Authors@R: c( person(given = "Lampros", family = "Mouselimis", email = "mouselimislampros@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "https://orcid.org/0000-0002-8024-1546")), person(given = "Alberto", family = "Gosso", email = "gosso.alberto@gmail.com", role = "aut") )

Authors@R: c( person(given = "Lampros", family = "Mouselimis", email = "mouselimislampros@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "https://orcid.org/0000-0002-8024-1546")), person(given = "Alberto", family = "Gosso", email = "gosso.alberto@gmail.com", role = "aut"), person(given = "Edwin", family = "de Jonge", email = "edwindjonge@gmail.com", role = "ctb", comment = c(ORCID = "https://orcid.org/0000-0002-6580-4718", Github = "Github Contributor")) )
Edwin de Jonge [ctb] (<https://orcid.org/0000-0002-6580-4718>, Github

person("Ethan", "Heinzen", email = "heinzen.ethan@mayo.edu", role = c("aut", "cre"))
Description: A flexible framework for calculating Elo ratings and resulting
rankings of any two-team-per-matchup system (chess, sports leagues, 'Go',
etc.). This implementation is capable of evaluating a variety of matchups,
Elo rating updates, and win probabilities, all based on the basic Elo

rating system. It also includes methods to benchmark performance,
including logistic regression and Markov chain models.

Title: Ranking Teams by Elo Rating and Comparable Methods

Title: Exact Logistic Regression via MCMC
Depends: R(>= 2.3.0), coda, graphics, stats
Author: David Zamar, Jinko Graham, Brad McNeney
Description: elrm implements a Markov Chain Monte Carlo algorithm to approximate exact conditional inference for logistic regression models. Exact conditional inference is based on the distribution of the sufficient statistics for the parameters of interest given the sufficient statistics for the remaining nuisance parameters. Using model formula notation, users specify a logistic model and model terms of interest for exact inference.
Packaged: Mon Nov 20 08:53:09 2006; hornik

Packaged: Sun Sep 30 11:01:38 2007; ruben