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SpatialRDD - Conduct Multiple Types of Geographic Regression Discontinuity Designs

Spatial versions of Regression Discontinuity Designs (RDDs) are becoming increasingly popular as tools for causal inference. However, conducting state-of-the-art analyses often involves tedious and time-consuming steps. This package offers comprehensive functionalities for executing all required spatial and econometric tasks in a streamlined manner. Moreover, it equips researchers with tools for performing essential placebo and balancing checks comprehensively. The fact that researchers do not have to rely on 'APIs' of external 'GIS' software ensures replicability and raises the standard for spatial RDDs.

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5.96 score 41 stars 22 scripts 175 downloads

SpatialInference - Tools for Statistical Inference with Geo-Coded Data

Fast computation of Conley (1999) <doi:10.1016/S0304-4076(98)00084-0> spatial heteroskedasticity and autocorrelation consistent (HAC) standard errors for linear regression models with geo-coded data, with a fast C++ implementation by Christensen, Hartman, and Samii (2021) <doi:10.1017/S0020818321000187>. Performance-critical distance calculations, kernel weighting, and variance component accumulation are implemented in C++ via 'Rcpp' and 'RcppArmadillo'. Includes tools for estimating the spatial correlation range from covariograms and correlograms following the bandwidth selection method proposed in Lehner (2026) <doi:10.48550/arXiv.2603.03997>, and diagnostic visualizations for bandwidth selection.

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openblascpp

4.18 score 187 downloads