Geographically Weighted Partial Correlation Mapper
c(person(given = "Joseph Emile Honour",
family = "Percival",
role = c("aut", "cre"),
email = "ipercival@gmail.com",
comment = c(ORCID = "0000-0001-5941-4601")),
person(given = "Narumasa",
family = "Tsutsumida",
role = "aut",
email = "rsnaru.jp@gmail.com",
comment = c(ORCID = "0000-0002-6333-0301")))
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>.
config, golem, shiny, processx, attempt, DT, glue, htmltools,
shinydashboard, sf, dplyr, geodist, plotly, crosstalk, viridis,
leaflet, tools, shinyjs, Rcpp, corpcor, pkgload, methods
Joseph Emile Honour Percival [aut, cre]
(<https://orcid.org/0000-0001-5941-4601>),
Narumasa Tsutsumida [aut] (<https://orcid.org/0000-0002-6333-0301>)
Joseph Emile Honour Percival <ipercival@gmail.com>

c(
person("Gregory", "Connor", email = "gregory.connor@nuim.ie ", role = c("aut")),
person("Michael", "O'Neill", email = "m.oneill@ucd.ie", role = c("trl", "aut", "cre")))
Gregory Connor [aut],
Michael O'Neill [trl, aut, cre]
Computes the sample probability value (p-value)
for the estimated coefficient from a standard
genome-wide univariate regression.  It computes the exact
finite-sample p-value under the assumption that the measured
phenotype (the dependent variable in the regression) has
a known Bernoulli-normal mixture distribution.
Finite-sample genome-wide regression p-values (Gwrpv) with
a non-normally distributed phenotype (Gregory Connor and
Michael O'Neill, bioRxiv 204727 <doi:10.1101/204727>).

Geographically weighted regression with penalties and diagnostic
tools
Fits geographically weighted regression (GWR) models and uses tools to diagnose collinearity in the GWR models.