Package: binaryGP 0.2
Chih-Li Sung
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>.
Authors:
binaryGP_0.2.tar.gz
binaryGP_0.2.tar.gz(r-4.5-noble)binaryGP_0.2.tar.gz(r-4.4-noble)
binaryGP_0.2.tgz(r-4.4-emscripten)binaryGP_0.2.tgz(r-4.3-emscripten)
binaryGP.pdf |binaryGP.html✨
binaryGP/json (API)
| # Install 'binaryGP' in R: |
| install.packages('binaryGP', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 7 years agofrom:db68728417. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 07 2024 |
| R-4.5-linux-x86_64 | OK | Nov 07 2024 |
Exports:binaryGP_fitpredict.binaryGP
Dependencies:GPfitlatticelhslogitnormnloptrRcppRcppArmadillo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Binary Gaussian Process (with/without time-series) | binaryGP_fit |
| Predictions of Binary Gaussian Process | predict.binaryGP |
| Print Fitted results of Binary Gaussian Process | print.binaryGP |
| Summary of Fitting a Binary Gaussian Process | summary.binaryGP |
