Title:	Information-Theoretic Measures for Spatial Association
Version:	0.1.0
Description:	Leveraging information-theoretic measures like mutual information and v-measure to quantify spatial associations between patterns (Nowosad and Stepinski (2018) <doi:10.1080/13658816.2018.1511794>; Bai, H. et al. (2023) <doi:10.1080/24694452.2023.2223700>).
License:	GPL-3
Encoding:	UTF-8
RoxygenNote:	7.3.2
URL:	https://stscl.github.io/itmsa/, https://github.com/stscl/itmsa
BugReports:	https://github.com/stscl/itmsa/issues
Depends:	R (≥ 4.1.0)
LinkingTo:	Rcpp, RcppThread
Imports:	dplyr, purrr, sdsfun (≥ 0.6.0), sf
Suggests:	knitr, Rcpp, RcppThread, readr, rmarkdown, tibble
VignetteBuilder:	knitr
NeedsCompilation:	yes
Packaged:	2024-12-22 14:12:03 UTC; dell
Author:	Wenbo Lv [aut, cre, cph]
Maintainer:	Wenbo Lv <lyu.geosocial@gmail.com>
Repository:	CRAN
Date/Publication:	2024-12-23 11:30:01 UTC

Information-Theoretic Measures for Spatial Association

Description

Information-Theoretic Measures for Spatial Association

Usage

itm(
  formula,
  data,
  method = c("vm", "icm"),
  beta = 1,
  unit = c("e", "2", "10"),
  seed = 42,
  permutation_number = 999
)

Arguments

Value

A tibble.

Examples

sim = readr::read_csv(system.file('extdata/sim.csv',package = 'itmsa'))

# Information-theoretical V-measure
itm(z1 ~ z2, data = sim, method = 'vm')
# Information Consistency-Based Measures
itm(z1 ~ z2, data = sim, method = 'icm')