{ "_id": "6ae1bc3895ca2370cc8cb834", "Package": "deepgp", "Type": "Package", "Title": "Bayesian Deep Gaussian Processes using MCMC", "Version": "1.2.3", "Date": "2026-07-29", "Authors@R": "person(given = c(\"Annie\", \"S.\"),\nfamily = \"Booth\",\nrole = c(\"aut\", \"cre\"),\nemail = \"annie_booth@vt.edu\")", "Description": "Performs Bayesian posterior inference for deep Gaussian\nprocesses following Sauer, Gramacy, and Higdon (2023,\n). See Sauer (2023,\n) for comprehensive\nmethodological details and\n for a variety of\ncoding examples. Models are trained through MCMC including\nelliptical slice sampling of latent Gaussian layers and\nMetropolis-Hastings sampling of kernel hyperparameters.\nGradient-enhancement and gradient predictions are offered\nfollowing Booth (2026, ).\nVecchia approximation for faster computation is implemented\nfollowing Sauer, Cooper, and Gramacy (2023,\n). Optional monotonic warpings\nare implemented following Barnett et al. (2025,\n). Downstream tasks include\nsequential design through active learning Cohn/integrated mean\nsquared error (ALC/IMSE; Sauer, Gramacy, and Higdon, 2023),\noptimization through expected improvement (EI; Gramacy, Sauer,\nand Wycoff, 2022, ), and contour\nlocation through entropy (Booth, Renganathan, and Gramacy,\n2025, ). Models extend up to\nthree layers deep; a one layer model is equivalent to typical\nGaussian process regression. Incorporates OpenMP and SNOW\nparallelization and utilizes C/C++ under the hood.", "License": "LGPL", "Encoding": "UTF-8", "NeedsCompilation": "yes", "VignetteBuilder": "knitr", "Config/roxygen2/version": "8.0.0", "RoxygenNote": "7.3.3", "Packaged": { "Date": "2026-10-28 09:05:22 UTC", "User": "root" }, "Author": "Annie S. Booth [aut, cre]", "Maintainer": "Annie S. 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