Specification and estimation of multinomial logit
    models.  Large datasets and complex models are supported, with an
    intuitive syntax.  Multinomial Logit Models, Mixed models, random
    coefficients and Hybrid Choice are all supported.  For more
    information, see Molloy et al. (2021) <https://www.research-collection.ethz.ch/handle/20.500.11850/477416>.

The main functions perform mixed models analysis by least squares
    or REML by adding the function r() to formulas of lm() and glm(). A collection of
    text-book statistics for higher education is also included, e.g. modifications
    of the functions lm(), glm() and associated summaries from the package 'stats'.

Provides sampling and density functions for matrix
    variate normal, t, and inverted t distributions;  ML estimation for matrix
    variate normal and t distributions using the EM algorithm,
    including some restrictions on the parameters; and classification by linear and
    quadratic discriminant analysis for matrix variate normal and t
    distributions described in Thompson et al. (2019) <doi:10.1080/10618600.2019.1696208>.
    Performs clustering with matrix variate normal and t mixture models.

A collection of functions to perform various meta-analytical models
  through a unified mixed-effects framework, including standard univariate
  fixed and random-effects meta-analysis and meta-regression, and non-standard
  extensions such as multivariate, multilevel, longitudinal, and dose-response
  models.

Mixed variable optimization for non-linear functions.
    Can optimize function whose inputs are a combination of continuous,
    ordered, and unordered variables.

Simple tools to perform mixture optimization based on the 'desirability' package by Max Kuhn.
    It also provides a plot routine using 'ggplot2' and 'patchwork'.

Fits multiple variable mixtures of various parametric proportional hazard models using the EM-Algorithm. Proportionality restrictions can be imposed on the latent groups and/or on the variables. Several survival distributions can be specified. Missing values and censored values are allowed. Independence is assumed over the single variables.