Implements biplot (2d and 3d) of multivariate data based
             on principal components analysis and diagnostic tools of the quality of the reduction.

Calculates nonparametric pointwise confidence intervals for the survival distribution for right censored data, and for medians [Fay and Brittain <DOI:10.1002/sim.6905>]. Has two-sample tests for dissimilarity (e.g., difference, ratio or odds ratio) in survival at a fixed time, and differences in medians [Fay, Proschan, and Brittain <DOI:10.1111/biom.12231>]. Basically, the package gives exact inference methods for one- and two-sample exact inferences for Kaplan-Meier curves (e.g., generalizing Fisher's exact test to allow for right censoring), which are especially important for latter parts of the survival curve, small sample sizes or heavily censored data. Includes mid-p options.

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>.

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>.