An implementation for the multi-task Gaussian processes with common mean framework. Two main algorithms, called 'Magma' and 'MagmaClust', are available to perform predictions for supervised learning problems, in particular for time series or any functional/continuous data applications. The corresponding articles has been respectively proposed by Arthur Leroy, Pierre Latouche, Benjamin Guedj and Servane Gey (2022) <doi:10.1007/s10994-022-06172-1>, and Arthur Leroy, Pierre Latouche, Benjamin Guedj and Servane Gey (2023) <https://jmlr.org/papers/v24/20-1321.html>. Theses approaches leverage the learning of cluster-specific mean processes, which are common across similar tasks, to provide enhanced prediction performances (even far from data) at a linear computational cost (in the number of tasks). 'MagmaClust' is a generalisation of 'Magma' where the tasks are simultaneously clustered into groups, each being associated to a specific mean process. User-oriented functions in the package are decomposed into training, prediction and plotting functions. Some basic features (classic kernels, training, prediction) of standard Gaussian processes are also implemented.
Version: 1.2.1 Depends: R (≥ 2.10) Imports: broom, dplyr, ggplot2, magrittr, methods, mvtnorm, plyr, purrr, Rcpp, rlang, stats, tibble, tidyr, tidyselect LinkingTo: Rcpp Suggests: gganimate, gifski, gridExtra, knitr, plotly, png, rmarkdown, testthat (≥ 3.0.0), transformr Published: 2024-06-28 DOI: 10.32614/CRAN.package.MagmaClustR Author: Arthur Leroy [aut, cre], Pierre Latouche [aut], Pierre Pathé [ctb], Alexia Grenouillat [ctb], Hugo Lelievre [ctb] Maintainer: Arthur Leroy <arthur.leroy.pro at gmail.com> BugReports: https://github.com/ArthurLeroy/MagmaClustR/issues License: MIT + file LICENSE URL: https://github.com/ArthurLeroy/MagmaClustR, https://arthurleroy.github.io/MagmaClustR/ NeedsCompilation: yes Materials: README NEWS CRAN checks: MagmaClustR results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=MagmaClustR to link to this page.
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