A new methodology for linear regression with both curve response and curve regressors, which is described in Cho, Goude, Brossat and Yao (2013) <doi:10.1080/01621459.2012.722900> and (2015) <doi:10.1007/978-3-319-18732-7_3>. The key idea behind this methodology is dimension reduction based on a singular value decomposition in a Hilbert space, which reduces the curve regression problem to several scalar linear regression problems.
Version: 0.1.2 Depends: R (≥ 2.10) Imports: magrittr, lubridate, dplyr, stats Published: 2019-07-29 DOI: 10.32614/CRAN.package.clr Author: Amandine Pierrot with contributions and/or help from Qiwei Yao, Haeran Cho, Yannig Goude and Tony Aldon. Maintainer: Amandine Pierrot <amandine.m.pierrot at gmail.com> License: LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL (≥ 2.0)] Copyright: EDF R&D 2017 NeedsCompilation: no Materials: README, NEWS CRAN checks: clr results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=clr to link to this page.
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