Maintainer: Annie S. Booth (annie_booth@ncsu.edu)

Performs Bayesian posterior inference for deep Gaussian processes following Sauer, Gramacy, and Higdon (2023). See Sauer (2023) 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). Optional monotonic warpings are implemented following Barnett et al. (2024). 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), and contour location through entropy (Booth, Renganathan, and Gramacy, 2024). 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.

Run `help("deepgp-package")`

or
`help(package = "deepgp")`

for more information.

Sauer, A. (2023). Deep Gaussian process surrogates for computer
experiments. *Ph.D. Dissertation, Department of Statistics, Virginia
Polytechnic Institute and State University.* http://hdl.handle.net/10919/114845

Sauer, A., Gramacy, R.B., & Higdon, D. (2023). Active learning
for deep Gaussian process surrogates. *Technometrics, 65,* 4-18.
arXiv:2012.08015

Sauer, A., Cooper, A., & Gramacy, R. B. (2023).
Vecchia-approximated deep Gaussian processes for computer experiments.
*Journal of Computational and Graphical Statistics,* 1-14.
arXiv:2204.02904

Gramacy, R. B., Sauer, A. & Wycoff, N. (2022). Triangulation
candidates for Bayesian optimization. *Advances in Neural Information
Processing Systems (NeurIPS), 35,* 35933-35945. arXiv:2112.07457

Booth, A. S., Renganathan, S. A. & Gramacy, R. B. (2024). Contour
location for reliability in airfoil simulation experiments using deep
Gaussian processes. *In Review.* arXiv:2308.04420

Barnett, S., Beesley, L. J., Booth, A. S., Gramacy, R. B., &
Osthus D. (2024). Monotonic warpings for additive and deep Gaussian
processes. *In Review.* arXiv:2408.01540

What’s new in version 1.1.3?

- Option to force monotonic warpings in the two-layer DGP with the
argument
`monowarp = TRUE`

to`fit_two_layer`

. Monotonic warpings trigger separable lengthscales on the outer layer. - Updated default prior values on lengthscales and nugget for noisy
settings (when
`true_g = NULL`

) - Minor bug fix in Gibbs updating of separable lengthscale sampling in
`fit_one_layer`

- Some improvements to default plotting
- Updated package examples and vignette

What’s new in version 1.1.2?

- Option for user to specify ordering for Vecchia approximation
(through
`ordering`

argument in`fit`

functions) `lite = TRUE`

predictions have been sped up- bypassing the
`cov(t(mu_t))`

computation altogether (this is only necessary for`lite = FALSE`

) - removing
`d_new`

calculations - using
`diag_quad_mat`

Cpp function more often

- bypassing the
- Expected improvement is now available for Vecchia-approximated fits
- Internally, predict functions have been consolidated (removing nearly 500 lines of redundant code)
- Removed internal
`clean_prediction`

function as it was no longer needed - Minor bug fixes
- Fixed error in
`fit_one_layer`

with`vecchia = TRUE`

and`sep = TRUE`

caused by the`arma::mat covmat`

initialization in the`vecchia.cpp`

file - Fixed error in
`predict.dgp2`

with`return_all = TRUE`

(replaced`out`

with`object`

- thanks Steven Barnett!) - Fixed storage of
`ll`

in`continue`

functions (thanks Sebastien Coube!)

- Fixed error in

What’s new in version 1.1.1?

- Entropy calculations for contour locating sequential designs are
offered through the specification of an
`entropy_limit`

in any of the`predict`

functions. - In posterior predictions, there is now an option to return
point-wise mean and variance estimates for all MCMC samples through the
specification of
`return_all = TRUE`

. - To save on memory and storage,
`predict`

functions no longer return`s2_smooth`

or`Sigma_smooth`

. If desired, these quantities may be calculated by subtracting`tau2 * g`

from the diagonal. - The
`vecchia = TRUE`

option may now utilize either the Matern (`cov = "matern"`

) or squared exponential kernel (`cov = "exp2"`

“). - Performance improvements for
`cores = 1`

in`predict`

,`ALC`

, and`IMSE`

functions (helps to avoid a SNOW conflict when running multiple instances on the same machine). - Fit functions now return the outer log likelihood value along with MCMC samples. Used in trace plots to assess burn-in.
- In
`fit_two_layer`

, the intermediate latent layer may now have either a prior mean of zero (default) or a prior mean equal to`x`

(`pmx = TRUE`

). If`pmx`

is set to a constant, this will be the scale parameter on the inner Gaussian layer.

What’s new in version 1.1.0?

- Package vignette
- Option to specify
`sep = TRUE`

in`fit_one_layer`

to fit a GP with separable/anisotropic lengthscales. - Default cores in predict are now 1 (this avoids a conflict when running multiple sessions simultaneously on a single machine).

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.