Provides a function for the estimation of mixture of longitudinal factor analysis models using the iterative expectation-maximization algorithm (Ounajim, Slaoui, Louis, Billot, Frasca, Rigoard (2023) <doi:10.1002/sim.9804>) and several tools for visualizing and interpreting the models' parameters.

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.

A facility to generate various classes of fractional designs for order-of-addition experiments namely fractional order-of-additions orthogonal arrays, see Voelkel, Joseph G. (2019). "The design of order-of-addition experiments." Journal of Quality Technology 51:3, 230-241, <doi:10.1080/00224065.2019.1569958>. Provides facility to construct component orthogonal arrays, see Jian-Feng Yang, Fasheng Sun and Hongquan Xu (2020). "A Component Position Model, Analysis and Design for Order-of-Addition Experiments." Technometrics, <doi:10.1080/00401706.2020.1764394>. Supports generation of fractional designs for order-of-addition mixture experiments. Analysis of data from order-of-addition mixture experiments is also supported.