Implements a geographically weighted non-negative principal
    components analysis, which consists of the fusion of geographically
    weighted and sparse non-negative principal components analyses
    <doi:10.17608/k6.auckland.9850826.v1>.

Computes G-Wishart normalising constants through a Fourier approach. Either exact analytical results, numerical integration or Monte Carlo estimation are employed. Details at C. Wong, G. Moffa and J. Kuipers (2024), <doi:10.48550/arXiv.2404.06803>. Also includes approximations of the ratio of normalising constants, see details at C. Wong, G. Moffa and J. Kuipers (2025), <doi:10.48550/arXiv.2503.13046>.

Implements a geographically weighted partial correlation which is 
    an extension from gwss() function in the 'GWmodel' package 
    (Percival and Tsutsumida (2017) <doi:10.1553/giscience2017_01_s36>).

An interactive mapping tool for geographically weighted correlation and partial correlation. Geographically weighted partial correlation coefficients are calculated following (Percival and Tsutsumida, 2017)<doi:10.1553/giscience2017_01_s36> and are described in greater detail in (Tsutsumida et al., 2019)<doi:10.5194/ica-abs-1-372-2019> and (Percival et al., 2021)<arXiv:2101.03491>.

A modern, first implementation of Geographically Weighted Panel Regression (GWPR) for spatial panel data. The package provides a unified public API supporting Gaussian and binomial family models, within/pooling/random panel effects, three bandwidth search strategies (grid, Stochastic Gradient Descent, random), five kernel functions, and optional parallel execution via the 'future' framework. Diagnostic tools include spatial Moran's I, local F-test, Hausman test, and Lagrange Multiplier test.

Fits Weighted Quantile Sum (WQS) regression  (Carrico et al. (2014) <doi:10.1007/s13253-014-0180-3>), a random subset implementation of WQS (Curtin et al. (2019) <doi:10.1080/03610918.2019.1577971>), a repeated holdout validation WQS (Tanner et al. (2019) <doi:10.1016/j.mex.2019.11.008>) and a WQS with 2 indices (Renzetti et al. (2023) <doi:10.3389/fpubh.2023.1289579>) for continuous, binomial, multinomial, Poisson, quasi-Poisson and negative binomial outcomes.