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


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.1.1 by Amandine Pierrot, a month 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