JGL2-package: JGL2: Joint Graphical Lasso, select the lambda parameters...
In GiulioCostantini/JGL2: JGL2: Joint Graphical Lasso, select the lambda parameters using AIC or crossvalidation
Description
Author(s)
References
See Also
Description
This package selects the lambda parameters for the Joint grapical lasso with different methods, based on AIC or on k-fold crossvalidation
Author(s)
Giulio Costantini [aut, cre]
References
Danaher, P., Wang, P., and Witten, D. M. (2014). The joint graphical lasso for inverse covariance estimation across multiple classes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(2), 373-397. http://doi.org/10.1111/rssb.12033
Guo, J., Levina, E., Michailidis, G., and Zhu, J. (2011). Joint estimation of multiple graphical models. Biometrika, 98(1), 1-15. http://doi.org/10.1093/biomet/asq060
See Also
GiulioCostantini/JGL2 documentation built on May 6, 2019, 6:29 p.m.
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Description Author(s) References See Also
Description
This package selects the lambda parameters for the Joint grapical lasso with different methods, based on AIC or on k-fold crossvalidation
Author(s)
Giulio Costantini [aut, cre]
References
Danaher, P., Wang, P., and Witten, D. M. (2014). The joint graphical lasso for inverse covariance estimation across multiple classes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(2), 373-397. http://doi.org/10.1111/rssb.12033 Guo, J., Levina, E., Michailidis, G., and Zhu, J. (2011). Joint estimation of multiple graphical models. Biometrika, 98(1), 1-15. http://doi.org/10.1093/biomet/asq060
See Also
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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