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Check the irrepresentable condition (IRC) in both L1-regularized regression <doi:10.1109/TIT.2006.883611> and Gaussian graphical models. The IRC requires that the important and unimportant variables are not correlated, at least not all that much, and it is necessary for consistent model selection. Exploring the IRC as a function of the number of variables, assumed sparsity, and effect size can provide valuable insights into the model selection properties of L1-regularization.
| Version: | 1.0.0 |
| Imports: | glmnet, MASS, Rdpack, GGMncv, corpcor, parallel |
| Published: | 2021-04-09 |
| Author: | Donald Williams [aut, cre] |
| Maintainer: | Donald Williams <drwwilliams at ucdavis.edu> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | IRCcheck results |
| Reference manual: | IRCcheck.pdf |
| Package source: | IRCcheck_1.0.0.tar.gz |
| Windows binaries: | r-devel: IRCcheck_1.0.0.zip, r-release: IRCcheck_1.0.0.zip, r-oldrel: IRCcheck_1.0.0.zip |
| macOS binaries: | r-release (arm64): IRCcheck_1.0.0.tgz, r-oldrel (arm64): IRCcheck_1.0.0.tgz, r-release (x86_64): IRCcheck_1.0.0.tgz, r-oldrel (x86_64): IRCcheck_1.0.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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