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.

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.

Estimation of average treatment effects (ATE) of point interventions on time-to-event outcomes with K competing risks (K can be 1). The method uses propensity scores and inverse probability weighting for emulation of baseline randomization, which is described in Charpignon et al. (2022) <doi:10.1038/s41467-022-35157-w>.

Example data sets to run the example
    problems from causal inference textbooks. Currently, contains data
    sets for Huntington-Klein, Nick (2021 and 2025) "The Effect" <https://theeffectbook.net>, first and second edition,
    Cunningham, Scott (2021 and 2025, ISBN-13: 978-0-300-25168-5) "Causal Inference: The Mixtape", 
    and Hernán, Miguel and James Robins (2020) "Causal Inference: What If" 
    <https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/>.