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 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:
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