A modular package for simulating phylogenetic trees and species traits jointly. Trees can be simulated using modular birth-death parameters (e.g. changing starting parameters or algorithm rules). Traits can be simulated in any way designed by the user. The growth of the tree and the traits can influence each other through modifiers objects providing rules for affecting each other. Finally, events can be created to modify both the tree and the traits under specific conditions ( Guillerme, 2024 <DOI:10.1111/2041-210X.14306>).

There are some experimental scenarios where each experimental unit receives a sequence of treatments across multiple periods, and treatment effects persist beyond the period of application. It focuses on the construction and calculation of the parametric value of the residual effect designs balanced for carryover effects, also referred to as crossover designs, change-over designs, or repeated measurements designs (Aggarwal and Jha, 2010<doi:10.1080/15598608.2010.10412013>). The primary objective of the package is to generate a new class of Balanced Ternary Residual Effect Designs (BTREDs), balanced for carryover effects tailored explicitly for situations where the number of periods is less than or equal to the number of treatments. In addition, the package provides four new classes of Partially Balanced Ternary Residual Effect Designs (PBTREDs), constructed using incomplete block designs, initial sequences, and rectangular association scheme. In addition, one extra function is included to help study the parametric properties of a given residual effect design.

An R re-implementation of the 'treeinterpreter' package on PyPI
        <https://pypi.org/project/treeinterpreter/>. Each prediction can be
        decomposed as 'prediction = bias + feature_1_contribution + ... +
        feature_n_contribution'. This decomposition is then used to calculate
        the Mean Decrease Impurity (MDI) and Mean Decrease Impurity using
        out-of-bag samples (MDI-oob) feature importance measures based on the
        work of Li et al. (2019) <doi:10.48550/arXiv.1906.10845>.