Gaussian Processes for Social Science R package gpss version 1.0.3 Provides Gaussian process (GP) regression tools for social science inference problems. GPs combine flexible nonparametric regression with principled uncertainty quantification: rather than committing to a single model fit, the posterior reflects lesser knowledge at the edge of or beyond the observed data, where other approaches become highly model-dependent. The package reduces user-chosen hyperparameters from three to zero and supplies convenience functions for regression discontinuity (gp_rdd()), interrupted time-series (gp_its()), and general GP fitting (gpss(), gp_train(), gp_predict()). Methods are described in Cho, Kim, and Hazlett (2026) <doi:10.1017/pan.2026.10032>. Software Depends: R (>= 3.5.0) Imports: Rcpp (>= 1.0.14), Matrix, ggplot2, rlang LinkingTo: Rcpp, RcppArmadillo Suggests: MASS, posterior, testthat (>= 3.0.0) Chad Hazlett <chazlett@ucla.edu> Comprehensive R Archive Network (CRAN) Soonhong Cho [aut], Doeun Kim [aut] (ORCID: <https://orcid.org/0000-0003-4789-6599>), Chad Hazlett [aut, cre] GPL (>= 3) 2026-04-08 application/tgz https://CRAN.R-project.org/package=gpss doi:10.32614/CRAN.package.gpss