stats, utils, BAMMtools, ggplot2, reshape2

Geographical detectors for measuring spatial stratified heterogeneity,
as described in Jinfeng Wang (2010) <doi:10.1080/13658810802443457> and
Jinfeng Wang (2016) <doi:10.1016/j.ecolind.2016.02.052>. Includes the
optimal discretization of continuous data, four primary functions of
geographical detectors, comparison of size effects of spatial unit and
the visualizations of results. The descriptions of the package, methods
and case datasets refer to the citation information below.
stats, utils, BAMMtools, ggplot2, reshape2, grid

Geographical detectors for measuring spatial stratified heterogeneity,
as described in Jinfeng Wang (2010) <doi:10.1080/13658810802443457> and
Jinfeng Wang (2016) <doi:10.1016/j.ecolind.2016.02.052>. Includes the
optimal discretization of continuous data, four primary functions of
geographical detectors, comparison of size effects of spatial unit and
the visualizations of results. To use the package and to refer the
descriptions of the package, methods and case datasets, please cite
Yongze Song (2020) <doi:10.1080/15481603.2020.1760434>.

Datasets for the Book Graphical Data Analysis with R
Datasets used in the book 'Graphical Data Analysis with R' (Antony Unwin, CRC Press 2015).

A toolbox for the analysis of categorical data in social
sciences, and especially Geometric Data Analysis.
This package contains functions for 'specific' MCA (Multiple Correspondence Analysis),
'class specific' MCA, computing and plotting structuring factors and concentration ellipses,
'standardized' MCA, inductive tests and others tools for Geometric Data Analysis. It also provides functions
for the translation of logit models coefficients into percentages (forthcoming), weighted contingency tables and an association
measure - i.e. Percentages of Maximum Deviation from Independence (PEM).

A Toolbox for the Analysis of Categorical Data in Social
Sciences, and Especially Geometric Data Analysis
Contains functions for 'specific' MCA (Multiple Correspondence Analysis),
'class specific' MCA, computing and plotting structuring factors and concentration ellipses,
Multiple Factor Analysis, 'standardized' MCA, inductive tests and others tools for Geometric Data Analysis. It also provides functions
for the translation of logit models coefficients into percentages, weighted contingency tables and an association
measure - i.e. Percentages of Maximum Deviation from Independence (PEM).