Provides classes and functions to calculate various 
             distance measures and routes in heterogeneous geographic 
             spaces represented as grids. The package implements measures
             to model dispersal histories first presented by van Etten and
             Hijmans (2010) <doi:10.1371/journal.pone.0012060>. Least-cost
             distances as well as more complex distances based on (constrained)
             random walks can be calculated. The distances implemented in 
             the package are used in geographical genetics, accessibility 
             indicators, and may also have applications in other fields of
             geospatial analysis.

Retrieve datasets from the 'Global Data Lab' website <https://globaldatalab.org>
    directly into R data frames. Functions are provided to reference available options
    (indicators, levels, countries, regions) as well.

A toolkit with functions to fit, plot, summarize, and apply Generalized Dissimilarity Models. Mokany K, Ware C, Woolley SNC, Ferrier S, Fitzpatrick MC (2022) <doi:10.1111/geb.13459> Ferrier S, Manion G, Elith J, Richardson K (2007) <doi:10.1111/j.1472-4642.2007.00341.x>.

Functions to compute the Generalized Dynamic Principal Components
    introduced in Peña and Yohai (2016) <DOI:10.1080/01621459.2015.1072542>. The implementation
    includes an automatic procedure proposed in Peña, Smucler and Yohai (2020) <DOI:10.18637/jss.v092.c02>
    for the identification of both the number of lags to be used
    in the generalized dynamic principal components as well as the number of components required
    for a given reconstruction accuracy.

Convert GDP time series data from one unit to
    another. All common GDP units are included, i.e. current and constant
    local currency units, US$ via market exchange rates and international
    dollars via purchasing power parities.

Contains a function called gds() which accepts three input
    parameters like lower limits, upper limits and the frequencies of the
    corresponding classes. The gds() function calculate and return the values
    of mean ('gmean'), median ('gmedian'), mode ('gmode'), variance ('gvar'), standard
    deviation ('gstdev'), coefficient of variance ('gcv'), quartiles ('gq1', 'gq2', 'gq3'),
    inter-quartile range ('gIQR'), skewness ('g1'), and kurtosis ('g2') which facilitate
    effective data analysis. For skewness and kurtosis calculations we use moments.