Functions for drawing boxplots for data on (the boundary of) a unit circle (i.e., circular and axial data), from Buttarazzi D., Pandolfo G., Porzio G.C. (2018) <doi:10.1111/biom.12889>.

Model-based clustering using Bayesian parsimonious Gaussian mixture models.
  MCMC (Markov chain Monte Carlo) are used for parameter estimation. The RJMCMC (Reversible-jump Markov chain Monte Carlo) is used for model selection. 
  GREEN et al. (1995) <doi:10.1093/biomet/82.4.711>.

Fits the Bayesian partial least squares regression model
    introduced in Urbas et al. (2024) <doi:10.1214/24-AOAS1947>. Suitable
    for univariate and multivariate regression with high-dimensional data.

Bayesian purity model to estimate tumor purity using methylation 
    array data (DNA methylation Infinium 450K array data) without reference samples.

Creating, rendering and writing BPMN diagrams <https://www.bpmn.org/>. Functionalities can be used to visualize and export BPMN diagrams created using the 'pm4py' and 'bupaRminer' packages. Part of the 'bupaR' ecosystem.

To visualize the execution data of the processes on 'BPMN' (Business Process Model and Notation) diagrams, using overlays, style customization and interactions, with the 'bpmn-visualization' 'TypeScript' library.

Fitting Bayesian multiple and mixed-effect regression models for 
    circular data based on the projected normal distribution. Both continuous 
    and categorical predictors can be included. Sampling from the posterior is 
    performed via an MCMC algorithm. Posterior descriptives of all parameters, 
    model fit statistics and Bayes factors for hypothesis tests for inequality 
    constrained hypotheses are provided. See Cremers, Mulder & Klugkist (2018) 
    <doi:10.1111/bmsp.12108> and Nuñez-Antonio & Guttiérez-Peña (2014) 
    <doi:10.1016/j.csda.2012.07.025>.

Implements functions to update Bayesian Predictive Power Computations after not stopping a clinical trial at an interim analysis. Such an interim analysis can either be blinded or unblinded. Code is provided for Normally distributed endpoints with known variance, with a prominent example being the hazard ratio.