Create, query, and modify causal graphs. 'caugi' (Causal Graph
    Interface) is a causality-first, high performance graph package that 
    provides a simple interface to build, structure, and examine causal 
    relationships.

The Cauchy Process can model pulsed continuous trait evolution
    on phylogenies. The likelihood is tractable, and is used for parameter
    inference and ancestral trait reconstruction.
    See Bastide and Didier (2023) <doi:10.1093/sysbio/syad053>.

Accelerate Bayesian analytics workflows in 'R' through interactive modelling,
    visualization, and inference. Define probabilistic graphical models using directed
    acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians, 
    and programmers. This package relies on interfacing with the 'numpyro' python package.

We implement causal decomposition analysis using methods proposed by Park, Lee, and Qin (2022) and Park, Kang, and Lee (2023), which provide researchers with multiple-mediator imputation, single-mediator imputation, and product-of-coefficients regression approaches to estimate the initial disparity, disparity reduction, and disparity remaining (<doi:10.1177/00491241211067516>; <doi:10.1177/00811750231183711>). We also implement sensitivity analysis for causal decomposition using R-squared values as sensitivity parameters (Park, Kang, Lee, and Ma, 2023 <doi:10.1515/jci-2022-0031>). Finally, we include individualized causal decomposition and sensitivity analyses proposed by Park, Kang, and Lee (2025+) <doi:10.48550/arXiv.2506.19010>.

Software which provides numerous functionalities for detecting and removing group-level effects from high-dimensional scientific data which, when combined with additional assumptions, allow for causal conclusions, as-described in our manuscripts Bridgeford et al. (2024) <doi:10.1101/2021.09.03.458920> and Bridgeford et al. (2023) <doi:10.48550/arXiv.2307.13868>. Also provides a number of useful utilities for generating simulations and balancing covariates across multiple groups/batches of data via matching and propensity trimming for more than two groups.