The Explainable Ensemble Trees 'e2tree' approach has been proposed by Aria et al. (2024) <doi:10.1007/s00180-022-01312-6>. It aims to explain and interpret decision tree ensemble models using a single tree-like structure. 'e2tree' is a new way of explaining an ensemble tree trained through 'randomForest' or 'xgboost' packages.

Fits dose-response models using an evolutionary
    algorithm to estimate the model parameters. The procedure currently
    can fit 3-parameter, 4-parameter, and 5-parameter log-logistic models
    as well as exponential models. Functions are also provided to plot,
    make predictions, and calculate confidence intervals for the resulting
    models. For details see "Nonlinear Dose-response Modeling of
    High-Throughput Screening Data Using an Evolutionary Algorithm",
    Ma, J., Bair, E., Motsinger-Reif, A.; Dose-Response
    18(2):1559325820926734 (2020) <doi:10.1177/1559325820926734>.

Computation and visualization of the empirical attainment function (EAF) for the analysis of random sets in multi-criterion optimization. M. López-Ibáñez, L. Paquete, and T. Stützle (2010) <doi:10.1007/978-3-642-02538-9_9>.

An implementation of multiple-locus association mapping on a genome-wide scale. 'Eagle' can handle inbred and  outbred study populations, populations of arbitrary unknown complexity, and data larger than the memory capacity of the computer. Since 'Eagle' is based on linear mixed models, it is best suited to the analysis of data on continuous traits. However, it can tolerate non-normal data. 'Eagle' reports, as its findings, the best set of snp in strongest association with a trait. For users unfamiliar with R, to perform an analysis, run 'OpenGUI()'. This  opens a web browser to the menu-driven user interface for the input of data, and for performing genome-wide analysis.

A 'Shiny' web application for energy industry analytics.
      Take an overview of the industry, measure Key Performance Indicators,
      identify changes in the industry over time, and discover new relationships in the data.