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 |
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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