Non-Parametric Cluster Significance Testing with Reference to a
Unimodal Null Distribution
Erika S. Helgeson, David Vock, and Eric Bair
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 similar to them method described in Helgeson and Bair (2016) <arXiv:1610.01424> except a Gaussian copula approach is used to account for feature correlation.

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>.

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>.
Rcpp (>= 0.12.4), cubature, mvtnorm, numDeriv, statmod

Efficient Bayesian Models for Binary and Categorical Data
c(person("Gregor","Zens",role=c("aut","cre"),email="gzens@wu.ac.at"),
person("Sylvia","Frühwirth-Schnatter", role="aut"),
person("Helga","Wagner", role="aut"),
person("Daniel F.","Schmidt", role="ctb"),
person("Enes", "Makalic", role="ctb"))
Gregor Zens [aut, cre],
Sylvia Frühwirth-Schnatter [aut],
Helga Wagner [aut],
Daniel F. Schmidt [ctb],
Enes Makalic [ctb]
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 Frühwirth-Schnatter S., Zens G., Wagner H. (2020) <arXiv:2011.06898>. The underlying implementation is written in C++.
ggplot2, knitr, matrixStats, mnormt, pgdraw, reshape2, Rcpp,
RcppProgress, coda