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A graphical model is a multivariate (potentially very high dimensional)probabilistic model, which is formed by combining lower dimensional components.Inference (computation of conditional probabilities) is based on messagepassing algorithms that utilize conditional independence structures. Ingraphical models for discrete variables with finite state spaces, there is afundamental problem in high dimensions: A discrete distribution is representedby a table of values, and in high dimensions such tables can becomeprohibitively large. In inference, such tables must be multiplied which canlead to even larger tables. The sparta package meets this challenge byimplementing methods that efficiently handles multiplication andmarginalization of sparse tables. The package was written in the R programminglanguage and is freely available from the Comprehensive R Archive Network(CRAN). The companion package jti, also on CRAN, was developed to showcase thepotential of sparta in connection to the Junction Tree Algorithm. We show, thatjti is able to handle highly complex graphical models which are otherwiseinfeasible due to lack of computer memory, using sparta as a backend for tableoperations.

sparta: Sparse Tables and their Algebra with a View Towards High Dimensional Graphical Models