elcf4R-package          Forecasting Individual Electricity Load Curves
elcf4r_assign_kwf_clusters
                        Assign segments to a fitted KWF clustering
                        model
elcf4r_benchmark        Run a rolling-origin benchmark on a normalized
                        panel
elcf4r_build_benchmark_index
                        Build a day-level benchmark index from a
                        normalized panel
elcf4r_build_daily_segments
                        Build daily load-curve segments from a
                        normalized panel
elcf4r_calendar_groups
                        Derive deterministic KWF calendar groups
elcf4r_classify_thermosensitivity
                        Classify thermosensitivity from daily load data
elcf4r_download_elmas   Download the ELMAS dataset from figshare
elcf4r_download_gx      Download selected GX dataset components
elcf4r_download_ideal   Download selected IDEAL dataset components
elcf4r_download_sgsc    Download the Smart-Grid Smart-City
                        interval-reading archive
elcf4r_download_storenet
                        Download one or more StoreNet household files
                        from figshare
elcf4r_elmas_toy        Toy subset of ELMAS hourly cluster profiles
elcf4r_fit_gam          Fit a GAM model for load curves
elcf4r_fit_kwf          Fit a Kernel Wavelet Functional model for daily
                        load curves
elcf4r_fit_kwf_clustered
                        Fit a clustered KWF model for daily load curves
elcf4r_fit_lstm         Fit an LSTM model for daily load curves
elcf4r_fit_mars         Fit a MARS model for load curves
elcf4r_iflex_benchmark_index
                        iFlex benchmark index of complete
                        participant-days
elcf4r_iflex_benchmark_results
                        iFlex benchmark results for shipped forecasting
                        methods
elcf4r_iflex_example    iFlex example panel for package examples
elcf4r_kwf_cluster_days
                        Cluster daily segments for clustered KWF
elcf4r_lcl_benchmark_results
                        Low Carbon London benchmark results for shipped
                        forecasting methods
elcf4r_lcl_example      Low Carbon London example panel for package
                        examples
elcf4r_metrics          Forecast accuracy metrics for load curves
elcf4r_normalize_panel
                        Normalize a load panel to the elcf4R schema
elcf4r_read_gx          Read and normalize the GX residential
                        transformer-level scaffold
elcf4r_read_ideal       Read and normalize the IDEAL hourly
                        aggregate-electricity scaffold
elcf4r_read_iflex       Read and normalize the iFlex hourly dataset
elcf4r_read_lcl         Read and normalize the Low Carbon London
                        dataset
elcf4r_read_refit       Read and normalize the REFIT cleaned household
                        dataset
elcf4r_read_sgsc        Read and normalize the Smart-Grid Smart-City
                        interval-reading dataset
elcf4r_read_storenet    Read and normalize the StoreNet household
                        dataset
elcf4r_refit_benchmark_results
                        REFIT benchmark results for shipped forecasting
                        methods
elcf4r_refit_example    REFIT example panel for package examples
elcf4r_sgsc_example     Compact Smart-Grid Smart-City example panel
elcf4r_storenet_benchmark_results
                        StoreNet benchmark results for shipped
                        forecasting methods
elcf4r_storenet_example
                        StoreNet example panel for package examples
elcf4r_use_tensorflow_env
                        Select the Python environment used for
                        TensorFlow-backed LSTM fits
predict.elcf4r_kwf_clusters
                        Assign new segments to a fitted KWF clustering
                        model
predict.elcf4r_model    Predict from an 'elcf4r_model'
