'BEAST2' (<https://www.beast2.org>) is a widely used
    Bayesian phylogenetic tool, that uses DNA/RNA/protein data
    and many model priors to create a posterior of jointly estimated 
    phylogenies and parameters.
    'BEAST2' is a command-line tool.
    This package provides a way to call 'BEAST2' 
    from an 'R' function call.

Provides JAR to perform Markov chain Monte Carlo (MCMC) inference using
    the popular Bayesian Evolutionary Analysis by Sampling Trees 'BEAST X' software
    library of Baele et al (2025) <doi:10.1038/s41592-025-02751-x>. 'BEAST X' supports
    auto-tuning Metropolis-Hastings, slice, Hamiltonian Monte Carlo and Sequential Monte
    Carlo sampling for a large variety of composable standard and phylogenetic
    statistical models using high performance computing.  By placing the 'BEAST X' JAR in
    this package, we offer an efficient distribution system for 'BEAST X' use by other R
    packages using CRAN.

Bayesian dynamic borrowing with covariate adjustment via inverse probability 
  weighting for simulations and data analyses in clinical trials. This makes it easy to use
  propensity score methods to balance covariate distributions between external and internal 
  data. This methodology based on Psioda et al (2025) <doi:10.1080/10543406.2025.2489285>.

'BEAST2' (<https://www.beast2.org>) is a widely used
  Bayesian phylogenetic tool, that uses DNA/RNA/protein data
  and many model priors to create a posterior of jointly estimated 
  phylogenies and parameters.
  'BEAUti 2' (which is part of 'BEAST2') is a GUI tool 
  that allows users to specify the many possible setups
  and generates the XML file 'BEAST2' needs to run.
  This package provides a way to create 'BEAST2' input
  files without active user input, but using
  R function calls instead.

Dose-response modeling for negative-binomial distributed data with
  a variety of dose-response models. Covariate adjustment and Bayesian
  model averaging is supported. Functions are provided to easily obtain
  inference on the dose-response relationship and plot the dose-response curve.