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
binaryGP_0.2.zip(r-4.7)binaryGP_0.2.zip(r-4.6)binaryGP_0.2.zip(r-4.5)
binaryGP_0.2.tgz(r-4.6-x86_64)binaryGP_0.2.tgz(r-4.6-arm64)binaryGP_0.2.tgz(r-4.5-x86_64)binaryGP_0.2.tgz(r-4.5-arm64)
binaryGP_0.2.tar.gz(r-4.7-arm64)binaryGP_0.2.tar.gz(r-4.7-x86_64)binaryGP_0.2.tar.gz(r-4.6-arm64)binaryGP_0.2.tar.gz(r-4.6-x86_64)
binaryGP_0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
binaryGP/json (API)

# Install 'binaryGP' in R:
install.packages('binaryGP', repos = c('https://chihli.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

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

openblascpp

1.11 score 13 scripts 599 downloads 2 exports 7 dependencies

Last updated from:db68728417. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK133
linux-devel-x86_64OK144
source / vignettesOK225
linux-release-arm64OK130
linux-release-x86_64OK117
macos-release-arm64OK116
macos-release-x86_64OK240
macos-oldrel-arm64OK126
macos-oldrel-x86_64OK283
windows-develOK161
windows-releaseOK102
windows-oldrelOK120
wasm-releaseOK114

Exports:binaryGP_fitpredict.binaryGP

Dependencies:GPfitlatticelhslogitnormnloptrRcppRcppArmadillo