A system for embedded scientific computing and reproducible research with R.
    The OpenCPU server exposes a simple but powerful HTTP api for RPC and data interchange
    with R. This provides a reliable and scalable foundation for statistical services or 
    building R web applications. The OpenCPU server runs either as a single-user development
    server within the interactive R session, or as a multi-user Linux stack based on Apache2. 
    The entire system is fully open source and permissively licensed. The OpenCPU website
    has detailed documentation and example apps.

Non-spatial and spatial open-population capture-recapture analysis.

Exposes some of the available 'OpenCV' <https://opencv.org/> algorithms,
    such as a QR code scanner, and edge, body or face detection. These can either be 
    applied to analyze static images, or to filter live video footage from a camera device.

Access data from the "City of Toronto
    Open Data Portal" (<https://open.toronto.ca>) directly from R.

Enables the usage of the OpenDota API from <https://www.opendota.com/>, get game lists, and download JSON's of parsed replays from
    the OpenDota API. Also has functionality to execute own code to extract the specific parts of the JSON file.

An implementation of DuMouchel's (1999) <doi:10.1080/00031305.1999.10474456>
  Bayesian data mining method for the market basket problem.
  Calculates Empirical Bayes Geometric Mean (EBGM) and posterior quantile scores
  using the Gamma-Poisson Shrinker (GPS) model to find unusually large cell
  counts in large, sparse contingency tables. Can be used to find unusually high
  reporting rates of adverse events associated with products. In general, can be
  used to mine any database where the co-occurrence of two variables or items is
  of interest. Also calculates relative and proportional reporting ratios.
  Builds on the work of the 'PhViD' package, from which much of the code is
  derived. Some of the added features include stratification to adjust for
  confounding variables and data squashing to improve computational efficiency.
  Includes an implementation of the EM algorithm for hyperparameter estimation
  loosely derived from the 'mederrRank' package.