FRegSigCom: Functional Regression using Signal Compression Approach

Signal compression methods for functional regression. It includes various function-on-function (FOF) regression models such as the linear FOF model with functional response and both scalar and functional predictors for a small number of functional predictors, linear FOF models with a large number of functional predictors, linear FOF model for spiky data, stepwise selection for FOF models with two-way interactions, and nonlinear FOF models. It also includes scalar-on-function regression models with single (SOF) or multivariate (mSOF) scalar response variable, and SOF model for spiky data.

Version: 0.3.0
Depends: R (≥ 2.10), fda, Matrix
Imports: Rcpp
LinkingTo: Rcpp, RcppEigen
Suggests: refund, MASS, wavethresh
Published: 2018-11-15
Author: Ruiyan Luo, Xin Qi
Maintainer: Ruiyan Luo <rluo at gsu.edu>
License: GPL-2
URL: http://sites.gsu.edu/rluo/, http://sites.gsu.edu/xqi3/
NeedsCompilation: yes
CRAN checks: FRegSigCom results

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Reference manual: FRegSigCom.pdf
Package source: FRegSigCom_0.3.0.tar.gz
Windows binaries: r-devel: FRegSigCom_0.3.0.zip, r-devel-gcc8: FRegSigCom_0.3.0.zip, r-release: FRegSigCom_0.3.0.zip, r-oldrel: FRegSigCom_0.3.0.zip
OS X binaries: r-release: FRegSigCom_0.3.0.tgz, r-oldrel: FRegSigCom_0.3.0.tgz
Old sources: FRegSigCom archive

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