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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) <doi:10.48550/arXiv.1705.02511>.
| Version: | 0.2 |
| Depends: | R (≥ 2.14.1) |
| Imports: | Rcpp (≥ 0.12.0), lhs (≥ 0.10), logitnorm (≥ 0.8.29), nloptr (≥ 1.0.4), GPfit (≥ 1.0-0), stats, graphics, utils, methods |
| LinkingTo: | Rcpp, RcppArmadillo |
| Published: | 2017-09-19 |
| DOI: | 10.32614/CRAN.package.binaryGP |
| Author: | Chih-Li Sung |
| Maintainer: | Chih-Li Sung <iamdfchile at gmail.com> |
| License: | GPL-2 | GPL-3 |
| NeedsCompilation: | yes |
| CRAN checks: | binaryGP results |
| Reference manual: | binaryGP.pdf |
| Package source: | binaryGP_0.2.tar.gz |
| Windows binaries: | r-devel: binaryGP_0.2.zip, r-release: binaryGP_0.2.zip, r-oldrel: binaryGP_0.2.zip |
| macOS binaries: | r-release (arm64): binaryGP_0.2.tgz, r-oldrel (arm64): binaryGP_0.2.tgz, r-release (x86_64): binaryGP_0.2.tgz, r-oldrel (x86_64): binaryGP_0.2.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.