clr: Curve Linear Regression via Dimension Reduction

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>
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


Reference manual: clr.pdf


Package source: clr_0.1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): clr_0.1.2.tgz, r-oldrel (arm64): clr_0.1.2.tgz, r-release (x86_64): clr_0.1.2.tgz, r-oldrel (x86_64): clr_0.1.2.tgz
Old sources: clr archive


Please use the canonical form to link to this page.