Produce bridge hands, allowing parameters for hands to offer specific for bidding sequences.

BRIC-seq is a genome-wide approach for determining RNA stability in mammalian cells.
    This package provides a series of functions for performing quality check of your BRIC-seq data,
    calculation of RNA half-life for each transcript and comparison of RNA half-lives between two conditions.

Provides functions for estimating marginal likelihoods, Bayes
    factors, posterior model probabilities, and normalizing constants in general,
    via different versions of bridge sampling (Meng & Wong, 1996, 
    <http://www3.stat.sinica.edu.tw/statistica/j6n4/j6n43/j6n43.htm>).
    Gronau, Singmann, & Wagenmakers (2020) <doi:10.18637/jss.v092.i10>.

Build decision trees and random forests for classification and regression. The implementation strikes a balance between minimizing computing efforts and maximizing the expected predictive accuracy, thus scales well to large data sets. Multi-threading is available through 'OpenMP' <https://gcc.gnu.org/wiki/openmp>.

Implementation of the BRIk, FABRIk and FDEBRIk algorithms 
        to initialise k-means. These methods are intended for the 
        clustering of multivariate and functional data, respectively.
        They make use of the Modified Band Depth and bootstrap to 
        identify appropriate initial seeds for k-means, which are 
        proven to be better options than many techniques in the 
        literature. Torrente and Romo (2021) <doi:10.1007/s00357-020-09372-3>
        It makes use of the functions kma and kma.similarity, from the 
        archived package fdakma, by Alice Parodi et al.

An automated graphical exploratory data analysis (EDA) tool that introduces: 
  a.) wideplot graphics for exploring the structure of a dataset through a grid of variables 
      and graphic types. 
  b.) longplot graphics, which present the entire catalog of available graphics for representing 
      a particular variable using a grid of graphic types and variations on these types.
  c.) plotup function, which presents a particular graphic for a specific variable of a dataset. 
      The plotup() function also makes it possible to obtain the code used to generate the graphic, 
      meaning that the user can adjust its properties as needed.
  d.) matrixplot graphics that is a grid of a particular graphic showing bivariate relationships
      between all pairs of variables of a certain(s) type(s) in a multivariate data set.