`gRain`-
- gRain is an R package for probability propagation in graphical independence networks (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
- See also Højsgaard, Edwards Lauritzen (2012) Graphical Modelling with R. Springers UseR! series.

`gRim`-
- gRim is an R package for graphical interaction models (graphical log-linear models for discrete data, Gaussian graphical models for continuous data and Mixed interaction models for mixed data).
- See also Højsgaard, Edwards Lauritzen (2012) Graphical Modelling with R. Springers UseR! series.

`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.

`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.

- 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

- The packages listed above use the
`graph`,`RBGL`and`Rgraphviz`packages. These packages are NOT on`CRAN`but on`bioconductor`. Hence you MUST install these packages BEFORE installing the graphical modelling packages. To do so, execute these commands:`source("http://bioconductor.org/biocLite.R"); biocLite(c("graph","RBGL","Rgraphviz"))` - Then install the graphical modelling packages from
`CRAN`with:`install.packages("gRbase", dependencies=TRUE); install.packages("gRain", dependencies=TRUE); install.packages("gRim", dependencies=TRUE)`

- Specification of conditional probability tables (CPTs) here.
- Reference card for working with arrays in
`gRbase`here.

- Slides from tutorial on graphical models and Bayesian networks (using
`gRain`and`gRim`) at useR!2015 i Aalborg are available here. The tutorial was organized together with Therese Graversen, University of Copenhagen.

- A reproducible example in terms of a short code fragment.
- The data. The preferred way of sending the data "mydata" is to copy and paste the result from running
`dput(mydata)`. - The result of running the
`sessionInfo()`function.

**Q:**- Is it possible to specify likelihood evidence (also called
virtual evidence) in
`gRain`? **A:**- Yes, as of version 1.1-2 this has been implemented. The function to use is
`setEvidence()`. A vignette on the topic has also been added. Please report unexpected behaviour. **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 report sucesses and failures.
**Q:**- Does
`gRain`have support for Bayesian networks for variables that are (multivariate) normal? Or for mixtures of discrete and normal 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.
**Q:**- Does
`gRain`have support for Bayesian networks for variables that are not discrete (and with a finite state space)? **A:**- Not in full generality. However, using the likelihood evidence facilities, one can work with some types of non-discrete variables.

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