A novel data-augmentation Markov chain Monte Carlo sampling algorithm to fit a progressive compartmental model of disease in a Bayesian framework 
             Morsomme, R.N., Holloway, S.T., Ryser, M.D. and Xu J. (2024) <doi:10.48550/arXiv.2408.14625>.

Decomposition for differences-in-differences with variation in
    treatment timing from Goodman-Bacon (2018) <doi:10.3386/w25018>.

Models discrete sequential data using Bayesian Context Trees.
    Context trees, also known as Variable Length Markov
    Chains (VLMCs), are parsimonious Markov models where the order of
    dependence can vary with the observed past. Provides a generic 'R6'
    class structure that exposes the full tree
    for building custom algorithms, exact Bayesian inference via a
    bottom-up recursive algorithm (closed-form marginal likelihood,
    Maximum A Posteriori (MAP) tree, exact posterior probabilities, and
    exact sampling from the posterior), a frequentist estimator via the
    context algorithm with likelihood-ratio pruning,
    simulation utilities, and a Metropolis-Hastings sampler.
    See Paulichen and Freguglia (2026) <doi:10.48550/arXiv.2603.25806>.

The Bayesian Adjustment for Confounding (BAC) algorithm (Wang et al., 2012)
 can be used to estimate the causal effect of a continuous exposure on a continuous outcome.
 This package provides an approximate sensitivity analysis of BAC with regards to the
 hyperparameter omega. BACprior also provides functions to guide the user in their choice
 of an appropriate omega value. The method is based on Lefebvre, Atherton and Talbot (2014).

Estimating the average causal effect based on the Bayesian Adjustment for Confounding (BAC) algorithm.

Bindings to 'badgen' <https://www.npmjs.com/package/badgen> to
    generate beautiful 'svg' badges in R without internet access. Images can
    be converted to 'png' using the 'rsvg' package as shown in examples.

Query information and generate badge for using in README and GitHub Pages.

Implements Bayesian model averaging for dynamic panels with weakly
    exogenous regressors as described in the paper by
    Moral-Benito (2013, <doi:10.1080/07350015.2013.818003>).
    The package provides functions to estimate dynamic panel data models
    and analyze the results of the estimation.