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: 0.1.0
Depends: R (≥ 2.10)
Imports: magrittr, lubridate, dplyr, stats
Published: 2018-12-03
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
CRAN checks: clr results

Downloads:

Reference manual: clr.pdf
Package source: clr_0.1.0.tar.gz
Windows binaries: r-devel: clr_0.1.0.zip, r-release: clr_0.1.0.zip, r-oldrel: clr_0.1.0.zip
OS X binaries: r-release: clr_0.1.0.tgz, r-oldrel: clr_0.1.0.tgz

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