Functions for generating simulation data.

msaenet.sim.gaussian

Generate Simulation Data for Benchmarking Sparse Regressions (Gaussian Response)

msaenet.sim.binomial

Generate Simulation Data for Benchmarking Sparse Regressions (Binomial Response)

msaenet.sim.poisson

Generate Simulation Data for Benchmarking Sparse Regressions (Poisson Response)

msaenet.sim.cox

Generate Simulation Data for Benchmarking Sparse Regressions (Cox Model)

Functions for fitting adaptive and multi-step estimation models based on elastic-net, MCP-net, or SCAD-net penalties.

aenet

Adaptive Elastic-Net

msaenet

Multi-Step Adaptive Elastic-Net

amnet

Adaptive MCP-Net

msamnet

Multi-Step Adaptive MCP-Net

asnet

Adaptive SCAD-Net

msasnet

Multi-Step Adaptive SCAD-Net

msaenet-package

Multi-Step Adaptive Estimation Methods for Sparse Regressions

Functions for inspecting the fitted AENet/MSAENet models.

msaenet.nzv

Get Indices of Non-Zero Variables

msaenet.nzv.all

Get Indices of Non-Zero Variables in All Steps

msaenet.tp

Get the Number of True Positive Selections

msaenet.fp

Get the Number of False Positive Selections

msaenet.fn

Get the Number of False Negative Selections

coef

Extract Model Coefficients

Functions for plot, print, and make predictions based on the fitted AENet/MSAENet model.

plot

Plot msaenet Model Objects

predict

Make Predictions from an msaenet Model

print

Print msaenet Model Information

Utility functions for computing RMSE, MAE, and RMSLE.

msaenet.rmse

Root Mean Squared Error (RMSE)

msaenet.mse

Mean Squared Error (MSE)

msaenet.mae

Mean Absolute Error (MAE)

msaenet.rmsle

Root Mean Squared Logarithmic Error (RMSLE)