Implements a wide variety of one- and two-parameter Bayesian CRM
    designs. The program can run interactively, allowing the user to enter outcomes
    after each cohort has been recruited, or via simulation to assess operating
    characteristics. See Sweeting et al. (2013): <doi:10.18637/jss.v054.i13>.

The bias-corrected estimation methods for the receiver operating characteristics
  ROC surface and the volume under ROC surfaces (VUS) under missing at random (MAR)
  assumption.

Search and access more than ten thousand datasets included in 'BCRPDATA' (see <https://estadisticas.bcrp.gob.pe/estadisticas/series/ayuda/bcrpdata> for more information).

Bindings to the 'blowfish' password hashing algorithm
    <https://www.openbsd.org/papers/bcrypt-paper.pdf> derived from
    the 'OpenBSD' implementation.

A collection of tools for regression analysis of non-negative data,
    including strictly positive and zero-inflated observations, based on the class of the Box-Cox
    symmetric (BCS) distributions and its zero-adjusted extension. The BCS distributions are a 
    class of flexible probability models capable of describing different levels of skewness and 
    tail-heaviness. The package offers a comprehensive regression modeling framework, including 
    estimation and tools for evaluating goodness-of-fit.

Methods for choosing the rank of an SVD (singular value decomposition) 
    approximation via cross validation. The package provides both Gabriel-style 
    "block" holdouts and Wold-style "speckled" holdouts. It also includes an 
    implementation of the SVDImpute algorithm. For more information about
    Bi-cross-validation, see Owen & Perry's 2009 AoAS article
    (at <arXiv:0908.2062>) and Perry's 2009 PhD thesis
    (at <arXiv:0909.3052>).

Algorithms developed for binned data analysis,
  gene expression data analysis and
  measurement error models for ordinal data analysis.

The Bloom Detecting Algorithm enables the detection of blooms within a time series of species abundance and extracts 22 phenological variables. For details, see Karasiewicz et al. (2022) <doi:10.3390/jmse10020174>.