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Bayesian Gaussian Process Beta Regression Models for the Social Sciences: A Case Study on Spatial Proportion Data from Archaeology

Bayesian Methods for the Social Sciences II, 17th of October 2024, Amsterdam

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Methods

All simulations were coded using Stan (Stan Development Team, 2021b), using the RStan package version 2.26.22 (Stan Development Team, 2021a) as an interface to communicate between Stan and R, with all post-sampling analyses and graphs conducted in R v. 4.3.1 (R Core Team, 2023), and using RStudio v. 2023.02.2 (RStudio Team, 2023).

Bibliography

Datos Abiertos Bogotá. (2020). Localidad. Bogotá. D.C. Retrieved July 19, 2021, from https://datosabiertos.bogota.gov.co/dataset/localidad-bogota-d-c

Departamento Administrativo Nacional de Estadística. (2020). Nivel geográfico municipio. Retrieved July 19, 2021, from https://geoportal.dane.gov.co/servicios/descarga-y- metadatos/descarga-mgn-marco-geoestadistico-nacional/

Finley, A. O., Datta, A., Cook, B. D., Morton, D. C., Andersen, H. E., & Banerjee, S. (2019). Efficient Algorithms for Bayesian Nearest Neighbor Gaussian Processes. Journal of Computational and Graphical Statistics, 28(2), 401–414. https://doi.org/10.1080/10618600.2018.1537924

Maier, M. J. (2014). DirichletReg: Dirichlet Regression for Compositional Data in R (tech. rep. No. 125). Institute for Statistics and Mathematics, Wirtschafts Universität Wien. Vienna.

R Core Team. (2023). R: A language and environment for statistical computing (manual). R Foundation for Statistical Computing. Vienna, Austria. https://www.R-project.org/

RStudio Team. (2023). RStudio: Integrated Development Environment for R (v. 2023.06.1+524). RStudio, PBC. http://www.rstudio.com/

Stan Development Team. (2021a). RStan: The R interface to Stan (v. 2.26.22). https://mc-stan.org/

Stan Development Team. (2021b). Stan Modeling Language User’s Guide. https://mc-stan.org

Tobler, W. R. (1970).A computer movie simulating urban growth in the Detroit region. EconomicGeography, 46, 234–240.

Vieri, J. (2023). Bayesian regional models of gold and copper alloys from pre-Hispanic Colombia [PhD]. University of Cambridge. https://doi.org/10.17863/CAM.105360

Vieri, J., Crema, E. R., Uribe Villegas, M. A., Sáenz Samper, J., & Martinón-Torres, M. (2024). Beyond baselines of performance: Beta regression models of compositional variability in craft production studies. Manuscript submitted for publication

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