ggplot2 (>= 3.3.3), graphics, Rcpp (>= 1.0.6), stats
rmarkdown, knitr, testthat (>= 3.0.0), mockery

Estimates mixture Rasch models, including the dichotomous
Rash model, the rating scale model, and the partial credit
model.

Estimates Rasch models and mixture Rasch models, including the dichotomous Rash model, the rating scale model, and the partial credit model.

Estimates Rasch models and mixture Rasch models, including the dichotomous Rasch model, the rating scale model, and the partial credit model.

Plotting and Average Theta Functions for Multiple Class Mixed
Rasch Models
c(person("Pamela", "Trantham", role = c("aut", "cre"),
email = "pamelapsc@yahoo.com"))
Provides supplemental functions for the 'mixRasch'
package (Willse, 2014), <https://cran.r-project.org/package=mixRasch/mixRasch.pdf> including a
plotting function to compare item parameters for multiple
class models and a function that provides average theta values for each class in
a mixture model.

Mixtures of Power Exponential and Skew Power Exponential
Distributions for Use in Model-Based Clustering and
Classification
Utkarsh J. Dang[aut, cre], Michael P. B. Gallaugher[ctb], Ryan P. Browne[aut, cre], and Paul D. McNicholas[aut]
Mixtures of skewed and elliptical distributions are implemented using mixtures of multivariate skew
power exponential and power exponential distributions, respectively. A generalized expectation-maximization
framework is used for parameter estimation. Methodology for mixtures of power exponential distributions is
from Dang et al. (2015) <doi: 10.1111/biom.12351>.

Ryan P. Browne[aut, cre], Utkarsh J. Dang[aut, cre], Michael P. B. Gallaugher[ctb], and Paul D. McNicholas[aut]

Clustering Using Mixtures of Sub Gaussian Stable Distributions
Developed for model-based clustering using the finite mixtures of skewed sub-Gaussian
stable distributions developed by Teimouri (2022) <arXiv:2205.14067>.

Developed for model-based clustering using the finite mixtures of skewed sub-Gaussian
stable distributions developed by Teimouri (2022) <arXiv:2205.14067> and estimating
parameters of the symmetric stable distribution within the Bayesian framework.