gdverse - Analysis of Spatial Stratified Heterogeneity
Detecting spatial associations via spatial stratified heterogeneity, accounting for spatial dependencies, interpretability, complex interactions, and robust stratification. In addition, it supports the spatial stratified heterogeneity family described in Lv et al. (2025)<doi:10.1111/tgis.70032>.
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geographical-detectorgeoinformaticsgeospatial-analysisspatial-analysisspatial-data-analysisspatial-statisticsspatial-stratified-heterogeneitycpp
8.91 score 89 stars 2 dependents 27 scripts 499 downloadsspEDM - Spatial Empirical Dynamic Modeling
Inferring causation from spatial cross-sectional data through empirical dynamic modeling (EDM), with methodological extensions including geographical convergent cross mapping from Gao et al. (2023) <doi:10.1038/s41467-023-41619-6>, as well as the spatial causality test following the approach of Herrera et al. (2016) <doi:10.1111/pirs.12144>, together with geographical pattern causality proposed in Zhang & Wang (2025) <doi:10.1080/13658816.2025.2581207>.
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causal-analysiscausal-discoverycausal-inferencecausal-influencecausal-interactionscausal-networkscppempirical-dynamic-modelinggeoinformaticsgeospatial-causalityspatial-analysisspatial-data-analysisspatial-data-sciencespatial-statisticsopenblascppopenmp
8.41 score 67 stars 23 scripts 764 downloads
GD - Geographical Detectors for Assessing Spatial Factors
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>. The model has been applied in factor exploration of road performance and multi-scale spatial segmentation for network data, as described in Yongze Song (2018) <doi:10.3390/rs10111696> and Yongze Song (2020) <doi:10.1109/TITS.2020.3001193>, respectively.
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geographical-detectorspatial-stratified-heterogeneity
7.65 score 16 stars 59 scripts 3.1k downloadstEDM - Temporal Empirical Dynamic Modeling
Inferring causation from time series data through empirical dynamic modeling (EDM), with methods such as convergent cross mapping from Sugihara et al. (2012) <doi:10.1126/science.1227079>, partial cross mapping introduced by Leng et al. (2020) <doi:10.1038/s41467-020-16238-0>, and cross mapping cardinality described in Tao et al. (2023) <doi:10.1016/j.fmre.2023.01.007>, following a systematic description proposed in Lyu et al. (2026) <doi:10.1016/j.compenvurbsys.2026.102435>.
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causal-analysiscausal-discoverycausal-inferencechaoscppdynamical-systemsempirical-dynamic-modelingtemporal-causal-discoverytemporal-causalitytime-seriestime-series-analysisurban-analyticsurban-data-scienceopenblascppopenmp
6.83 score 64 stars 2 scripts 263 downloadspc - Pattern Causality Analysis
Infer causation from observational data through pattern causality analysis (PC), with original algorithm for time series data from Stavroglou et al. (2020) <doi:10.1073/pnas.1918269117>, as well as methodological extensions for spatial cross-sectional data introduced by Zhang & Wang (2025) <doi:10.1080/13658816.2025.2581207>, together with a systematic description proposed in Lyu et al. (2026) <doi:10.1016/j.compenvurbsys.2026.102435>.
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causal-discoverycausal-interactionscausalitycausality-algorithmscausality-analysiscppcross-mappingdynamical-systemsempirical-dynamic-modelinggeoinformaticspattern-causalityspatial-data-analysisspatial-statisticstime-seriestime-series-analysiscpp
6.72 score 17 stars 103 scripts 487 downloads
sdsfun - Spatial Data Science Complementary Features
Wrapping and supplementing commonly used functions in the R ecosystem related to spatial data science, while serving as a basis for other packages maintained by Wenbo Lyu.
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geoinformaticsspatial-data-analysisspatial-data-sciencespatial-statisticsopenblascpp
6.47 score 20 stars 11 dependents 6 scripts 473 downloadsinfoxtr - Information-Theoretic Measures for Revealing Variable Interactions
Implements information-theoretic measures to explore variable interactions, including KSG mutual information estimation for continuous variables from Kraskov et al. (2004) <doi:10.1103/PhysRevE.69.066138>, knockoff conditional mutual information described in Zhang & Chen (2025) <doi:10.1126/sciadv.adu6464>, synergistic-unique-redundant decomposition introduced by Martinez-Sanchez et al. (2024) <doi:10.1038/s41467-024-53373-4>, allowing detection of complex and diverse relationships among variables.
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bioinformaticscausal-discoverycausal-interactioncausalitycausality-analysiscppdata-drivendata-driven-decision-makingdata-sciencegeoinformaticsinfo-theoryinformaticsinformation-theoryspatial-analysisspatial-data-sciencespatial-statisticsstatistical-inferencetime-seriestime-series-analysiscpp
6.27 score 53 stars 526 downloads
geocomplexity - Mitigating Spatial Bias Through Geographical Complexity
The geographical complexity of individual variables can be characterized by the differences in local attribute variables, while the common geographical complexity of multiple variables can be represented by fluctuations in the similarity of vectors composed of multiple variables. In spatial regression tasks, the goodness of fit can be improved by incorporating a geographical complexity representation vector during modeling, using a geographical complexity-weighted spatial weight matrix, or employing local geographical complexity kernel density. Similarly, in spatial sampling tasks, samples can be selected more effectively by using a method that weights based on geographical complexity. By optimizing performance in spatial regression and spatial sampling tasks, the spatial bias of the model can be effectively reduced.
