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 |
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 Mavericks binaries: | r-release: SparseLearner_1.0-2.tgz, r-oldrel: SparseLearner_1.0-2.tgz |
Old sources: | SparseLearner archive |
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