The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

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.

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.