Package: binaryGP 0.2

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:Chih-Li Sung

binaryGP_0.2.tar.gz
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binaryGP.pdf |binaryGP.html✨
binaryGP/json (API)

# Install 'binaryGP' in R:
install.packages('binaryGP', repos = c('https://chihli.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.11 score 13 scripts 505 downloads 2 exports 7 dependencies

Last updated 7 years agofrom:db68728417. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 02 2024
R-4.5-win-x86_64OKNov 02 2024
R-4.5-linux-x86_64OKNov 02 2024
R-4.4-win-x86_64OKNov 02 2024
R-4.4-mac-x86_64OKNov 02 2024
R-4.4-mac-aarch64OKNov 02 2024
R-4.3-win-x86_64OKNov 02 2024
R-4.3-mac-x86_64OKNov 02 2024
R-4.3-mac-aarch64OKNov 02 2024

Exports:binaryGP_fitpredict.binaryGP

Dependencies:GPfitlatticelhslogitnormnloptrRcppRcppArmadillo