Interface with the 'Brickset' API
    <https://brickset.com/article/52664/api-version-3-documentation> for
    getting data about LEGO sets. Data sets that
    can be used for teaching and learning without the need of a 'Brickset'
    account and API key are also included. Includes all LEGO since through
    the end of 2023.

An implementation of the bridge distribution with logit-link in
    R. In Wang and Louis (2003) <DOI:10.1093/biomet/90.4.765>, such a univariate
    bridge distribution was derived as the distribution of the random intercept that
    'bridged' a marginal logistic regression and a conditional logistic regression.
    The conditional and marginal regression coefficients are a scalar multiple
    of each other. Such is not the case if the random intercept distribution was
    Gaussian.

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.