths with working model wrappers, unified predict.elcf4r_model(), and migrated the LSTM implementation to keras3 with automatic detection of the r-tensorflow virtualenv.
• Cleaned up package metadata, namespace declarations, tests, and examples so package checks now pass apart from environment-specific CRAN notes.

elcf4R 0.1.0

• Package creation and initial release containing estimators, autoplot helpers, and reliability utilities.