Maintainer: Annie Sauer anniees@vt.edu

Performs posterior inference for deep Gaussian processes following Sauer, Gramacy, and Higdon (2020) <arXiv:2012.08015>. 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 (2022) <arXiv:2204.02904>. Downstream tasks include sequential design through active learning Cohn/integrated mean squared error (ALC/IMSE; Sauer, Gramacy, and Higdon, 2020) and optimization through expected improvement (EI; Gramacy, Sauer, and Wycoff, 2021 <arXiv:2112.07457>). Models extend up to three layers deep; a one layer model is equivalent to typical Gaussian process regression. Covariance kernel options are matern (default) and squared exponential. Applicable to both noisy and deterministic functions. Incorporates SNOW parallelization; utilizes C and C++ with OpenMP parallelization under the hood.

Run `help("deepgp-package")`

or
`help(package = "deepgp")`

for more information.

Sauer, A., Gramacy, R.B., & Higdon, D. (2020). Active learning
for deep Gaussian process surrogates. *Technometrics*,
(just-accepted), 1-39.

Sauer, A., Cooper, A., & Gramacy, R. B. (2022).
Vecchia-approximated deep Gaussian processes for computer experiments.
*pre-print on arXiv:2204.02904*

What’s new in version 1.0.1?

- Minor bug fixes/improvements.
- New warning message when OpenMP parallelization is not utilized for the Vecchia approximation. This happens when the package is downloaded from CRAN on a Mac. To set up OpenMP, download package source and compile with gcc/g++ instead of clang.

What’s new in version 1.0.0?

- Models may now leverage the Vecchia approximation (through the
specification of
`vecchia = TRUE`

in fit functions) for faster computation. The speed of this implementation relies on OpenMP parallelization (make sure the`-fopenmp`

flag is present with package installation). - SNOW parallelization now uses less memory/storage.
`tau2`

is now calculated at the time of MCMC, not at the time of prediction. This avoids some extra calculations.

What’s new in version 0.3.0?

- The Matern kernel is now the default covariance. The smoothness
parameter is user-adjustable but must be either
`v = 0.5`

,`v = 1.5`

, or`v = 2.5`

(default). The squared exponential kernel is still required for use with ALC and IMSE (set`cov = "exp2"`

in fit functions). - Expected improvement (EI) may now be computed concurrently with
predictions. Set
`EI = TRUE`

inside`predict`

calls. EI calculations are nugget-free and are for*minimizing*the response (negate`y`

if maximization is desired). - To save memory, hidden layer mappings used in predictions are no
longer stored and returned by default. To store them, set
`store_latent = TRUE`

inside predict.