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.

Fast and efficient sampling from general univariate probability
    density functions. Implements a rejection sampling approach designed to take
    advantage of modern CPU caches and minimise evaluation of the target
    density for most samples. Many standard densities are internally implemented
    in 'C' for high performance, with general user defined densities also
    supported. A paper describing the methodology will be released soon.

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

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.

Tools for transport planning with an emphasis on spatial
    transport data and non-motorized modes.
    The package was originally developed to support the 'Propensity to Cycle Tool', a publicly available strategic cycle network planning tool
    (Lovelace et al. 2017) <doi:10.5198/jtlu.2016.862>, but has since been
    extended to support public transport routing and accessibility analysis
    (Moreno-Monroy et al. 2017) <doi:10.1016/j.jtrangeo.2017.08.012> and
    routing with locally hosted routing engines such as 'OSRM'
    (Lowans et al. 2023) <doi:10.1016/j.enconman.2023.117337>.
    The main functions are for creating and manipulating geographic "desire
    lines" from origin-destination (OD) data (building on the 'od'
    package); calculating routes on the transport network locally and via
    interfaces to routing services such as <https://cyclestreets.net/>
    (Desjardins et al. 2021) <doi:10.1007/s11116-021-10197-1>;
    and calculating route segment attributes such as bearing. The package
    implements the 'travel flow aggregration' method described in Morgan
    and Lovelace (2020) <doi:10.1177/2399808320942779> and the
    'OD jittering' method described in Lovelace et al. (2022)
    <doi:10.32866/001c.33873>.
    Further information on the package's aim and scope can be found in the
    vignettes and in a paper in the R Journal (Lovelace and Ellison 2018)
    <doi:10.32614/RJ-2018-053>, and in a paper outlining the landscape of
    open source software for geographic methods in transport planning
    (Lovelace, 2021) <doi:10.1007/s10109-020-00342-2>.