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  "Title": "Gaussian Processes for Social Science",
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  "Date": "2026-04-01",
  "Authors@R": "c(person(\"Soonhong\", \"Cho\", , \"tnsehdtm@gmail.com\", role = c(\"aut\")),\nperson(\"Doeun\", \"Kim\", , \"doeun2@ucla.edu\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0003-4789-6599\")),\nperson(\"Chad\", \"Hazlett\", , \"chazlett@ucla.edu\", role = c(\"aut\", \"cre\")))",
  "Description": "Provides Gaussian process (GP) regression tools for social\nscience inference problems.  GPs combine flexible nonparametric\nregression with principled uncertainty quantification: rather\nthan committing to a single model fit, the posterior reflects\nlesser knowledge at the edge of or beyond the observed data,\nwhere other approaches become highly model-dependent.  The\npackage reduces user-chosen hyperparameters from three to zero\nand supplies convenience functions for regression discontinuity\n(gp_rdd()), interrupted time-series (gp_its()), and general GP\nfitting (gpss(), gp_train(), gp_predict()).  Methods are\ndescribed in Cho, Kim, and Hazlett (2026)\n<doi:10.1017/pan.2026.10032>.",
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