Facilities for working with Atlantis box-geometry model (BGM) 
 files. Atlantis is a deterministic, biogeochemical, whole-of-ecosystem model. 
 Functions are provided to read from BGM files directly, preserving their 
 internal topology, as well as helper functions to generate spatial data from these
 mesh forms. This functionality aims to simplify the creation and modification of box 
 and geometry as well as the ability to integrate with other data sources.

Provides a complete interface to 'LibBi', a library for Bayesian
    inference (see <https://libbi.org> and Murray, 2015
    <doi:10.18637/jss.v067.i10> for more information). This includes functions
    for manipulating 'LibBi' models, for reading and writing 'LibBi'
    input/output files, for converting 'LibBi' output to provide traces for use
    with the coda package, and for running 'LibBi' to conduct inference.

Implementation of the algorithms (with minor modifications)
    to correct bias in quantitative DNA methylation analyses as described
    by Moskalev et al. (2011) <doi:10.1093/nar/gkr213>. Publication:
    Kapsner et al. (2021) <doi:10.1002/ijc.33681>.

Read and write 'Bibtex' files. Convert between bibliography
    formats, including 'Bibtex', 'Biblatex', 'PubMed', 'Endnote', and
    'Bibentry'.  Includes a port of the 'bibutils' utilities by Chris
    Putnam <https://sourceforge.net/projects/bibutils/>. Supports all
    bibliography formats and character encodings implemented in
    'bibutils'.

Contains a collection of helper functions to use with 'rbi', the R
  interface to 'LibBi', described in
  Murray et al. (2015) <doi:10.18637/jss.v067.i10>. It contains functions to
  adapt the proposal distribution and number of particles in
  particle Markov-Chain Monte Carlo, as well as calculating the
  Deviance Information Criterion (DIC) and converting between times in 'LibBi'
  results and R time/dates.

Manually bin data using weight of evidence and information value. Includes other binning 
    methods such as equal length, quantile and winsorized. Options for combining levels of categorical
    data are also available. Dummy variables can be generated based on the bins created using any of 
    the available binning methods. References: Siddiqi, N. (2006) <doi:10.1002/9781119201731.biblio>.