Title: Functional Regression using Signal Compression Approach
Description: Signal compression methods for function-on-function (FOF) regression with functional response and functional predictors, including linear models with both scalar and functional predictors for a small number of functional predictors, linear models with functional predictors for a large number of functional predictors, and nonlinear models. Ruiyan Luo and Xin Qi (2017) <doi:10.1080/01621459.2016.1164053>.

Description: Signal compression methods for function-on-function (FOF) regression with functional response and functional predictors, including linear models with both scalar and functional predictors for a small number of functional predictors, linear models with functional predictors for a large number of functional predictors, stepwise selection for FOF models with two-way interactions, and nonlinear models. References are linked to via the URL below.

Description: Signal compression methods for function-on-function regression with functional response and functional predictors, including linear models with both scalar and functional predictors for a small number of functional predictors, linear models with functional predictors for a large number of functional predictors, stepwise selection for FOF models with two-way interactions, and nonlinear models. References are linked to via the URL below.

Description: 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.
Collate: 'ffsig.R' 'ff.wave.sigcom.R' 'ff.spike.R' 'ff.nonlinear.R'