binaryGP
Fit and Predict a Gaussian Process Model with (Time-Series) Binary Response
Allows the estimation and prediction for binary Gaussian process model. The mean function can be assumed to have time-series structure. The estimation methods for the unknown parameters are based on penalized quasi-likelihood/penalized quasi-partial likelihood and restricted maximum likelihood. The predicted probability and its confidence interval are computed by Metropolis-Hastings algorithm. More details can be seen in Sung et al (2017) <arXiv:1705.02511>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2 |
rolling linux/jammy R-4.5 | binaryGP_0.2.tar.gz |
127.3 KiB |
0.2 |
rolling linux/noble R-4.5 | binaryGP_0.2.tar.gz |
131.2 KiB |
0.2 |
rolling source/ R- | binaryGP_0.2.tar.gz |
13.1 KiB |
0.2 |
latest linux/jammy R-4.5 | binaryGP_0.2.tar.gz |
127.3 KiB |
0.2 |
latest linux/noble R-4.5 | binaryGP_0.2.tar.gz |
131.2 KiB |
0.2 |
latest source/ R- | binaryGP_0.2.tar.gz |
13.1 KiB |
0.2 |
2026-04-26 source/ R- | binaryGP_0.2.tar.gz |
13.1 KiB |
0.2 |
2026-04-23 source/ R- | binaryGP_0.2.tar.gz |
13.1 KiB |
0.2 |
2026-04-09 windows/windows R-4.5 | binaryGP_0.2.zip |
452.0 KiB |
0.2 |
2025-04-20 source/ R- | binaryGP_0.2.tar.gz |
13.1 KiB |