R: Multi-Step Adaptive Estimation Methods for Sparse Regressions

# Multi-Step Adaptive Estimation Methods for Sparse Regressions

## Documentation for package ‘msaenet’ version 3.1.2

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

aenet | Adaptive Elastic-Net
amnet | Adaptive MCP-Net
asnet | Adaptive SCAD-Net
coef.msaenet | Extract Model Coefficients
msaenet | Multi-Step Adaptive Elastic-Net
msaenet.fn | Get the Number of False Negative Selections
msaenet.fp | Get the Number of False Positive Selections
msaenet.mae | Mean Absolute Error (MAE)
msaenet.mse | Mean Squared Error (MSE)
msaenet.nzv | Get Indices of Non-Zero Variables
msaenet.nzv.all | Get Indices of Non-Zero Variables in All Steps
msaenet.rmse | Root Mean Squared Error (RMSE)
msaenet.rmsle | Root Mean Squared Logarithmic Error (RMSLE)
msaenet.sim.binomial | Generate Simulation Data for Benchmarking Sparse Regressions (Binomial Response)
msaenet.sim.cox | Generate Simulation Data for Benchmarking Sparse Regressions (Cox Model)
msaenet.sim.gaussian | Generate Simulation Data for Benchmarking Sparse Regressions (Gaussian Response)
msaenet.sim.poisson | Generate Simulation Data for Benchmarking Sparse Regressions (Poisson Response)
msaenet.tp | Get the Number of True Positive Selections
msamnet | Multi-Step Adaptive MCP-Net
msasnet | Multi-Step Adaptive SCAD-Net
plot.msaenet | Plot msaenet Model Objects
predict.msaenet | Make Predictions from an msaenet Model
print.msaenet | Print msaenet Model Information