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) and (2015) . 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.

R package for Curve Linear Regression


clr 0.1.1

  • Fix small bug in clrdata(): to deal with Winter daylight saving times
  • Add precisions in clrdata() description

Reference manual

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0.1.2 by Amandine Pierrot, a year ago

Browse source code at

Authors: Amandine Pierrot with contributions and/or help from Qiwei Yao , Haeran Cho , Yannig Goude and Tony Aldon.

Documentation:   PDF Manual  

LGPL (>= 2.0) license

Imports magrittr, lubridate, dplyr, stats

See at CRAN