Learning and inference over dynamic Bayesian networks of arbitrary 
    Markovian order. Extends some of the functionality offered by the 'bnlearn' 
    package to learn the networks from data and perform exact inference. 
    It offers three structure learning algorithms for dynamic Bayesian networks:
    Trabelsi G. (2013) <doi:10.1007/978-3-642-41398-8_34>, Santos F.P. and Maciel C.D. (2014)
    <doi:10.1109/BRC.2014.6880957>, Quesada D., Bielza C. and Larrañaga P. (2021)
    <doi:10.1007/978-3-030-86271-8_14>. It also offers the possibility to perform 
    forecasts of arbitrary length. A tool for visualizing the structure of the 
    net is also provided via the 'visNetwork' package.

This tool is for parsing public drug databases such as 'DrugBank' XML database <https://go.drugbank.com/>.
    The parsed data are then returned in a proper 'R' object called 'dvobject'.

Leverages 'dplyr' to process the calculations of a plot inside a database. 
    This package provides helper functions that abstract the work at three levels:
    outputs a 'ggplot', outputs the calculations, outputs the formula
    needed to calculate bins.

A 'dplyr' back end for databases that allows you to work with
    remote database tables as if they are in-memory data frames.  Basic
    features works with any database that has a 'DBI' back end; more
    advanced features require 'SQL' translation to be provided by the
    package author.

Calculates lower bound on power, upper bound on power, and exact power (small sets only) for detection boundary tests (e.g. Berk-Jones, Generalized Berk-Jones, innovated Berk-Jones) used in set-based inference studies. These detection boundary tests are described in Sun et al., (2019) <doi:10.1080/01621459.2019.1660170>.

Bayesian Beta Regression, adapted for bounded discrete responses, commonly seen in survey responses.
  Estimation is done via Markov Chain Monte Carlo sampling, using a Gibbs wrapper around univariate slice sampler 
  (Neal (2003) <DOI:10.1214/aos/1056562461>), as implemented in the R package MfUSampler 
  (Mahani and Sharabiani (2017) <DOI: 10.18637/jss.v078.c01>).