After some months of work the bpcs package is at version v1.0.0 and is available on CRAN (https://CRAN.R-project.org/package=bpcs). Now we can easily run Bayesian inference on Bradley-Terry models (including many extensions).

See below the description of the package

Models for the analysis of paired comparison data using Stan. The models include Bayesian versions of the Bradley-Terry model, including random effects (1 level), generalized model for predictors, order effect (home advantage) and the variations for the Davidson (1970) model to handle ties. Additionally, we provide a number of functions to facilitate inference and obtaining results with these models.

Features of the bpcs package

• Bayesian computation of different variations of the Bradley-Terry (including with home advantage, random effects and the generalized model).
• Bayesian computation of different variations of the Davidson model to handle ties in the contest (including with home advantage, random effects and the generalized model).
• Accepts a column with the results of the contest or the scores for each player.
• Customize a normal prior distribution for every parameter.
• Compute HDP interval for every parameter with the get_hpdi_parameters function
• Compute rank of the players with the get_rank_of_players function.
• Compute all the probability combinations for one player beating the other with the get_probabilities function.
• Convert aggregated tables of results into long format (one contest per row) with the expand_aggregated_data.
• Obtain the posterior distribution for every parameter of the model with the get_sample_posterior function.
• Easy predictions using the predict function.
• We do not reinforce any table or plotting library! Results are returned as data frames for easier plotting and creating tables
• We reinforce the need to manually specify the model to be used.

Models available

• Bradley-Terry (bt) (Bradley and Terry 1952)
• Davidson model (davidson) for handling ties (Davidson 1970)

Options to add to the models:

• Order effect (-ordereffect). E.g. for home advantage (Davidson and Beaver 1977)
• Generalized models (-generalized). When we have contestant specific predictors (Springall 1973)
• Intercept random effects (-U). For example, to compensate clustering or repeated measures (Böckenholt 2001)

E.g.:

• Simple BT model: bt
• Davidson model with random effects: davidson-U
• Generalized BT model with order effect: bt-generalized-ordereffect

Get the package

To get the latest version of the bpcs package, install it from Github:

remotes::install_github('davidissamattos/bpcs')