An implementation of the Bayesian Spatial Bradley–Terry (BSBT) model. It can be used to investigate data sets where judges compared different spatial areas. It constructs a network to describe how the areas are connected, and then places a correlated prior distribution on the quality parameter for each area, based on the network. The package includes MCMC algorithms to estimate the quality parameters.
The covariance functions can be used to construct the Multivariate Normal prior distribution. The prior distribution includes a constraint, where a linear combination of the parameters can be specified. There are two functions:
constrained_adjacency_covariance_function creates a covariance matrix
using a network based metric, and
constrained_covariance_function creates a matrix using the Euclidean distance metric.
The main MCMC function is
run_mcmc, but in cases where the gender of the judges is known
run_gender_mcmc can be used to analyse how the different genders behave.
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