gRbase: A package for graphical modelling in R. The gRbase package provides certain general constructs which are used by other graphical modelling packages, in particular by the packages gRain, gRim and gRc. gRbase contains several datasets relevant for use in connection with graphical models. Almost all datasets used in the book Graphical Models with R (2012) are contained in gRbase. gRbase implements several graph algorithms (based mainly on representing graphs as adjacency matrices - either in the form of a standard matrix or a sparse matrix). Some graph algorithms are: (i) maximum cardinality search (for marked and unmarked graphs). (ii) moralize. (iii) triangulate. (iv) junctionTree. gRbase facilities for array operations, gRbase implements functions for testing for conditional independence. gRbase illustrates how hierarchical log-linear models (hllm) may be implemented and describes concept of gmData (graphical meta data). These features, however, are not maintained anymore and remains in gRbase only because there exists a paper describing these facilities: A Common Platform for Graphical Models in R: The gRbase Package, Journal of Statistical Software, Vol 14, No 17, 2005.

References in zbMATH (referenced in 15 articles , 1 standard article )

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  1. Djordjilović, Vera; Chiogna, Monica: Searching for a source of difference in graphical models (2022)
  2. Peeters, C. F. W., Bilgrau, A. E., van Wieringen, W. N. : rags2ridges: A One-Stop-l2-Shop for Graphical Modeling of High-Dimensional Precision Matrices (2022) not zbMATH
  3. Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2022)
  4. Mads Lindskou, Søren Højsgaard, Poul Svante Eriksen, Torben Tvedebrink: sparta: Sparse Tables and their Algebra for use in High Dimensional Bayesian Networks (2021) arXiv
  5. Gilles Kratzer, Fraser Iain Lewis, Arianna Comin, Marta Pittavino, Reinhard Furrer: Additive Bayesian Network Modelling with the R Package abn (2019) arXiv
  6. Jones, Edmund; Didelez, Vanessa: Thinning a triangulation of a Bayesian network or undirected graph to create a minimal triangulation (2017)
  7. Perez-de-la-Cruz, Gonzalo; Eslava-Gomez, Guillermina: Discriminant analysis with Gaussian graphical tree models (2016)
  8. Bontempi, Gianluca; Flauder, Maxime: From dependency to causality: a machine learning approach (2015)
  9. Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2015)
  10. Markus Kalisch; Martin Mächler; Diego Colombo; Marloes Maathuis; Peter Bühlmann: Causal Inference Using Graphical Models with the R Package pcalg (2012) not zbMATH
  11. Marco Scutari: Learning Bayesian Networks with the bnlearn R Package (2009) arXiv
  12. Søren Højsgaard; Steffen Lauritzen: Inference in Graphical Gaussian Models with Edge and Vertex Symmetries with the gRc Package for R (2007) not zbMATH
  13. Giovanni Marchetti: Independencies Induced from a Graphical Markov Model After Marginalization and Conditioning: The R Package ggm (2006) not zbMATH
  14. Claus Dethlefsen; Søren Højsgaard: A Common Platform for Graphical Models in R: The gRbase Package (2005) not zbMATH
  15. Søren Højsgaard: The mimR Package for Graphical Modelling in R (2004) not zbMATH