SparseLearner: Sparse Learning Algorithms Using a LASSO-Type Penalty for Coefficient Estimation and Model Prediction
Version 1.0-2

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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AuthorPi Guo, Yuantao Hao
Date of publication2015-11-17 14:40:37
MaintainerPi Guo <guopi.01@163.com>
LicenseGPL-2
Version1.0-2
URL https://www.researchgate.net/profile/Pi_Guo3
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("SparseLearner")

Man pages

Bagging.lasso: A Bagging Prediction Model Using LASSO Selection Algorithm.
Bolasso: Bolasso model.
BRLasso: Bootstrap ranking LASSO model.
Plot.importance: Generate a plot of variable importance.
Predict.bagging: Make predictions for new data from a 'bagging' object.
Print.bagging: Print a bagging object.
Sparse.graph: Graphic Modeling Using LASSO-Type Sparse Learning Algorithm.
TSLasso: Two-stage hybrid LASSO model.

Functions

BRLasso Man page Source code
Bagging.lasso Man page Source code
Bolasso Man page Source code
Plot.importance Man page Source code
Predict.bagging Man page Source code
Print.bagging Man page Source code
Sparse.graph Man page Source code
TSLasso Man page Source code

Files

NAMESPACE
R
R/Print.bagging.R
R/Sparse.graph.R
R/Plot.importance.R
R/TSLasso.R
R/Bagging.lasso.R
R/BRLasso.R
R/Predict.bagging.R
R/Bolasso.R
MD5
DESCRIPTION
man
man/Predict.bagging.Rd
man/Bolasso.Rd
man/Sparse.graph.Rd
man/Plot.importance.Rd
man/Print.bagging.Rd
man/BRLasso.Rd
man/Bagging.lasso.Rd
man/TSLasso.Rd
SparseLearner documentation built on May 29, 2017, 9:18 p.m.