Gaussian finite mixture models fitted via EM algorithm for
  model-based clustering, classification, and density estimation, 
  including Bayesian regularization, dimension reduction for 
  visualisation, and resampling-based inference.

Extend the functionality of the 'mclust' package for Gaussian
    finite mixture modeling by including: density estimation for data with
    bounded support (Scrucca, 2019 <doi:10.1002/bimj.201800174>); modal
    clustering using MEM (Modal EM) algorithm for Gaussian mixtures
    (Scrucca, 2021 <doi:10.1002/sam.11527>); entropy estimation via
    Gaussian mixture modeling (Robin & Scrucca, 2023
    <doi:10.1016/j.csda.2022.107582>); Gaussian mixtures modeling of 
    financial log-returns (Scrucca, 2024 <doi:10.3390/e26110907>).

Given a set of data points, a clustering is defined as a disjoint partition
    where each pair of sets in a partition has no overlapping elements. 
    This package provides 25 methods that play a role somewhat similar to 
    distance or metric that measures similarity of two clusterings - or partitions.
    For a more detailed description, see Meila, M. (2005) <doi:10.1145/1102351.1102424>.

Estimate and test inter-generational social mobility effect on an outcome with cross-sectional or longitudinal data.

Provides a series of numerical methods for extracting parameters of distributions for risks based on knowing the expected value and c-statistics (e.g., from a published report on the performance of a risk prediction model). This package implements the methodology described in Sadatsafavi et al (2024) <doi:10.48550/arXiv.2409.09178>. The core of the package is mcmap(), which takes a pair of (mean, c-statistic) and the distribution type requested. This function provides a generic interface to more customized functions (mcmap_beta(), mcmap_logitnorm(), mcmap_probitnorm()) for specific distributions.

Simulates continuous distributions of random vectors using
    Markov chain Monte Carlo (MCMC).  Users specify the distribution by an
    R function that evaluates the log unnormalized density.  Algorithms
    are random walk Metropolis algorithm (function metrop), simulated
    tempering (function temper), and morphometric random walk Metropolis
    (Johnson and Geyer, 2012, <doi:10.1214/12-AOS1048>,
    function morph.metrop),
    which achieves geometric ergodicity by change of variable.