Packages for graphical modelling with R

1  Packages

gRbase

• gRbase provides efficient graph algorithms, functions for easy creation of graphs, functions for manipulation of highdimensional tables, data relevant to graphical models.
• gRbase does not provide modelling facilities. These facilities are provided by other packages (who depends on gRbase).
• The reference to gRbase is: Dethlefsen, C., Højsgaard, S. (2005) A Common Platform for Graphical Models in R: The gRbase Package. Journal of Statistical Software Vol. 14, No. 17.
• Vignettes (for online viewing):
  • gRbase-arrayops1: Primitive array operations in the gRbase package,
  • gRbase-arrayops2: Advanced array operations in the gRbase package,
  • gRbase-graphs: Graphs in the gRbase package

gRain

• gRain is an R package for probability propagation in graphical independence networks such as Bayesian networks.
• The reference to gRain is: Højsgaard, S. (2012) Graphical Independence Networks with the gRain Package for R. Journal of Statistical Software Vol. 46, No. 10. 1-26

gRim

• gRim is an R package for graphical interaction models.
• gRim implements graphical log-linear models for discrete data, Gaussian graphical models for continuous data and Mixed interaction models for mixed data.

gRc

• gRc is an R package for Inference in Graphical Gaussian Models with Edge and Vertex Symmetries
• The reference to gRc is: Højsgaard, Søren ; Lauritzen, Steffen L. (2007) Inference in Graphical Gaussian Models with Edge and Vertex Symmetries with the gRc Package for R. Journal of Statistical Software, Vol. 23, No. 6, 2007.
• A related paper is: Højsgaard, Søren ; Lauritzen, Steffen L. Graphical Gaussian models with edge and vertex symmetries. Journal of the Royal Statistical Society; Series B: Statistical Methodology, Vol. 70, No. 5, 2008, p. 1005-1027.

2  Books

• Højsgaard, Edwards Lauritzen (2012) Graphical Modelling with R. Springers UseR! series. This book contains several illustrations of the use of the gRbase, gRain and gRim packages.
  Errata list for Graphical Modelling with R.
• Edwards (2000) Introduction to Graphical Modelling. Springer
• Lauritzen (1996) Graphical Models. Oxford University Press

3  Development versions of the packages

4  Installation

1. You MUST use R version 2.15.2 (or later). This is ESSENTIAL.
2. The packages listed above use the graph, RBGL and Rgraphviz packages. These packages are NOT on CRAN but on bioconductor. To install these packages, execute
   source("http://bioconductor.org/biocLite.R"); biocLite(c("graph","RBGL","Rgraphviz"))
3. Then install the packages from CRAN in the usual way.

5  Reporting unexpected behaviours (bugs)

6  FAQ (frequently asked questions)

Q:

I have R version 2.15.1 (or earlier) installed. Must I really update to version 2.15.2 in order to use these packages?

A:

That is the easiest. If you make a fresh installation of 2.15.2 (or later) as described above then everything should work out of the box. If you use 2.15.0 or 2.15.1 you can also get things to work but you must then make sure to obtain the updated bioconductor packages yourself.

Q:

It used to be so that one should install the Graphviz program separately in order to use the Rgraphviz package. Is that still so?

A:

No. If you use R version 2.15.2 when installing the bioconductor packages, everything should work out of the box. (That is one reason why you are asked you to use version 2.15.2 or later).

Q:

I want to build a Bayesian network with 80.000 nodes. Can I do so with gRain?

A:

Work has been done on supporting large networks. Please let me know of sucesses and failures.

Q:

Does gRain have support for Bayesian networks based on the multivariate normal distribution and for mixed variables?

A:

No. Implementation for the multivariate normal distribution is straight forward (if you work with the canoncical rather than the moment parameters). Any contribution would be most welcome. For mixed variables, the only algorithm I know of is numerically unstable.