# Bayesian Deep Gaussian Processes using MCMC

## Documentation for package ‘deepgp’ version 1.2.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## Help Pages

deepgp-package | Package deepgp
ALC | Active Learning Cohn for Sequential Design
ALC.dgp2 | Active Learning Cohn for Sequential Design
ALC.dgp3 | Active Learning Cohn for Sequential Design
ALC.gp | Active Learning Cohn for Sequential Design
continue | Continues MCMC sampling
continue.dgp2 | Continues MCMC sampling
continue.dgp2vec | Continues MCMC sampling
continue.dgp3 | Continues MCMC sampling
continue.dgp3vec | Continues MCMC sampling
continue.gp | Continues MCMC sampling
continue.gpvec | Continues MCMC sampling
crps | Calculates CRPS
deepgp | Package deepgp
fit_one_layer | MCMC sampling for one layer GP
fit_three_layer | MCMC sampling for three layer deep GP
fit_two_layer | MCMC sampling for two layer deep GP
IMSE | Integrated Mean-Squared (prediction) Error for Sequential Design
IMSE.dgp2 | Integrated Mean-Squared (prediction) Error for Sequential Design
IMSE.dgp3 | Integrated Mean-Squared (prediction) Error for Sequential Design
IMSE.gp | Integrated Mean-Squared (prediction) Error for Sequential Design
plot | Plots object from 'deepgp' package
plot.dgp2 | Plots object from 'deepgp' package
plot.dgp2vec | Plots object from 'deepgp' package
plot.dgp3 | Plots object from 'deepgp' package
plot.dgp3vec | Plots object from 'deepgp' package
plot.gp | Plots object from 'deepgp' package
plot.gpvec | Plots object from 'deepgp' package
post_sample | Generates joint posterior samples from a trained GP/DGP
post_sample.dgp2 | Generates joint posterior samples from a trained GP/DGP
post_sample.dgp2vec | Generates joint posterior samples from a trained GP/DGP
post_sample.dgp3 | Generates joint posterior samples from a trained GP/DGP
post_sample.dgp3vec | Generates joint posterior samples from a trained GP/DGP
post_sample.gp | Generates joint posterior samples from a trained GP/DGP
post_sample.gpvec | Generates joint posterior samples from a trained GP/DGP
predict | Predict posterior mean and variance/covariance
predict.dgp2 | Predict posterior mean and variance/covariance
predict.dgp2vec | Predict posterior mean and variance/covariance
predict.dgp3 | Predict posterior mean and variance/covariance
predict.dgp3vec | Predict posterior mean and variance/covariance
predict.gp | Predict posterior mean and variance/covariance
predict.gpvec | Predict posterior mean and variance/covariance
rmse | Calculates RMSE
score | Calculates score
sq_dist | Calculates squared pairwise distances
to_vec | Converts non-Vecchia object to its Vecchia version
trim | Trim/Thin MCMC iterations
trim.dgp2 | Trim/Thin MCMC iterations
trim.dgp2vec | Trim/Thin MCMC iterations
trim.dgp3 | Trim/Thin MCMC iterations
trim.dgp3vec | Trim/Thin MCMC iterations
trim.gp | Trim/Thin MCMC iterations
trim.gpvec | Trim/Thin MCMC iterations