A new robust principal component analysis algorithm is implemented that relies upon the Cauchy Distribution. The algorithm is suitable for high dimensional data even if the sample size is less than the number of variables. The methodology is described in this paper: Fayomi A., Pantazis Y., Tsagris M. and Wood A.T.A. (2024). "Cauchy robust principal component analysis with applications to high-dimensional data sets". Statistics and Computing, 34: 26. <doi:10.1007/s11222-023-10328-x>.

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 the methods proposed by Park, Lee, and Qin (2020) and Park, Kang, and Lee (2021+) <arXiv:2109.06940>. This package allows researchers to use the multiple-mediator-imputation, single-mediator-imputation, and product-of-coefficients regression methods to estimate the initial disparity, disparity reduction, and disparity remaining. It also allows to make the inference conditional on baseline covariates. We also implement sensitivity analysis for the causal decomposition analysis using R-squared values as sensitivity parameters (Park, Kang, Lee, and Ma, 2023).

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) <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.