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binaryGP
View on CRAN: Click
here
Download and install binaryGP package within the R console
Install from CRAN:
install.packages("binaryGP")
Install from Github:
library("remotes")
install_github("cran/binaryGP")
Install by package version:
library("remotes")
install_version("binaryGP", "0.2")
Attach the package and use:
library("binaryGP")
Maintained by
Chih-Li Sung
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-09-19
Latest Update: 2017-09-19
Description:
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) .
How to cite:
Chih-Li Sung (2017). binaryGP: Fit and Predict a Gaussian Process Model with (Time-Series) Binary Response. R package version 0.2, https://cran.r-project.org/web/packages/binaryGP
Previous versions and publish date:
No previous versions
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Functions, R codes and Examples using
the binaryGP R package
Some associated functions: binaryGP_fit . predict.binaryGP . print.binaryGP . summary.binaryGP .
Some associated R codes: RcppExports.R . binaryGP_fit.R . corr_vec.R . is.binaryGP.R . likelihood.R . likelihood_w_nugget.R . orthogonalize.R . orthogonalize_vec.R . predict.binaryGP.R . print.binaryGP.R . summary.binaryGP.R . Full binaryGP package functions and examples
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