A convenient tool to install and update Bioconductor packages.

Tools to create, validate, and export BioCompute Objects
    described in King et al. (2019) <doi:10.17605/osf.io/h59uh>.
    Users can encode information in data frames, and compose
    BioCompute Objects from the domains defined by the standard.
    A checksum validator and a JSON schema validator are provided.
    This package also supports exporting BioCompute Objects as JSON,
    PDF, HTML, or 'Word' documents, and exporting to cloud-based platforms.

A cross-platform representation of models as sets of equations
    that facilitates modularity in model building and allows users to harness
    modern techniques for numerical integration and data visualization.
    Documentation is provided by several vignettes included in this package;
    also see Lochocki et al. (2022) <doi:10.1093/insilicoplants/diac003>.

The Biodem package provides a number of functions for Biodemographic analysis.

Graphical User Interface (via the R-Commander) and utility functions (often based on the vegan package) for statistical analysis of biodiversity and ecological communities, including species accumulation curves, diversity indices, Renyi profiles, GLMs for analysis of species abundance and presence-absence, distance matrices, Mantel tests, and cluster, constrained and unconstrained ordination analysis. A book on biodiversity and community ecology analysis is available for free download from the website. In 2012, methods for (ensemble) suitability modelling and mapping were expanded in the package.

A tool to perform all different statistical tests and calculations
    needed by Biological dosimetry Laboratories. Detailed documentation is
    available in <https://biodosetools-team.github.io/documentation/>.

Multilevel ecological data series (MEDS) are sequences of observations ordered according to temporal/spatial hierarchies that are defined by sample designs, with sample variability confined to ecological factors. Dendroclimatic MEDS of tree rings and climate are modeled into normalized fluctuations of tree growth and aridity.  Modeled fluctuations (model frames) are compared with Mantel correlograms on multiple levels defined by sample design. Package implementation can be understood by running examples in modelFrame(), and muleMan() functions.