Software to simulate population change across space and time. Visintin et al. (2020) <doi:10.1111/2041-210X.13354>.

Provides function to estimate multiple change points
    using marginal likelihood method. See the Manual file in data folder for
    a detailed description of all functions, and a walk through tutorial.
    For more information of the method, please see Du, Kao and Kou (2016) 
    <doi:10.1080/01621459.2015.1006365>.

Functions to perform stepwise split regularized regression. The approach first 
             uses a stepwise algorithm to split the variables into the models with a goodness
             of fit criterion, and then regularization is applied to each model. The weights 
             of the models in the ensemble are determined based on a criterion selected by 
             the user.

Collection of stepwise procedures to conduct multiple hypotheses testing. The details of the stepwise algorithm can be found in Romano and Wolf (2007) <DOI:10.1214/009053606000001622> and Hsu, Kuan, and Yen (2014) <DOI:10.1093/jjfinec/nbu014>.

Functions for the collection of 3D points and curves using a stereo camera setup.

This is a collection of various kinds of data with broad uses for teaching. 
    My students, and academics like me who teach the same topics I teach, should find 
    this useful if their teaching workflow is also built around the R programming 
    language. The applications are multiple but mostly cluster on topics of statistical
    methodology, international relations, and political economy.

Work with containers over the Docker API.  Rather than
    using system calls to interact with a docker client, using the
    API directly means that we can receive richer information from
    docker.  The interface in the package is automatically generated
    using the 'OpenAPI' (a.k.a., 'swagger') specification, and all
    return values are checked in order to make them type stable.

These are miscellaneous functions that I find useful for my research and teaching.
    The contents include themes for plots, functions for simulating
    quantities of interest from regression models, functions for simulating various 
    forms of fake data for instructional/research purposes, and many more. All told, the functions
    provided here are broadly useful for data organization, data presentation, data recoding, 
    and data simulation.