Provides an interface to 'Blimp' software for Bayesian latent variable modeling,
    missing data analysis, and multiple imputation. The package generates 'Blimp' syntax,
    executes 'Blimp' models, and imports results back into 'R' as structured objects with
    methods for visualization and analysis. Requires 'Blimp' software (freely available
    at <https://www.appliedmissingdata.com/blimp>) to be installed separately.

An R Interface to 'Bloomberg' is provided via the 'Blp API'.

An R-shiny application to visualize bio-loggers time series at a microsecond precision as Acceleration, Temperature, Pressure, Light intensity. It is possible to link behavioral labels extracted
  from 'BORIS' software <http://www.boris.unito.it> or manually written in a csv file.

BM25 is a ranking function used by search engines to rank matching documents according to their relevance to a user's search query.
    This package provides a light wrapper around the 'BM25' 'rust' crate for Okapi BM25 text search.
    For more information, see Robertson et al. (1994) <https://trec.nist.gov/pubs/trec3/t3_proceedings.html>.

Provides four boolean matrix factorization (BMF) methods.
             BMF has many applications like data mining and categorical data 
             analysis. BMF is also known as boolean matrix decomposition (BMD) 
             and was found to be an NP-hard (non-deterministic polynomial-time)
             problem. Currently implemented methods are 
             'Asso' Miettinen, Pauli and others (2008) 
                    <doi:10.1109/TKDE.2008.53>,
             'GreConD' R. Belohlavek, V. Vychodil (2010) 
                      <doi:10.1016/j.jcss.2009.05.002> ,
             'GreConDPlus' R. Belohlavek, V. Vychodil (2010) 
                      <doi:10.1016/j.jcss.2009.05.002> ,
             'topFiberM' A. Desouki, M. Roeder, A. Ngonga (2019) 
                       <arXiv:1903.10326>.

Implements standard and reference based multiple imputation methods for continuous
    longitudinal endpoints (Gower-Page et al. (2022) <doi:10.21105/joss.04251>). In particular,
    this package supports deterministic conditional mean imputation and jackknifing as described
    in Wolbers et al.  (2022) <doi:10.1002/pst.2234>, Bayesian multiple imputation as described
    in Carpenter et al. (2013) <doi:10.1080/10543406.2013.834911>, and bootstrapped maximum
    likelihood imputation as described in von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>.