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>.

Provides utility functions that extend the capabilities of the 
    reference-based multiple imputation package 'rbmi'. It supports clinical 
    trial analysis workflows with functions for managing imputed datasets, 
    applying analysis methods across imputations, and tidying results for 
    reporting.