clr: Curve Linear Regression via Dimension Reduction (original) (raw)
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 formhttps://CRAN.R-project.org/package=clrto link to this page.