## JGL: Performs the Joint Graphical Lasso for sparse inverse covariance
estimation on multiple classes

The Joint Graphical Lasso is a generalized method for
estimating Gaussian graphical models/ sparse inverse covariance
matrices/ biological networks on multiple classes of data. We
solve JGL under two penalty functions: The Fused Graphical
Lasso (FGL), which employs a fused penalty to encourage inverse
covariance matrices to be similar across classes, and the Group
Graphical Lasso (GGL), which encourages similar network
structure between classes. FGL is recommended over GGL for
most applications.

Version: |
2.3 |

Depends: |
igraph |

Published: |
2013-04-16 |

Author: |
Patrick Danaher |

Maintainer: |
Patrick Danaher <pdanaher at uw.edu> |

License: |
GPL-2 |

NeedsCompilation: |
no |

CRAN checks: |
JGL results |

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