Provides estimators for multinomial logit models in their
    conditional logit (for discrete choices) and baseline logit variants
    (for categorical responses), optionally with overdispersion or random
    effects. Random effects models are estimated using the PQL technique
    (based on a Laplace approximation) or the MQL technique (based on a
    Solomon-Cox approximation). Estimates should be treated
    with caution if the group sizes are small.

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.