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

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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.

Author
Pi Guo, Yuantao Hao
Date of publication
2015-11-17 14:40:37
Maintainer
Pi Guo <guopi.01@163.com>
License
GPL-2
Version
1.0-2
URLs

View on CRAN

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.

Files in this package

SparseLearner
SparseLearner/NAMESPACE
SparseLearner/R
SparseLearner/R/Print.bagging.R
SparseLearner/R/Sparse.graph.R
SparseLearner/R/Plot.importance.R
SparseLearner/R/TSLasso.R
SparseLearner/R/Bagging.lasso.R
SparseLearner/R/BRLasso.R
SparseLearner/R/Predict.bagging.R
SparseLearner/R/Bolasso.R
SparseLearner/MD5
SparseLearner/DESCRIPTION
SparseLearner/man
SparseLearner/man/Predict.bagging.Rd
SparseLearner/man/Bolasso.Rd
SparseLearner/man/Sparse.graph.Rd
SparseLearner/man/Plot.importance.Rd
SparseLearner/man/Print.bagging.Rd
SparseLearner/man/BRLasso.Rd
SparseLearner/man/Bagging.lasso.Rd
SparseLearner/man/TSLasso.Rd