A method for modeling genetic data as a combination of discrete
    layers, within each of which relatedness may decay continuously with geographic
    distance. This package contains code for running analyses (which are implemented
    in the modeling language 'rstan') and visualizing and interpreting output. See the
    paper for more details on the model and its utility.

Prints code that can be used to recreate R objects. In a
    sense it is similar to 'base::dput()' or 'base::deparse()' but
    'constructive' strives to use idiomatic constructors.

Data package for the supplementary data in Prem et al. (2017)
    <doi:10.1371/journal.pcbi.1005697> and Prem et al. 
    <doi:10.1371/journal.pcbi.1009098>.
    Provides easy access to contact data for 177 countries, for use in
    epidemiological, demographic or social sciences research.

Download, cache, and manage social contact survey data from
  the social contact data community on Zenodo
  (<https://zenodo.org/communities/social_contact_data>) for use in
  infectious disease modelling. Provides functions to list available
  surveys, download survey files with automatic caching, and retrieve
  citations. Contact survey data describe who contacts whom in a population
  and are used to parameterise age-structured transmission models, for
  example via the 'socialmixr' package. The surveys available include those
  from the POLYMOD study (Mossong et al. (2008)
  <doi:10.1371/journal.pmed.0050074>) and other social contact data shared
  on Zenodo.

Extends the functionality of base 'R' lists and provides
    specialized data structures 'deque', 'set', 'dict', and 'dict.table',
    the latter to extend the 'data.table' package.

Automates the process of containerizing 'R' projects. The core
    function of 'containr' is 'generate_dockerfile()', which analyzes an 'R'
    project's environment and dependencies via an 'renv' lock file and generates
    a ready-to-use 'Dockerfile' that encapsulates the computational setup. The
    package helps researchers build portable and consistent workflows so that
    analyses can be reliably shared, archived, and rerun across systems. See
    R Core Team (2025) <https://www.R-project.org/>, Ushey et al. (2025)
    <https://CRAN.R-project.org/package=renv>, and Docker Inc. (2025)
    <https://www.docker.com/>.