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.

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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

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