[R--gR] Package for Discrete Bayesian Networks
If you have access to Hugin then you may find the RHugin package useful. http://rhugin.r-forge.r-project.org/ It provides an R API for the Hugin Decision Engine. Cheers, Kjell
On Mar 27, 2009, at 3:45 PM, Thomas Liebig wrote:
Hi Matthias, I also want to utilise large discrete Bayesian Networks with R (for spatial applications). Actually I learn the networks outside R with some selfmade tools and am looking for a package that offers me basic inference and sampling functions afterwards. Deal doesnt provide them. Claus Dethlefsen suggested to use Rhugs instead that provides an interface to hugin. Therefore, i'm currently implementing these two R functions (inference, sampling) plus an additional import function. For your application you would also need a fast Network Search algorithm for large networks. Perhaps the 'Sparse Candidate Algorithm', 'Scalable Sparse Bayesian Network Search' or the 'Screen Based Network Search' as deal only provides the poor Greedy Search for Bayesian Networks. Feel free to contact me in case, you are interested in exchanging functions or experience. Your application sounds also very interesting to me, and i would be happy to learn more about your textmining domain. regards, Thomas Mat_1 at gmx.net schrieb:
All, I would like to apply Bayesian networks to text-based information. That is, I would like to use Bayesian networks to extract 'sentiment' (say, 'good' vs. 'bad') form unstructured text.
From the CRAN Task View on gRaphical Models in R it seems that I should use the combination of the two packages deal and gRain (please correct me if I overlooked something). The package deal can work both with discrete and continuous variables. For my project I only need to work with discrete variables. Due to the large amount of data that I potentially have to handle, I am concerned that maybe deal will not run fast enough because it is 'too general' for my purpose.
In this sense, I would like to ask the following: is there an R package that I overlooked and that is tailored towards Bayesian networks with discrete variables? Many thanks, Matthias
-- Thomas Liebig Fraunhofer-Institut f?r Intelligente Analyse- und Informationssysteme (IAIS) Schloss Birlinghoven, D-53754 Sankt Augustin, Germany Email: thomas.liebig at iais.fraunhofer.de Phone: +49 2241 142050 Fax: +49 2241 142072
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