Author: Aboubacar Y. Touré and Célestin C. Kokonendji
Description: Univariate Poisson dispersion index for count data, univariate exponential variation index for positive continuous data functions are performed. Also, the generalized dispersion index and the marginal dispersion index functions are carried out. Finally, the generalized variation index and the marginal variation index functions are computed.

Description: Firstly, both functions of the univariate Poisson dispersion index (DI) for count data and the univariate exponential variation index (VI) for nonnegative continuous data are performed. Next, other functions of univariate indexes such the binomial dispersion index (DIb), the negative binomial dispersion index (DInb) and the inverse Gaussian variation index (VIiG) are given. Finally, we are computed some multivariate versions of these functions such that the generalized dispersion index (GDI) with its marginal one (MDI) and the generalized variation index (GVI) with its marginal one (MVI) too.

Title: Geographically Weighted Logistic Elastic Net Regression
Description: Fit a geographically weighted logistic elastic net regression.

Description: Fit a geographically weighted logistic elastic net regression. Detailed explanations can be found in Yoneoka et al. (2016): New algorithm for constructing area-based index with geographical heterogeneities and variable selection: An application to gastric cancer screening <doi:10.1038/srep26582>.
Imports: doParallel, geosphere, sp, spgwr, glmnet, foreach, methods,

Title: Geographically Weighted Panel Regression
Authors@R: c(person("Chao", "Li", role = c("aut", "cre"),
person("Shunsuke", "Managi", role = "aut",
Description: Geographically weighted panel regression is grounded in a branch of spatial statistics. Using geographically weights, the geographically weighted panel regression is try to solve the residuals from panel regression clustering spatially. To investigate whether the residuals cluster spatially, the Moran's I test is also improved. Furthermore, three local statistic tests are contained to help the users select model. The major references are Fotheringham et al. (2003, ISBN:978-0-470-85525-6) and Beenstock and Felsenstein (2019, ISBN:978-3-030-03614-0).
Imports: data.table, doParallel, dplyr, foreach, GWmodel, iterators,
lmtest, methods, parallel, plm, sp, stats