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.

Simulation-based evidence accumulation models for analyzing responses
  and reaction times in single- and multi-response tasks. The package includes
  simulation engines for five representative models: the Diffusion Decision
  Model (DDM), Leaky Competing Accumulator (LCA), Linear Ballistic Accumulator
  (LBA), Racing Diffusion Model (RDM), and Levy Flight Model (LFM), and
  extends these frameworks to multi-response settings. The package supports
  user-defined functions for item-level parameterization and the incorporation
  of covariates, enabling flexible customization and the development of new
  model variants based on existing architectures. Inference is performed using
  simulation-based methods, including Approximate Bayesian Computation (ABC)
  and Amortized Bayesian Inference (ABI), which allow parameter estimation
  without requiring tractable likelihood functions. In addition to core
  inference tools, the package provides modules for parameter recovery,
  posterior predictive checks, and model comparison, facilitating the study of
  a wide range of cognitive processes in tasks involving perceptual decision
  making, memory retrieval, and value-based decision making. Key methods
  implemented in the package are described in Ratcliff (1978) <doi:10.1037/0033-295X.85.2.59>,
  Usher and McClelland (2001) <doi:10.1037/0033-295X.108.3.550>, Brown and Heathcote (2008)
  <doi:10.1016/j.cogpsych.2007.12.002>, Tillman, Van Zandt and Logan (2020)
  <doi:10.3758/s13423-020-01719-6>, Wieschen, Voss and Radev (2020)
  <doi:10.20982/tqmp.16.2.p120>, Csilléry, François and Blum (2012)
  <doi:10.1111/j.2041-210X.2011.00179.x>, Beaumont (2019)
  <doi:10.1146/annurev-statistics-030718-105212>, and Sainsbury-Dale,
  Zammit-Mangion and Huser (2024) <doi:10.1080/00031305.2023.2249522>.