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AFSSEN

Adaptive Function-on-Scalar Smoothing Elastic Net

AFSSEN is a methodology that simultaneously select significant predicotrs and produce smooth estimates of their parameters in a function-on-scalar linear modelwith sub-Gaussian errors and high-dimensional predictors.

Documentation

For installing this package, use

devtools::install_github("ardeeshany/AFSSEN")

AFSSEN.R

We have option to control sparsity and smoothness separately with using two penalty parameters $\lambda_H$ and $\lambda_K$. We aim to estimate a smooth version of $\bf{\beta}$ to minimize the following target function.


The following AFFSEN() function helps us to estimate the smooth $\bf{\beta}$ and find the significant predictors: