Package: elcf4R Title: Electricity Load Curves Forecasting at Individual Level Version: 0.4.1 Date: 2026-04-07 Depends: R (>= 3.5.0) Imports: stats, utils, mgcv, earth, keras3, Rcpp, tensorflow, data.table, wavelets, jsonlite, xml2, DBI, RSQLite LinkingTo: Rcpp Suggests: knitr, reticulate, rmarkdown, testthat (>= 3.0.0) Authors@R: c( person(given = "Frederic", family= "Bertrand", role = c("cre", "aut"), email = "frederic.bertrand@lecnam.net", comment = c(ORCID = "0000-0002-0837-8281")), person(given = "Fatima", family= "Fahs", role = c("aut"), email = "fatima.fahs@es.fr"), person(given = "Myriam", family= "Maumy-Bertrand", role = c("aut"), email = "myriam.maumy@ehesp.fr", comment = c(ORCID = "0000-0002-4615-1512"))) Author: Frederic Bertrand [cre, aut] (), Fatima Fahs [aut], Myriam Maumy-Bertrand [aut] () Maintainer: Frederic Bertrand Description: Implements forecasting methods for individual electricity load curves, including Kernel Wavelet Functional (KWF), clustered KWF, Generalized Additive Models (GAM), Multivariate Adaptive Regression Splines (MARS), and Long Short-Term Memory (LSTM) models. Provides normalized dataset adapters for iFlex, StoreNet, Low Carbon London, and REFIT; download and read support for IDEAL and GX; explicit Python backend selection for TensorFlow-based LSTM fits; helpers for daily segmentation and rolling-origin benchmarking; and compact shipped example panels and benchmark-result datasets. LazyLoad: yes LazyData: yes VignetteBuilder: knitr License: GPL-3 Encoding: UTF-8 URL: https://fbertran.github.io/elcf4R/, https://github.com/fbertran/elcf4R BugReports: https://github.com/fbertran/elcf4R/issues Roxygen: list(markdown = TRUE) RoxygenNote: 7.3.3 NeedsCompilation: yes Config/pak/sysreqs: libpng-dev libxml2-dev python3 Repository: https://fbertran.r-universe.dev Date/Publication: 2026-04-22 17:56:18 UTC RemoteUrl: https://github.com/fbertran/elcf4r RemoteRef: HEAD RemoteSha: fe13164be406588dd19c5a9bfaf49b47455fa93c Packaged: 2026-06-21 10:45:08 UTC; root