Contains the development of a tool that provides a web-based
    graphical user interface (GUI) to perform Techniques from a subset of
    spatial statistics known as geographically weighted (GW) models.
    Contains methods described by Brunsdon et al., 1996
    <doi:10.1111/j.1538-4632.1996.tb00936.x>, Brunsdon et al., 2002
    <doi:10.1016/s0198-9715(01)00009-6>, Harris et al., 2011
    <doi:10.1080/13658816.2011.554838>, Brunsdon et al., 2007
    <doi:10.1111/j.1538-4632.2007.00709.x>.

Geometric objects defined in 'geozoo' can be simulated or displayed in the R package 'tourr'.

Encode and decode the Google Encoded Polyline Algorithm Format (<https://developers.google.com/maps/documentation/utilities/polylinealgorithm>).

Maximum likelihood estimation, random values generation, density computation and other functions for the exponential-Poisson generalised exponential-Poisson and Poisson-exponential distributions. References include: Rodrigues G. C., Louzada F. and Ramos P. L. (2018). "Poisson-exponential distribution: different methods of estimation". Journal of Applied Statistics, 45(1): 128--144. <doi:10.1080/02664763.2016.1268571>. Louzada F., Ramos, P. L. and Ferreira, H. P. (2020). "Exponential-Poisson distribution: estimation and applications to rainfall and aircraft data with zero occurrence". Communications in Statistics--Simulation and Computation, 49(4): 1024--1043. <doi:10.1080/03610918.2018.1491988>. Barreto-Souza W. and Cribari-Neto F. (2009). "A generalization of the exponential-Poisson distribution". Statistics and Probability Letters, 79(24): 2493--2500. <doi:10.1016/j.spl.2009.09.003>.

Implements a new multiple imputation method that draws
    imputations from a latent joint multivariate normal model which
    underpins generally structured data. This model is constructed using a
    sequence of flexible conditional linear models that enables the
    resulting procedure to be efficiently implemented on high dimensional
    datasets in practice. See Robbins (2021) <arXiv:2008.02243>.