Performs model fitting and sequential design for deep Gaussian
processes following Sauer, Gramacy, and Higdon (2020) <arXiv:2012.08015>.
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.  Sequential design criteria
include integrated mean-squared error (IMSE), active learning Cohn (ALC),
and expected improvement (EI).  Applicable to both noisy and
deterministic functions.  Incorporates SNOW parallelization and
utilizes C and C++ under the hood.
grDevices, graphics, stats, doParallel, foreach, parallel,
Rcpp, mvtnorm

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 and utilizes C and C++ under the hood.
grDevices, graphics, stats, doParallel, foreach, parallel,
GpGp, Matrix, Rcpp, mvtnorm, FNN

An Implementation of Deep Neural Network for Regression and
Classification
An implementation of deep neural network with rectifier linear units trained
with stochastic gradient descent method and batch normalization. A combination of these methods have
achieved state-of-the-art performance in ImageNet classification by overcoming the gradient saturation
problem experienced by many deep architecture neural network models in the past. In addition,
batch normalization and dropout are implemented as a means of regularization. The deeplearning package is
inspired by the darch package and uses its class DArch.
c(
person(given = "Zhi", family = "Ruan", email = "ryan.zhiruan@gmail.com", role = c("aut", "cre")),
person("Martin", "Drees", email = "mdrees@stud.fh-dortmund.de", role = c("cph"))
)