R packages:
 A. FRegSigCom (Functional Regression using Signal Compression Approach). Current version: 0.3.0. The pdf manual is here. This package implements:
(a). the signal compression approach for linear functiononfunction regression proposed in the following paper (1);
(b). the method for nonlinear functiononfunction regression model proposed in the following paper (2);
(c). the smoothsparse approach for linear functiononfunction regression model in the following paper (3), where the number of predictor curves can be much larger than the sample size;
(d). the interaction functiononfunction regression model and stepwise model selection in the following paper (4).
The pdf manual is here.
(e). the methods for scalaronfunction and functiononfunction regression models with densely observed spiky functional data in the paper (5).
(f). the methods for functional regression models with multivariate response and multiple or even thousands of predictor curves in the paper (6).
(g). the methods for functiononfunction regression models using wavelet transformation in the paper (7).
Reference:


 (1). Ruiyan Luo and Xin Qi (2017) Functiononfunction linear regression by signal compression. Journal of American Statistical Association. 112(518): 690705.
 (2). Xin Qi and Ruiyan Luo. (2019) Nonlinear additive function on function with multiple predictor curves. Statistica Sinica, 29, 719739.
 (3). Xin Qi, and Ruiyan Luo. (2018) Function on function regression with thousands of predictive curves. Journal of Multivariate Analysis, 163: 5166.
 (4). Ruiyan Luo and Xin Qi. (2019) Interaction model and model selection for functiononfunction regression. Journal of Computational and Graphical Statistics, Volume 28, Issue 2, 309322.
 (5). Xin Qi and Ruiyan Luo. Functional regression for highly densely observed data with novel regularization. (Under review).
 (6). Xin Qi and Ruiyan Luo, Functional regression with multivariate response. (Under review).
 (7). Ruiyan Luo and Xin Qi. (2016) Functional wavelet regression for functiononfunction linear models. Electronic Journal of Statistics. 10(2): 31793216

 B. SiER (Signal Extraction Approach for Sparse Multivariate Response Regression). This package implements the signal extraction approach for linear regression proposed in the following paper, where the predictor is a highdimensional multivariate variable and the response is either a scalar variable or a multivariate variable. The pdf manual is here.
Reference:

 Ruiyan Luo and Xin Qi. (2017) Signal extraction approach for sparse multivariate response regression. Journal of Multivariate Analysis. 153: 83–97.


R codes:

 Wavelet based signal compression for linear functiononfunction regression (zip file here). This file contains the R code for all the R functions, simulations and applications to real data in the following paper. We implement a wavelet based method for functiononfunction regression model, where we apply wavelet transformation to multiple predictor curves and convert the functiononfunction regression to the function on highdimensional multivariate wavelet coefficients regression.
Reference: Ruiyan Luo, and Xin Qi.(2016) Functional wavelet regression for functiononfunction linear models. Electronic Journal of Statistics. 10(2):31793216.
 Wavelet based signal compression for linear functiononfunction regression (zip file here). This file contains the R code for all the R functions, simulations and applications to real data in the following paper. We implement a wavelet based method for functiononfunction regression model, where we apply wavelet transformation to multiple predictor curves and convert the functiononfunction regression to the function on highdimensional multivariate wavelet coefficients regression.