scpcR provides spatial correlation-robust inference for regression coefficients following Müller & Watson 2022 and Müller & Watson 2023, implemented in R based on their original Stata implementation.
Citation: If you use this package, please cite Becker, Boll and Voth 2026, Müller & Watson 2022, and Müller & Watson 2023. See CITATION.bib for the BibTeX entries.
If you encounter any issues or have any questions, please open an issue on GitHub or contact the authors.
Install the latest release version from GitHub with:
remotes::install_github("spatial-spur/scpcR@v0.1.3")set.seed(42)
n <- 60
dat <- data.frame(
y = rnorm(n),
x = rnorm(n),
lon = runif(n, -100, -80),
lat = runif(n, 30, 45)
)
fit <- lm(y ~ x, data = dat)
out <- scpcR::scpc(
fit,
data = dat,
lon = "lon",
lat = "lat"
)
summary(out)Please refer to the package documentation for detailed information and other (R, Python, Stata) packages.
Becker, Sascha O., P. David Boll and Hans-Joachim Voth "Testing and Correcting for Spatial Unit Roots in Regression Analysis", The Stata Journal 26(2): 177–202. https://doi.org/10.1177/1536867X261449932.
Müller, Ulrich K. and Mark W. Watson "Spatial Correlation Robust Inference", Econometrica 90(6) (2022), 2901–2935. https://www.princeton.edu/~umueller/SHAR.pdf.
Müller, Ulrich K. and Mark W. Watson "Spatial Correlation Robust Inference in Linear Regression and Panel Models", Journal of Business & Economic Statistics 41(4) (2023), 1050–1064. https://www.princeton.edu/~umueller/SpatialRegression.pdf.