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.

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/>.

Fits mixtures of multivariate contaminated normal distributions
        (with eigen-decomposed scale matrices) via the expectation conditional-
	maximization algorithm under a clustering or classification paradigm
	Methods are described in Antonio Punzo, Angelo Mazza, and Paul D McNicholas (2018) <doi:10.18637/jss.v085.i10>.

Provides methods for difference-in-differences with a continuous treatment and staggered treatment adoption. Includes estimation of treatment effects and causal responses as a function of the dose, event studies indexed by length of exposure to the treatment, and aggregation into overall average effects. Uniform inference procedures are included, along with both parametric and nonparametric models for treatment effects. The methods are based on Callaway, Goodman-Bacon, and Sant'Anna (2025) <doi:10.48550/arXiv.2107.02637>.