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SparseLearner (version 1.0-2)

Sparse Learning Algorithms Using a LASSO-Type Penalty for Coefficient Estimation and Model Prediction

Description

Coefficient estimation and model prediction based on the LASSO sparse learning algorithm and its improved versions such as Bolasso, bootstrap ranking LASSO, two-stage hybrid LASSO and others. These LASSO estimation procedures are applied in the fields of variable selection, graphical modeling and ensemble learning. The bagging LASSO model uses a Monte Carlo cross-entropy algorithm to determine the best base-level models and improve predictive performance.

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Version

Install

install.packages('SparseLearner')

Monthly Downloads

42

Version

1.0-2

License

GPL-2

Maintainer

Pi Guo

Last Published

November 17th, 2015

Functions in SparseLearner (1.0-2)

Bolasso

Bolasso model.
Predict.bagging

Make predictions for new data from a 'bagging' object.
Bagging.lasso

A Bagging Prediction Model Using LASSO Selection Algorithm.
Print.bagging

Print a bagging object.
TSLasso

Two-stage hybrid LASSO model.
BRLasso

Bootstrap ranking LASSO model.
Sparse.graph

Graphic Modeling Using LASSO-Type Sparse Learning Algorithm.
Plot.importance

Generate a plot of variable importance.
SGraph

Graphic Modeling Using LASSO-Type Sparse Learning Algorithm.