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 |
| Reference manual: | clr.pdf |
| Package source: | clr_0.1.2.tar.gz |
| Windows binaries: | r-devel: clr_0.1.2.zip, r-release: clr_0.1.2.zip, r-oldrel: clr_0.1.2.zip |
| macOS binaries: | r-release: clr_0.1.2.tgz, r-oldrel: clr_0.1.2.tgz |
| Old sources: | clr archive |
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