Description: Assess the significance of identified clusters and estimates the true number of clusters by comparing the explained variation due to the clustering from the original data to that produced by clustering a unimodal reference distribution which preserves the covariance structure in the data. The reference distribution is generated using kernel density estimation and a Gaussian copula framework. A dimension reduction strategy and sparse covariance estimation optimize this method for the high-dimensional, low-sample size setting. This method is described in Helgeson, Vock, and Bair (2021) <doi:10.1111/biom.13376>.

Title: Uncertainty in Partial Credit Models
Description: Provides an extension to the Partial Credit Model and Generalized Partial Credit Models which allows for an additional person parameter that characterizes the uncertainty of the person. The method was originally proposed by Tutz and Schauberger (2020) <doi:10.1177/0146621620920932>.
Imports: Rcpp (>= 0.12.4), cubature, mvtnorm, numDeriv, statmod

Title: Efficient Bayesian Models for Binary and Categorical Data
Description: Highly efficient Bayesian implementations of probit, logit, multinomial logit and binomial logit models. Functions for plotting and tabulating the estimation output are available as well. Estimation is based on Gibbs sampling where the Markov chain Monte Carlo algorithms are based on the latent variable representations and boosting algorithms outlined in Fr�hwirth-Schnatter S., Zens G., Wagner H. (2020) <arXiv:2011.06898>. The underlying implementation is written in C++.
Imports: ggplot2, knitr, matrixStats, mnormt, pgdraw, reshape2, Rcpp,

Title: Efficient Bayesian Algorithms for Binary and Categorical Data
Description: Efficient Bayesian implementations of probit, logit, multinomial logit and binomial logit models. Functions for plotting and tabulating the estimation output are available as well. Estimation is based on Gibbs sampling where the Markov chain Monte Carlo algorithms are based on the latent variable representations and marginal data augmentation algorithms outlined in Fr�hwirth-Schnatter S., Zens G., Wagner H. (2020) <arXiv:2011.06898>.
Imports: ggplot2, knitr, matrixStats, mnormt, pgdraw, reshape2, coda,