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-Package: agghoo
-Title: Aggregated Hold-Out Cross Validation
-Date: 2022-08-30
-Version: 0.1-0
-Description: The 'agghoo' procedure is an alternative to usual cross-validation.
-    Instead of choosing the best model trained on V subsamples, it determines
-    a winner model for each subsample, and then aggregate the V outputs.
-    For the details, see "Aggregated hold-out" by Guillaume Maillard,
-    Sylvain Arlot, Matthieu Lerasle (2021) <arXiv:1909.04890>
-    published in Journal of Machine Learning Research 22(20):1--55.
-Author: Sylvain Arlot <sylvain.arlot@universite-paris-saclay.fr> [cph,ctb],
-    Benjamin Auder <benjamin.auder@universite-paris-saclay.fr> [aut,cre,cph],
-    Melina Gallopin <melina.gallopin@universite-paris-saclay.fr> [cph,ctb],
-    Matthieu Lerasle <matthieu.lerasle@universite-paris-saclay.fr> [cph,ctb],
-    Guillaume Maillard <guillaume.maillard@uni.lu> [cph,ctb]
-Maintainer: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr>
-Depends: R (>= 3.5.0)
-Imports: class, parallel, R6, rpart, FNN
-Suggests: roxygen2
-URL: https://git.auder.net/?p=agghoo.git
-License: MIT + file LICENSE
-RoxygenNote: 7.2.1
-Collate: 'compareTo.R' 'agghoo.R' 'R6_AgghooCV.R' 'R6_Model.R'
-        'checks.R' 'utils.R' 'A_NAMESPACE.R'
-NeedsCompilation: no
-Packaged: 2022-09-09 15:45:56 UTC; auder
-Built: R 4.2.1; ; 2022-09-09 15:46:05 UTC; unix