Stacking arrays according to dimension names, subset-aware
    splitting and mapping of functions, intersecting along arbitrary
    dimensions, converting to and from data.frames, and many other helper
    functions.

A toolkit for medical records data analysis. The 'naryn'
    package implements an efficient data structure for storing medical
    records, and provides a set of functions for data extraction,
    manipulation and analysis.

Provides functions to access NASA's Earth Imagery and Assets API
    and the Earth Observatory Natural Event Tracker (EONET) webservice.

An API client for NASA POWER global meteorology, surface solar
    energy and climatology data API.  POWER (Prediction Of Worldwide Energy
    Resources) data are freely available for download with varying spatial 
    resolutions dependent on the original data and with several temporal
    resolutions depending on the POWER parameter and community.  This work is
    funded through the NASA Earth Science Directorate Applied Science Program.
    For more on the data themselves, the methodologies used in creating, a web-
    based data viewer and web access, please see <https://power.larc.nasa.gov/>.

This package contains tidied data from the ASA 2006 data expo,
    as well as a number of useful other related data sets.

Functions for interacting directly with the Nasdaq Data Link API to offer data in a number of formats usable in R, downloading a zip with all data from a Nasdaq Data Link database, and the ability to search. This R package uses the Nasdaq Data Link API. For more information go to <https://docs.data.nasdaq.com/>. For more help on the package itself go to <https://data.nasdaq.com/tools/r>.

Implement and enhance the performance of spatial fuzzy clustering using Fuzzy Geographically Weighted Clustering with various optimization algorithms, mainly from Xin She Yang (2014) <ISBN:9780124167438> with book entitled Nature-Inspired Optimization Algorithms. The optimization algorithm is useful to tackle the disadvantages of clustering inconsistency when using the traditional approach. The distance measurements option is also provided in order to increase the quality of clustering results. The Fuzzy Geographically Weighted Clustering with nature inspired optimisation algorithm was firstly developed by Arie Wahyu Wijayanto and Ayu Purwarianti (2014) <doi:10.1109/CITSM.2014.7042178> using Artificial Bee Colony algorithm.