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A genetic algorithm plus derivative optimizer.

Provides spatial data analysis functionalities including Exploratory Spatial Data Analysis, 
    Spatial Cluster Detection and Clustering Analysis, Regionalization, etc. based on the C++ source code 
    of 'GeoDa', which is an open-source software tool that serves as an introduction to spatial data analysis.
    The 'GeoDa' software and its documentation are available at <https://geodacenter.github.io>.

Provides the hybrid Bayesian method Geometric Density Estimation. On the one hand, it scales the dimension of our data, on the other it performs inference. The method is fully described in the paper "Scalable Geometric Density Estimation" by Y. Wang, A. Canale, D. Dunson (2016) <http://proceedings.mlr.press/v51/wang16e.pdf>.

A lightweight implementation of the geomorphon terrain
  form classification algorithm of Jasiewicz and Stepinski (2013)
  <doi:10.1016/j.geomorph.2012.11.005> based largely on the 
  'GRASS GIS' 'r.geomorphon' module. This implementation employs 
  a novel algorithm written in C++ and 'RcppParallel'.

Provides an interface to the Python package 'Geomstats' authored by Miolane et 
    al. (2020) <arXiv:2004.04667>.

Supports analysis of spatial data processed with the 'GeoPAT' 2
    software <https://github.com/Nowosad/geopat2>. 
    Available features include creation of a grid based on the 'GeoPAT' 2
    grid header file and reading a 'GeoPAT' 2 text outputs.

Create, read, and write 'GEXF' (Graph Exchange 'XML' Format) graph files (used in 'Gephi' and others). Using the 'XML' package, rgexf allows reading and writing GEXF files, including attributes, 'GEXF' visual attributes (such as color, size, and position), network dynamics (for both edges and nodes), and edges' weights. Users can build/handle graphs element-by-element or massively through data frames, visualize the graph on a web browser through 'gexf-js' (a 'javascript' library), and interact with the 'igraph' package.