Analyzes finite mixture models for various parametric and semiparametric settings.  This includes mixtures of parametric distributions (normal, multivariate normal, multinomial, gamma), various Reliability Mixture Models (RMMs), mixtures-of-regressions settings (linear regression, logistic regression, Poisson regression, linear regression with changepoints, predictor-dependent mixing proportions, random effects regressions, hierarchical mixtures-of-experts), and tools for selecting the number of components (bootstrapping the likelihood ratio test statistic, mixturegrams, and model selection criteria).  Bayesian estimation of mixtures-of-linear-regressions models is available as well as a novel data depth method for obtaining credible bands.  This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772 and the Chan Zuckerberg Initiative: Essential Open Source Software for Science (Grant No. 2020-255193).

Curve Fitting of monotonic(sigmoidal) & non-monotonic(J-shaped) 
 dose-response data. Predicting mixture toxicity based on reference 
 models such as 'concentration addition', 'independent action', and 'generalized 
 concentration addition'.

Statistical framework for comparing sets of trees using hypothesis testing methods. Designed for transmission trees, phylogenetic trees, and directed acyclic graphs (DAGs), the package implements chi-squared tests to compare edge frequencies between sets and PERMANOVA to analyse topological dissimilarities with customisable distance metrics, following Anderson (2001) <doi:10.1111/j.1442-9993.2001.01070.pp.x>.

A set of utility functions for analysing and modelling data from 
    continuous report short-term memory experiments using either the 2-component
    mixture model of Zhang and Luck (2008) <doi:10.1038/nature06860> or the 
    3-component mixture model of Bays et al. (2009) <doi:10.1167/9.10.7>. Users 
    are also able to simulate from these models.

An implementation of 14 parsimonious mixture models for model-based clustering or model-based classification. Gaussian, Student's t, generalized hyperbolic, variance-gamma or skew-t mixtures are available. All approaches work with missing data. Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>, Browne and McNicholas (2014) <doi:10.1007/s11634-013-0139-1>, Browne and McNicholas (2015) <doi:10.1002/cjs.11246>.