Likelihood-based boosting approaches for generalized mixed models are provided.

The algorithm of semi-supervised learning is based on finite Gaussian mixture models and includes a mechanism for handling missing data. It aims to fit a g-class Gaussian mixture model using maximum likelihood. The algorithm treats the labels of unclassified features as missing data, building on the framework introduced by Rubin (1976) <doi:10.2307/2335739> for missing data analysis. By taking into account the dependencies in the missing pattern, the algorithm provides more information for determining the optimal classifier, as specified by Bayes' rule.

An implementation of maximum simulated likelihood method for the
    estimation of multinomial logit models with random coefficients as presented by Sarrias and Daziano (2017) <doi:10.18637/jss.v079.i02>.
    Specifically, it allows estimating models with continuous heterogeneity
    such as the mixed multinomial logit and the generalized multinomial logit.
    It also allows estimating models with discrete heterogeneity such as the
    latent class and the mixed-mixed multinomial logit model.

Various R programming tools for model fitting.

Make 2D and 3D plots of linear programming (LP), 
    integer linear programming (ILP), or mixed integer linear programming (MILP) models 
    with up to three objectives. Plots of both the solution and criterion space are possible.
    For instance the non-dominated (Pareto) set for bi-objective LP/ILP/MILP programming models 
    (see vignettes for an overview). The package also contains an function for checking if a point
    is inside the convex hull.

Datasets analysed in the book Antony Unwin (2024, ISBN:978-0367674007) "Getting (more out of) Graphics".

Efficiently manage and process data from 'oTree' experiments.
    Import 'oTree' data and clean them by using 
    functions that handle messy data, dropouts, and other problematic cases.
    Create IDs, calculate the time, transfer
    variables between app data frames, and delete sensitive information.
    Review your experimental data prior to running 
    the experiment and automatically generate a detailed summary 
    of the variables used in your 'oTree' code.
    Information on 'oTree' is found in Chen, D. L., Schonger, M., &
    Wickens, C. (2016) <doi:10.1016/j.jbef.2015.12.001>.