'BEAST2' (<https://www.beast2.org>) is a widely used
  Bayesian phylogenetic tool, that uses DNA/RNA/protein data
  and many model priors to create a posterior of jointly estimated 
  phylogenies and parameters.
  'BEAUti 2' (which is part of 'BEAST2') is a GUI tool 
  that allows users to specify the many possible setups
  and generates the XML file 'BEAST2' needs to run.
  This package provides a way to create 'BEAST2' input
  files without active user input, but using
  R function calls instead.

Provides a collection of utility functions for biostatistics,
    agricultural trial planning, and experimental design. Key features include
    generating experimental designs (like Latin Square, Alpha-Lattice by
    Patterson and Williams (1976) <doi:10.2307/2335087>, and Factorial),
    fieldbook creation, layout sketching, QR code-based label generation, and
    descriptive statistical tools to easily handle most common descriptive
    statistics for quantitative variables as described by Field, A., Miles, J.,
    & Field, Z. (2012, ISBN:978-1-4462-0045-2).

Dose-response modeling for negative-binomial distributed data with
  a variety of dose-response models. Covariate adjustment and Bayesian
  model averaging is supported. Functions are provided to easily obtain
  inference on the dose-response relationship and plot the dose-response curve.

An interface for the 'Neo4j' database providing
    mapping between different identifiers of biological entities.
    This Biological Entity Dictionary (BED)
    has been developed to address three main challenges.
    The first one is related to the completeness of identifier mappings.
    Indeed, direct mapping information provided by the different systems
    are not always complete and can be enriched by mappings provided by other
    resources.
    More interestingly, direct mappings not identified by any of these
    resources can be indirectly inferred by using mappings to a third reference.
    For example, many human Ensembl gene ID are not directly mapped to any
    Entrez gene ID but such mappings can be inferred using respective mappings
    to HGNC ID. The second challenge is related to the mapping of deprecated
    identifiers. Indeed, entity identifiers can change from one resource
    release to another. The identifier history is provided by some resources,
    such as Ensembl or the NCBI, but it is generally not used by mapping tools.
    The third challenge is related to the automation of the mapping process
    according to the relationships between the biological entities of interest.
    Indeed, mapping between gene and protein ID scopes should not be done
    the same way than between two scopes regarding gene ID.
    Also, converting identifiers from different organisms should be possible
    using gene orthologs information.
    The method has been published by
    Godard and van Eyll (2018) <doi:10.12688/f1000research.13925.3>.