Provides multiple sources of stopwords, for use in text analysis and natural language processing.

Storm is a distributed real-time computation system. Similar to how
 Hadoop provides a set of general primitives for doing batch processing, Storm
 provides a set of general primitives for doing real-time computation.

    Storm includes a "Multi-Language" (or "Multilang") Protocol to allow
 implementation of Bolts and Spouts in languages other than Java.  This R
 extension provides implementations of utility functions to allow an application
 developer to focus on application-specific functionality rather than Storm/R
 communications plumbing.

Set of functions to quantify and map the
    behaviour of winds generated by tropical storms and cyclones in space
    and time. It includes functions to compute and analyze fields such as the maximum sustained wind field, power dissipation index and duration of exposure to winds above a given threshold. It also includes functions to map the trajectories as well as characteristics of the storms.

Creates and manages simple key-value stores.  These can
    use a variety of approaches for storing the data.  This package
    implements the base methods and support for file system, in-memory
    and DBI-based database stores.

Functions for retrieving general and specific data from the Norwegian Parliament,
  through the Norwegian Parliament API at <https://data.stortinget.no>.

Download, explore, and analyze Literary Theme Ontology themes
    and thematically annotated story data. To learn more about the project
    visit <https://github.com/theme-ontology/theming> and
    <https://www.themeontology.org>.

An interface to explore trends in Twitter data using the 
    'Storywrangler' Application Programming Interface (API), which can be found
    here: <https://github.com/janeadams/storywrangler>.

A toolkit for Reliability Availability and Maintainability (RAM) modeling of industrial process systems.

Combining Predictive Analytics and Experimental Design to Optimize Results. To be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals. Once a "good" training set is identified, the response variable can be obtained only for this set to build a model for predicting the response in the test set. The algorithms in the package can be tweaked to solve some other subset selection problems.