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

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.

Version: 1.0-2
Depends: R (≥ 3.0.2), glmnet
Imports: SIS, mlbench, RankAggreg, SiZer, lqa, qgraph
Published: 2015-11-17
Author: Pi Guo, Yuantao Hao
Maintainer: Pi Guo <guopi.01 at 163.com>
License: GPL-2
URL: https://www.researchgate.net/profile/Pi_Guo3
NeedsCompilation: no
CRAN checks: SparseLearner results

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Reference manual: SparseLearner.pdf
Package source: SparseLearner_1.0-2.tar.gz
Windows binaries: r-devel: SparseLearner_1.0-2.zip, r-release: SparseLearner_1.0-2.zip, r-oldrel: SparseLearner_1.0-2.zip
OS X El Capitan binaries: r-release: SparseLearner_1.0-2.tgz
OS X Mavericks binaries: r-oldrel: SparseLearner_1.0-2.tgz
Old sources: SparseLearner archive

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