gRc
gRc: Inference in Graphical Gaussian Models with Edge and Vertex Symmetries , Estimation, model selection and other aspects of statistical inference in Graphical Gaussian models with edge and vertex symmetries (Graphical Gaussian models with colours)
(Source: http://cran.r-project.org/web/packages)
Keywords for this software
References in zbMATH (referenced in 8 articles , 1 standard article )
Showing results 1 to 8 of 8.
Sorted by year (- Vinciotti, Veronica; Augugliaro, Luigi; Abbruzzo, Antonino; Wit, Ernst C.: Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks (2016)
- Forbes, Peter G.M.; Lauritzen, Steffen: Linear estimating equations for exponential families with application to Gaussian linear concentration models (2015)
- Gehrmann, Helene; Lauritzen, Steffen L.: Estimation of means in graphical Gaussian models with symmetries (2012)
- Kiiveri, Harri; de Hoog, Frank: Fitting very large sparse Gaussian graphical models (2012)
- Shah, Parikshit; Chandrasekaran, Venkat: Group symmetry and covariance regularization (2012)
- Uhler, Caroline: Geometry of maximum likelihood estimation in Gaussian graphical models (2012)
- Sturmfels, Bernd; Uhler, Caroline: Multivariate Gaussians, semidefinite matrix completion, and convex algebraic geometry (2010)
- Høsgaard, Søren: Graphical models for sparse data: graphical Gaussian models with vertex and edge symmetries (2008)