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) .


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0.2 by Chih-Li Sung, a year ago

Browse source code at

Authors: Chih-Li Sung

Documentation:   PDF Manual  

GPL-2 | GPL-3 license

Imports Rcpp, lhs, logitnorm, nloptr, GPfit, stats, graphics, utils, methods

Linking to Rcpp, RcppArmadillo

See at CRAN