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SGPR (version 0.1.2)

Sparse Group Penalized Regression for Bi-Level Variable Selection

Description

Fits the regularization path of regression models (linear and logistic) with additively combined penalty terms. All possible combinations with Least Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP) and Exponential Penalty (EP) are supported. This includes Sparse Group LASSO (SGL), Sparse Group SCAD (SGS), Sparse Group MCP (SGM) and Sparse Group EP (SGE). For more information, see Buch, G., Schulz, A., Schmidtmann, I., Strauch, K., & Wild, P. S. (2024) .

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Version

Install

install.packages('SGPR')

Monthly Downloads

147

Version

0.1.2

License

GPL (>= 3)

Maintainer

Gregor Buch

Last Published

May 16th, 2024

Functions in SGPR (0.1.2)

process.lambda

Set up a lambda sequence
predict.sgp.cv

Predictions based on a SGP models
coef.sgp

Coefficients from an SGP model
coef.sgp.cv

Coefficients from SGP models
plot.sgp

Plots the coefficient path of an SGP object
process.X

Process X for a sparse group penalty
SGPR-package

SGPR: Sparse Group Penalized Regression for Bi-Level Variable Selection
process.Z

Process Z for a sparse group penalty
plot.sgp.cv

Plots the cross-validation curve from a SGP object
predict.sgp

Predictions based on a SGP model
get.loss

A function that calculates the loss/cost
process.penalty

Process the arguments about the sparse group penalty
sgp.cv

Cross-validation for sparse group penalties
sgp

Fit a sparse group regularized regression path
process.y

Process y for a sparse group penalty
process.group

Process groupings for a sparse group penalty