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(<msaenet>)

Extract Model Coefficients

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

plot(<msaenet>)

Plot msaenet Model Objects

predict(<msaenet>)

Make Predictions from an msaenet Model

print(<msaenet>)

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)