# clr v0.1.0

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## 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.

## Readme

# clr

R package for Curve Linear Regression

## Functions in clr

Name | Description | |

clust_train | Electricity load example: clusters on train set | |

clrdata | Create an object of clrdata | |

clr | Curve Linear Regression via dimension reduction | |

predict.clr | Prediction from fitted CLR model(s) | |

gb_load | Electricity load from Great Britain | |

clr-package | Curve Linear Regression | |

clust_test | Electricity load example: clusters on test set | |

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## Details

Type | Package |

Copyright | EDF R&D 2017 |

License | LGPL (>= 2.0) |

Encoding | UTF-8 |

LazyData | true |

RoxygenNote | 6.1.1 |

NeedsCompilation | no |

Packaged | 2018-11-28 23:16:59 UTC; amandinepierrot |

Repository | CRAN |

Date/Publication | 2018-12-03 12:12:44 UTC |

imports | dplyr , lubridate , magrittr , stats |

depends | R (>= 2.10) |

Contributors | Amandine Pierrot |

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