Package: tsrobprep Title: Robust Preprocessing of Time Series Data Version: 0.3.2 Date: 2022-02-21 Authors@R: c( person(given = "Michał", family = "Narajewski", role = c("aut", "cre"), email = "michal.narajewski@uni-due.de", comment = c(ORCID = "https://orcid.org/0000-0002-3115-0162")), person(given = "Jens", family = "Kley-Holsteg", role = "aut", email = "jens.kley-holsteg@hs-ruhrwest.de"), person(given = "Florian", family = "Ziel", role = "aut", email = "florian.ziel@uni-due.de", comment = c(ORCID = "https://orcid.org/0000-0002-2974-2660"))) Description: Methods for handling the missing values outliers are introduced in this package. The recognized missing values and outliers are replaced using a model-based approach. The model may consist of both autoregressive components and external regressors. The methods work robust and efficient, and they are fully tunable. The primary motivation for writing the package was preprocessing of the energy systems data, e.g. power plant production time series, but the package could be used with any time series data. For details, see Narajewski et al. (2021) . Depends: R (>= 3.2.0) License: MIT + file LICENSE Encoding: UTF-8 Imports: glmnet, MASS, Matrix, mclust, quantreg, Rdpack, splines, textTinyR, zoo RdMacros: Rdpack LazyData: true RoxygenNote: 7.1.1 NeedsCompilation: no Packaged: 2026-10-12 10:15:05 UTC; root Author: Michał Narajewski [aut, cre] (), Jens Kley-Holsteg [aut], Florian Ziel [aut] () Maintainer: Michał Narajewski Repository: https://cran.r-universe.dev Date/Publication: 2022-02-22 09:30:01 UTC RemoteUrl: https://github.com/cran/tsrobprep RemoteRef: HEAD RemoteSha: 73706963112128ad96fdf4b071666587d40a23fc