fdaSP-package {fdaSP}

Sparse Functional Data Analysis Methods

Description

Provides algorithms to fit linear regression models under several popular penalization techniques and functional linear regression models based on Majorizing-Minimizing (MM) and Alternating Direction Method of Multipliers (ADMM) techniques. See Boyd et al (2010) <doi:10.1561/2200000016> for complete introduction to the method.

Package Content

Index of help topics:

confband                Function to plot the confidence bands
f2fSP                   Overlap Group Least Absolute Shrinkage and
                        Selection Operator for function-on-function
                        regression model
f2fSP_cv                Cross-validation for Overlap Group Least
                        Absolute Shrinkage and Selection Operator for
                        function-on-function regression model
f2sSP                   Overlap Group Least Absolute Shrinkage and
                        Selection Operator for scalar-on-function
                        regression model
f2sSP_cv                Cross-validation for Overlap Group Least
                        Absolute Shrinkage and Selection Operator on
                        scalar-on-function regression model
fdaSP-package           Sparse Functional Data Analysis Methods
lmSP                    Sparse Adaptive Overlap Group Least Absolute
                        Shrinkage and Selection Operator
lmSP_cv                 Cross-validation for Sparse Adaptive Overlap
                        Group Least Absolute Shrinkage and Selection
                        Operator
softhresh               Function to solve the soft thresholding problem

Maintainer

Mauro Bernardi <[e-mail]>

Author(s)

Mauro Bernardi [aut, cre], Marco Stefanucci [aut], Antonio Canale [ctb]