Package: deepgp 1.1.3
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:
deepgp_1.1.3.tar.gz
deepgp_1.1.3.zip(r-4.5)deepgp_1.1.3.zip(r-4.4)deepgp_1.1.3.zip(r-4.3)
deepgp_1.1.3.tgz(r-4.4-x86_64)deepgp_1.1.3.tgz(r-4.4-arm64)deepgp_1.1.3.tgz(r-4.3-x86_64)deepgp_1.1.3.tgz(r-4.3-arm64)
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://anniesbooth.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 29 days agofrom:6af68eef4a. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Aug 20 2024 |
| R-4.5-win-x86_64 | OK | Aug 20 2024 |
| R-4.5-linux-x86_64 | OK | Aug 20 2024 |
| R-4.4-win-x86_64 | OK | Aug 20 2024 |
| R-4.4-mac-x86_64 | OK | Aug 20 2024 |
| R-4.4-mac-aarch64 | OK | Aug 20 2024 |
| R-4.3-win-x86_64 | OK | Aug 20 2024 |
| R-4.3-mac-x86_64 | OK | Aug 20 2024 |
| R-4.3-mac-aarch64 | OK | Aug 20 2024 |
Exports:ALCcontinuecrpsfit_one_layerfit_three_layerfit_two_layerIMSErmsescoresq_disttrim
Dependencies:BHcodetoolsdoParallelFNNforeachGpGpiteratorslatticeMatrixmvtnormRcppRcppArmadillo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Package deepgp | deepgp-package |
| Active Learning Cohn for Sequential Design | ALC ALC.dgp2 ALC.dgp3 ALC.gp |
| Continues MCMC sampling | continue continue.dgp2 continue.dgp2vec continue.dgp3 continue.dgp3vec continue.gp continue.gpvec |
| Calculates CRPS | crps |
| MCMC sampling for one layer GP | fit_one_layer |
| MCMC sampling for three layer deep GP | fit_three_layer |
| MCMC sampling for two layer deep GP | fit_two_layer |
| Integrated Mean-Squared (prediction) Error for Sequential Design | IMSE IMSE.dgp2 IMSE.dgp3 IMSE.gp |
| Plots object from 'deepgp' package | plot plot.dgp2 plot.dgp2vec plot.dgp3 plot.dgp3vec plot.gp plot.gpvec |
| Predict posterior mean and variance/covariance | predict predict.dgp2 predict.dgp2vec predict.dgp3 predict.dgp3vec predict.gp predict.gpvec |
| Calculates RMSE | rmse |
| Calculates score | score |
| Calculates squared pairwise distances | sq_dist |
| Trim/Thin MCMC iterations | trim trim.dgp2 trim.dgp2vec trim.dgp3 trim.dgp3vec trim.gp trim.gpvec |
