BradleyTerryScalable - Fits the Bradley-Terry Model to Potentially Large and Sparse
Networks of Comparison Data
Facilities are provided for fitting the simple,
unstructured Bradley-Terry model to networks of binary
comparisons. The implemented methods are designed to scale well
to large, potentially sparse, networks. A fairly high degree of
scalability is achieved through the use of EM and MM
algorithms, which are relatively undemanding in terms of memory
usage (relative to some other commonly used methods such as
iterative weighted least squares, for example). Both maximum
likelihood and Bayesian MAP estimation methods are implemented.
The package provides various standard methods for a newly
defined 'btfit' model class, such as the extraction and
summarisation of model parameters and the simulation of new
datasets from a fitted model. Tools are also provided for
reshaping data into the newly defined "btdata" class, and for
analysing the comparison network, prior to fitting the
Bradley-Terry model. This package complements, rather than
replaces, the existing 'BradleyTerry2' package. (BradleyTerry2
has rather different aims, which are mainly the specification
and fitting of "structured" Bradley-Terry models in which the
strength parameters depend on covariates.)
