Title: Bayesian Paired Comparison Analysis with Stan
Description: 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. References: Bradley and Terry (1952) <doi:10.2307/2334029>; Davidson (1970) <doi:10.1080/01621459.1970.10481082>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
Imports: coda, dplyr, tidyr, stringr, ggplot2, gtools, methods, Rcpp
rstantools (>= 2.1.0), tibble, tidyselect, HDInterval,
shinystan, loo, magrittr, badger, stats, rlang, knitr
RcppParallel (>= 5.0.1), rstan (>= 2.20.0), StanHeaders (>=
Suggests: rmarkdown, testthat, covr, bayesplot, kableExtra
Author: David Issa Mattos [aut, cre] (<https://orcid.org/0000-0002-2501-9926>),
Maintainer: David Issa Mattos <issamattos.david@gmail.com>

Title: Bayesian Model Selection Approach for Parsimonious Gaussian
gtools (>= 3.8.1), label.switching (>= 1.8)
Author: Xiang Lu <Xiang_Lu at urmc.rochester.edu>,
Tanzy Love <tanzy_love at urmc.rochester.edu>
Description: Model-based clustering using Bayesian parsimonious Gaussian mixture models.
MCMC (Markov chain Monte Carlo) are used for parameter estimation. The RJMCMC (Reversible-jump Markov chain Monte Carlo) is used for model selection.

Title: Back-Projected Kernel Density Estimation
Author: Kjell Konis <kjell.konis@me.com>, Victor M. Panaretos
Depends: R (>= 2.15.0), mvtnorm, KernSmooth
Description: Nonparametric multivariate kernel density \ estimation

Description: Nonparametric multivariate kernel density \
estimation using a back-projected kernel.

Title: Back Projected Kernel Density Estimation
Author: Kjell Konis <kjell.konis@me.com>, Victor Panaretos
Description: alpha version but now a little bit better

Title: Bayesian Projected Normal Regression Models for Circular Data
Authors@R: person("Jolien", "Cremers", email = "joliencremers@gmail.com",
Description: Fitting Bayesian multiple and mixed-effect regression models for
circular data based on the projected normal distribution. Both continuous
and categorical predictors can be included. Sampling from the posterior is
performed via an MCMC algorithm. Posterior descriptives of all parameters,
model fit statistics and Bayes factors for hypothesis tests for inequality
constrained hypotheses are provided. See Cremers, Mulder & Klugkist (2018)
<doi:10.1111/bmsp.12108> and Nuñez-Antonio & Guttiérez-Peña (2014)