Record and generate a 'gif' of your 'R' sessions plots. When creating 
    a visualization, there is inevitably iteration and refinement that occurs. 
    Automatically save the plots made to a specified directory, previewing them 
    as they would be saved. Then combine all plots generated into a 'gif'
    to show the plot refinement over time.

Implements the board game 'CamelUp' for use in introductory statistics classes using a Shiny app.

Quick and easy access to datasets that let you replicate the
    empirical examples in Cameron and Trivedi (2005) "Microeconometrics: Methods and
    Applications" (ISBN: 9780521848053).The data are available as soon as you install
    and load the package (lazy-loading) as data frames. The documentation includes
    reference to chapter sections and page numbers where the datasets are used.

Creates multi-label cell-types for single-cell RNA-sequencing data based on weighted VAM scoring of cell-type specific gene sets. Schiebout, Frost (2022) <https://psb.stanford.edu/psb-online/proceedings/psb22/schiebout.pdf>.

Collect  marketing data from Campaign Manager using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.

Explore and normalize American campaign finance
    data. Created by the Investigative Reporting Workshop to facilitate
    work on The Accountability Project, an effort to collect public data
    into a central, standard database that is more easily searched:
    <https://publicaccountability.org/>.

A generic, easy-to-use and intuitive
    pharmacokinetic/pharmacodynamic (PK/PD) simulation platform based on R
    packages 'rxode2', 'RxODE' and 'mrgsolve'. CAMPSIS provides an
    abstraction layer over the underlying processes of writing a PK/PD
    model, assembling a custom dataset and running a simulation. CAMPSIS
    has a strong dependency to the R package 'campsismod', which allows to
    read/write a model from/to files and adapt it further on the fly in
    the R environment. Package 'campsis' allows the user to assemble a
    dataset in an intuitive manner. Once the user’s dataset is ready, the
    package is in charge of preparing the simulation, calling 'rxode2',
    'RxODE' or 'mrgsolve' (at the user's choice) and returning the
    results, for the given model, dataset and desired simulation settings.