R: Particle Learning of Gaussian Processes

# Particle Learning of Gaussian Processes

## Documentation for package ‘plgp’ version 1.1-13

- DESCRIPTION file
- Code demos. Use demo() to run them.

## Help Pages

plgp-package | Particle Learning of Gaussian Processes
addpall.CGP | Add data to pall
addpall.ConstGP | Add data to pall
addpall.GP | Add data to pall
data.CGP | Supply GP data to PL
data.CGP.adapt | Supply GP data to PL
data.ConstGP | Supply GP data to PL
data.ConstGP.improv | Supply GP data to PL
data.GP | Supply GP data to PL
data.GP.improv | Supply GP data to PL
draw.CGP | Metropolis-Hastings draw for GP parameters
draw.ConstGP | Metropolis-Hastings draw for GP parameters
draw.GP | Metropolis-Hastings draw for GP parameters
exp2d.C | 2-d Exponential Hessian Data
init.CGP | Initialize particles for GPs
init.ConstGP | Initialize particles for GPs
init.GP | Initialize particles for GPs
lpredprob.CGP | Log-Predictive Probability Calculation for GPs
lpredprob.ConstGP | Log-Predictive Probability Calculation for GPs
lpredprob.GP | Log-Predictive Probability Calculation for GPs
papply | Extending apply to particles
params.CGP | Extract parameters from GP particles
params.ConstGP | Extract parameters from GP particles
params.GP | Extract parameters from GP particles
PL | Particle Learning Skeleton Method
PL.env | Particle Learning Skeleton Method
plgp | Particle Learning Skeleton Method
pred.CGP | Prediction for GPs
pred.ConstGP | Prediction for GPs
pred.GP | Prediction for GPs
prior.CGP | Generate priors for GP models
prior.ConstGP | Generate priors for GP models
prior.GP | Generate priors for GP models
propagate.CGP | PL propagate rule for GPs
propagate.ConstGP | PL propagate rule for GPs
propagate.GP | PL propagate rule for GPs
rectscale | Un/Scale data in a bounding rectangle
rectunscale | Un/Scale data in a bounding rectangle