Macaulay2 package for computing the maximum likelihood estimates of Gaussian graphical models
Authors: Carlos Amendola, Luis David Garcia Puente, Roser Homs Pons, Olga Kuznetsova, Harshit J Motwani, Elina Robeva, David Swinarski.
Graphical Models MLE is a package for algebraic statistics that broadens the functionalities of GraphicalModels. It computes the maximum likelihood estimates (MLE) of the covariance matrix of Gaussian graphical models associated to loopless mixed graphs(LMG).
The main features of the package are the computation of the sampleCovarianceMatrix of sample data, the ideal generated by scoreEquations of log-likelihood functions of Gaussian graphical model, the MLdegree of such models and the MLE for the covariance or concentration matrix via solverMLE.
For more details on the type of graphical models that are accepted see gaussianRing. In particular, for further information about LMG with undirected, directed and bidirected edges, check partitionLMG.
This repository contains version 1.0 of the package, which will be available in the Macaulay2 distribution version 1.20 (May 2022). Since distribution 1.17, version 0.3 of GraphicalModelsMLE is available and its documentation can be found here: