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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:10.1007/9783319187327_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.
Package details 


Author  Amandine Pierrot with contributions and/or help from Qiwei Yao, Haeran Cho, Yannig Goude and Tony Aldon. 
Maintainer  Amandine Pierrot <[email protected]> 
License  LGPL (>= 2.0) 
Version  0.1.1 
Package repository  View on CRAN 
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