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.

Install the latest version of this package by entering the following in R:

`install.packages("SparseLearner")`

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 |

https://www.researchgate.net/profile/Pi_Guo3 |

**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.

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
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