README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "##",
  fig.path = "man/figures/README-",
  out.width = "100%",
  warning = FALSE,
  message = FALSE
)
```

# gdverse <a href="https://stscl.github.io/gdverse/"><img src="man/figures/logo.png" align="right" height="139" alt="gdverse website" /></a>

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**Analysis of Spatial Stratified Heterogeneity**

## Overview

*gdverse* consolidates cutting-edge SSH methodologies into a unified toolkit, redefining spatial association measurement as the evolutionary successor to [geodetector](https://CRAN.R-project.org/package=geodetector) and [GD](https://CRAN.R-project.org/package=GD) in the R ecosystem.

Current models and functions provided by **gdverse** are:

| *Model* | *Function* | *Support* |
|---------------------|--------------------|------------------|
|[GD][2]|`gd()`| ✔️ |
|[OPGD][3]|`opgd()`| ✔️ |
|[GOZH][4]|`gozh()`| ✔️ |
|[LESH][5]|`lesh()`| ✔️ |
|[SPADE][6]|`spade()`| ✔️ |
|[IDSA][7]|`idsa()`| ✔️ |
|[RGD][8]|`rgd()`| ✔️ |
|[RID][9]|`rid()`| ✔️ |
|[SRSGD][10]|`srsgd()`| ✔️ |

## Installation

- Install from [CRAN](https://CRAN.R-project.org/package=gdverse) with:

``` r
install.packages("gdverse", dependencies = TRUE)
```

- Install development binary version from [R-universe](https://stscl.r-universe.dev/gdverse) with:

``` r
install.packages("gdverse",
                 repos = c("https://stscl.r-universe.dev",
                           "https://cloud.r-project.org"),
                 dependencies = TRUE)
```

- Install development source version from [GitHub](https://github.com/stscl/gdverse) with:

``` r
if (!requireNamespace("pak", quietly = TRUE)) {
    install.packages("pak")
}
pak::pak("stscl/gdverse", dependencies = TRUE)
```

✨ Please ensure that **Rcpp** is properly installed and the appropriate **C++** compilation environment is configured in advance if you want to install **gdverse** from github.

✨ The **gdverse** package supports the use of robust discretization for the [robust geographical detector][8] and [robust interaction detector][9]. For details on using them, please refer to <https://stscl.github.io/gdverse/articles/rgdrid.html>.

## Example

```{r example_gdverse}
library(gdverse)
data("ndvi")
ndvi
```

### OPGD model

```{r}
discvar = names(ndvi)[-1:-3]
discvar
ndvi_opgd = opgd(NDVIchange ~ ., data = ndvi, 
                 discvar = discvar, cores = 6)
ndvi_opgd
```

### GOZH model

```{r}
g = gozh(NDVIchange ~ ., data = ndvi)
g
```

## CITATION

Please cite **[gdverse][1]** as:

```
Lv, W., Lei, Y., Liu, F., Yan, J., Song, Y., Zhao, W., 2025. gdverse: An R Package for Spatial Stratified Heterogeneity Family. Transactions in GIS 29. https://doi.org/10.1111/tgis.70032
```

A BibTeX entry for LaTeX users is:

``` bib
@article{lyu2025gdverse, 
    title={{gdverse}: An {R} Package for Spatial Stratified Heterogeneity Family}, 
    volume={29}, 
    ISSN={1467-9671},
    DOI={10.1111/tgis.70032},
    number={2}, 
    journal={Transactions in GIS}, 
    publisher={Wiley}, 
    author={Lv, Wenbo and Lei, Yangyang and Liu, Fangmei and Yan, Jianwu and Song, Yongze and Zhao, Wufan},
    year={2025}, 
    month={mar},
    pages={e70032}
}
```

## Reference

Lv, W., Lei, Y., Liu, F., Yan, J., Song, Y., Zhao, W., 2025. gdverse: An R Package for Spatial Stratified Heterogeneity Family. Transactions in GIS 29. [https://doi.org/10.1111/tgis.70032][1].

Wang, J., Li, X., Christakos, G., Liao, Y., Zhang, T., Gu, X., Zheng, X., 2010. Geographical Detectors‐Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China. International Journal of Geographical Information Science 24, 107–127. [https://doi.org/10.1080/13658810802443457][2].

Song, Y., Wang, J., Ge, Y., Xu, C., 2020. An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data. GIScience & Remote Sensing 57, 593–610. [https://doi.org/10.1080/15481603.2020.1760434][3].

Luo, P., Song, Y., Huang, X., Ma, H., Liu, J., Yao, Y., Meng, L., 2022. Identifying determinants of spatio-temporal disparities in soil moisture of the Northern Hemisphere using a geographically optimal zones-based heterogeneity model. ISPRS Journal of Photogrammetry and Remote Sensing 185, 111–128. [https://doi.org/10.1016/j.isprsjprs.2022.01.009][4].

Li, Y., Luo, P., Song, Y., Zhang, L., Qu, Y., Hou, Z., 2023. A locally explained heterogeneity model for examining wetland disparity. International Journal of Digital Earth 16, 4533–4552. [https://doi.org/10.1080/17538947.2023.2271883][5].

Cang, X., Luo, W., 2018. Spatial association detector (SPADE). International Journal of Geographical Information Science 32, 2055–2075. [https://doi.org/10.1080/13658816.2018.1476693][6].

Song, Y., Wu, P., 2021. An interactive detector for spatial associations. International Journal of Geographical Information Science 35, 1676–1701. [https://doi.org/10.1080/13658816.2021.1882680][7].

Zhang, Z., Song, Y., Wu, P., 2022. Robust geographical detector. International Journal of Applied Earth Observation and Geoinformation 109, 102782. [https://doi.org/10.1016/j.jag.2022.102782][8].

Zhang, Z., Song, Y., Karunaratne, L., Wu, P., 2024. Robust interaction detector: A case of road life expectancy analysis. Spatial Statistics 59, 100814. [https://doi.org/10.1016/j.spasta.2024.100814][9].

Bai, H., Li, D., Ge, Y., Wang, J., Cao, F., 2022. Spatial rough set-based geographical detectors for nominal target variables. Information Sciences 586, 525–539. [https://doi.org/10.1016/j.ins.2021.12.019][10].

Wang, J., Zhang, T., Fu, B., 2016. A measure of spatial stratified heterogeneity. Ecological Indicators 67, 250–256. [https://doi.org/10.1016/j.ecolind.2016.02.052][11].

Wang, J., Haining, R., Zhang, T., Xu, C., Hu, M., Yin, Q., Li, L., Zhou, C., Li, G., Chen, H., 2024. Statistical Modeling of Spatially Stratified Heterogeneous Data. Annals of the American Association of Geographers 114, 499–519. [https://doi.org/10.1080/24694452.2023.2289982][12].

&nbsp; 

[1]: https://doi.org/10.1111/tgis.70032
[2]: https://doi.org/10.1080/13658810802443457
[3]: https://doi.org/10.1080/15481603.2020.1760434
[4]: https://doi.org/10.1016/j.isprsjprs.2022.01.009
[5]: https://doi.org/10.1080/17538947.2023.2271883
[6]: https://doi.org/10.1080/13658816.2018.1476693
[7]: https://doi.org/10.1080/13658816.2021.1882680
[8]: https://doi.org/10.1016/j.jag.2022.102782
[9]: https://doi.org/10.1016/j.spasta.2024.100814
[10]: https://doi.org/10.1016/j.ins.2021.12.019
[11]: https://doi.org/10.1016/j.ecolind.2016.02.052
[12]: https://doi.org/10.1080/24694452.2023.2289982