Package: deepgp 1.1.3

Annie S. Booth

deepgp: Bayesian Deep Gaussian Processes using MCMC

Performs Bayesian posterior inference for deep Gaussian processes following Sauer, Gramacy, and Higdon (2023, <doi:10.48550/arXiv.2012.08015>). See Sauer (2023, <http://hdl.handle.net/10919/114845>) for comprehensive methodological details and <https://bitbucket.org/gramacylab/deepgp-ex/> for a variety of coding examples. Models are trained through MCMC including elliptical slice sampling of latent Gaussian layers and Metropolis-Hastings sampling of kernel hyperparameters. Vecchia-approximation for faster computation is implemented following Sauer, Cooper, and Gramacy (2023, <doi:10.48550/arXiv.2204.02904>). Optional monotonic warpings are implemented following Barnett et al. (2024, <doi:10.48550/arXiv.2408.01540>). Downstream tasks include sequential design through active learning Cohn/integrated mean squared error (ALC/IMSE; Sauer, Gramacy, and Higdon, 2023), optimization through expected improvement (EI; Gramacy, Sauer, and Wycoff, 2022 <doi:10.48550/arXiv.2112.07457>), and contour location through entropy (Booth, Renganathan, and Gramacy, 2024 <doi:10.48550/arXiv.2308.04420>). Models extend up to three layers deep; a one layer model is equivalent to typical Gaussian process regression. Incorporates OpenMP and SNOW parallelization and utilizes C/C++ under the hood.

Authors:Annie S. Booth [aut, cre]

deepgp_1.1.3.tar.gz
deepgp_1.1.3.tar.gz(r-4.5-noble)deepgp_1.1.3.tar.gz(r-4.4-noble)
deepgp_1.1.3.tgz(r-4.4-emscripten)deepgp_1.1.3.tgz(r-4.3-emscripten)
deepgp.pdf |deepgp.html
deepgp/json (API)

# Install 'deepgp' in R:
install.packages('deepgp', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

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

openblascppopenmp

3.08 score 1 stars 12 scripts 331 downloads 11 exports 12 dependencies

Last updated 4 months agofrom:6af68eef4a. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 01 2024
R-4.5-linux-x86_64OKDec 01 2024

Exports:ALCcontinuecrpsfit_one_layerfit_three_layerfit_two_layerIMSErmsescoresq_disttrim

Dependencies:BHcodetoolsdoParallelFNNforeachGpGpiteratorslatticeMatrixmvtnormRcppRcppArmadillo

deepgp: an R-package for Bayesian Deep Gaussian Processes

Rendered fromdeepgp.Rmdusingknitr::rmarkdownon Dec 01 2024.

Last update: 2024-08-20
Started: 2022-12-15

Readme and manuals

Help Manual

Help pageTopics
Package deepgpdeepgp-package
Active Learning Cohn for Sequential DesignALC ALC.dgp2 ALC.dgp3 ALC.gp
Continues MCMC samplingcontinue continue.dgp2 continue.dgp2vec continue.dgp3 continue.dgp3vec continue.gp continue.gpvec
Calculates CRPScrps
MCMC sampling for one layer GPfit_one_layer
MCMC sampling for three layer deep GPfit_three_layer
MCMC sampling for two layer deep GPfit_two_layer
Integrated Mean-Squared (prediction) Error for Sequential DesignIMSE IMSE.dgp2 IMSE.dgp3 IMSE.gp
Plots object from 'deepgp' packageplot plot.dgp2 plot.dgp2vec plot.dgp3 plot.dgp3vec plot.gp plot.gpvec
Predict posterior mean and variance/covariancepredict predict.dgp2 predict.dgp2vec predict.dgp3 predict.dgp3vec predict.gp predict.gpvec
Calculates RMSErmse
Calculates scorescore
Calculates squared pairwise distancessq_dist
Trim/Thin MCMC iterationstrim trim.dgp2 trim.dgp2vec trim.dgp3 trim.dgp3vec trim.gp trim.gpvec