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geographical-complexitygeospatial-analysisspatial-regressionspatial-relationsspatial-samplingspatial-statisticsopenblascppopenmp
5.90 score 22 stars 12 scripts 310 downloadsHSAR - Hierarchical Spatial Autoregressive Model
A Hierarchical Spatial Autoregressive Model (HSAR), based on a Bayesian Markov Chain Monte Carlo (MCMC) algorithm (Dong and Harris (2014) <doi:10.1111/gean.12049>). The creation of this package was supported by the Economic and Social Research Council (ESRC) through the Applied Quantitative Methods Network: Phase II, grant number ES/K006460/1.
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spatial-econometricsspatial-regressionspatial-statisticsopenblascpp
5.71 score 15 stars 34 scripts 176 downloads
itmsa - Information-Theoretic Measures for Spatial Association
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>).
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cpp
5.34 score 11 stars 182 downloadsinfocausality - Information-Theoretic Measure of Causality
Methods for quantifying temporal and spatial causality through information flow, and decomposing it into unique, redundant, and synergistic components, following the framework described in Martinez-Sanchez et al. (2024) <doi:10.1038/s41467-024-53373-4>.
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causal-discoverycausalityentropy-measuresinfo-theoryinformation-flowtemporal-causalitytime-series-analysiscpp
5.26 score 6 stars 196 downloads
cisp - A Correlation Indicator Based on Spatial Patterns
Utilizes spatial association marginal contributions derived from spatial stratified heterogeneity to capture the degree of correlation between spatial patterns.
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associationcorrelationgeoinformaticssimilarity-measurementspatial-patterns
5.18 score 10 stars 2 scripts 172 downloadsgeosimilarity - Geographically Optimal Similarity
Understanding spatial association is essential for spatial statistical inference, including factor exploration and spatial prediction. Geographically optimal similarity (GOS) model is an effective method for spatial prediction, as described in Yongze Song (2022) <doi:10.1007/s11004-022-10036-8>. GOS was developed based on the geographical similarity principle, as described in Axing Zhu (2018) <doi:10.1080/19475683.2018.1534890>. GOS has advantages in more accurate spatial prediction using fewer samples and critically reduced prediction uncertainty.
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geoinformaticsgeospatial-analyticsspatial-predictionsspatial-statistics
5.00 score 10 stars 6 scripts 506 downloads
localsp - Local Indicator of Stratified Power
Implements a local indicator of stratified power to analyze local spatial stratified association and demonstrate how spatial stratified association changes spatially and in local regions, as outlined in Hu et al. (2024) <doi:10.1080/13658816.2024.2437811>.
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4.65 score 3 stars 169 downloads
sshicm - Information Consistency-Based Measures for Spatial Stratified Heterogeneity
Spatial stratified heterogeneity (SSH) denotes the coexistence of within-strata homogeneity and between-strata heterogeneity. Information consistency-based methods provide a rigorous approach to quantify SSH and evaluate its role in spatial processes, grounded in principles of geographical stratification and information theory (Bai, H. et al. (2023) <doi:10.1080/24694452.2023.2223700>; Wang, J. et al. (2024) <doi:10.1080/24694452.2023.2289982>).
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geoinformaticsgeospatial-analysisinformation-theoryspatial-statisticsspatial-stratified-heterogeneitycpp
4.54 score 7 stars 2 scripts 141 downloadscoupling - Analysis of Coupling Coordination Degree
Implements coupling coordination degree (CCD) models and supports metacoupling analysis following Tang et al. (2021) <doi:10.1016/j.scs.2021.103405>.
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couplingcoupling-analysiscoupling-coordination-degreeintracouplingmeta-couplingmetacouplingpericouplingspatial-analysisspatial-data-analysisspatial-data-sciencetelecouplingcpp
4.48 score 3 stars
sesp - Spatially Explicit Stratified Power
Assesses spatial associations between variables through an equivalent geographical detector (q-statistic) within a regression framework and incorporates a spatially explicit stratified power model by integrating spatial dependence and spatial stratified heterogeneity, facilitating the modeling of complex spatial relationships.
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spatial-explicit-geographical-detectorspatial-stratified-heterogeneitycpp
4.41 score 17 stars 4 scripts
spEcula - Spatial Prediction Methods In R
Advanced spatial prediction methods based on various spatial relationships.
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geoinformaticsgisciencespatial-analysisspatial-predictionsspatial-statistics
4.36 score 23 stars 6 scripts
geocn - Loads Spatial Data Sets of China
Providing various commonly used spatial data related to Chinese regions in the R programming environment.
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chinachina-regiongeospatial-visualizationmaps
4.32 score 21 stars 10 scripts
tidyrgeoda - A tidy interface for rgeoda
An interface for 'rgeoda' to integrate with 'sf' objects and the 'tidyverse'.
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geocomputationgeoinformaticsgisciencespatial-analysisspatial-statistics
4.26 score 18 stars 5 scriptsSecDim - The Second Dimension of Spatial Association
Most of the current methods explore spatial association using observations at sample locations, which are defined as the first dimension of spatial association (FDA). The proposed concept of the second dimension of spatial association (SDA), as described in Yongze Song (2022) <doi:10.1016/j.jag.2022.102834>, aims to extract in-depth information about the geographical environment from locations outside sample locations for exploring spatial association.
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spatial-associationspatial-predictions
2.70 score 1 stars 2 scripts 7 downloads