The increasing popularity of geographically weighted (GW) techniques has resulted in the development of several R packages, such as 'GWmodel'. To facilitate their usages, 'GWmodelVis' provides a 'shiny'-based interactive visualization toolkit for geographically weighted (GW) models.
    It includes a number of visualization tools, including dynamic mapping of parameter surfaces, statistical visualization, sonification and exporting  videos via 'FFmpeg'.

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>.

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>.

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.

It implements a hybrid spatial model for improved spatial prediction by combining the variable selection capability of
             LASSO (Least Absolute Shrinkage and Selection Operator) with the Geographically Weighted Regression (GWR) model that 
             captures the spatially varying relationship efficiently. For method details see, Wheeler, D.C.(2009).<DOI:10.1068/a40256>.
             The developed hybrid model efficiently selects the relevant variables by using LASSO as the first step; these selected variables
             are then incorporated into the GWR framework, allowing the estimation of spatially varying regression coefficients at unknown locations
             and finally predicting the values of the response variable at unknown test locations while taking into account the spatial heterogeneity of the data. 
             Integrating the LASSO and GWR models enhances prediction accuracy by considering spatial heterogeneity and capturing the local relationships between 
             the predictors and the response variable. The developed hybrid spatial model can be useful for spatial modeling, especially in scenarios involving complex 
             spatial patterns and large datasets with multiple predictor variables.