gpss
Gaussian Processes for Social Science
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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.3 |
rolling source/ R- | gpss_1.0.3.tar.gz |
101.2 KiB |
1.0.3 |
rolling linux/jammy R-4.5 | gpss_1.0.3.tar.gz |
251.6 KiB |
1.0.3 |
rolling linux/noble R-4.5 | gpss_1.0.3.tar.gz |
253.3 KiB |
1.0.3 |
latest source/ R- | gpss_1.0.3.tar.gz |
101.2 KiB |
1.0.3 |
latest linux/jammy R-4.5 | gpss_1.0.3.tar.gz |
251.6 KiB |
1.0.3 |
latest linux/noble R-4.5 | gpss_1.0.3.tar.gz |
253.3 KiB |
1.0.3 |
2026-04-23 source/ R- | gpss_1.0.3.tar.gz |
101.2 KiB |
1.0.3 |
2026-04-09 windows/windows R-4.5 | gpss_1.0.3.zip |
571.7 KiB |