Description: Provides tools for exploration of R package dependencies.
The main deepdep() function allows to acquire deep dependencies of any package and plot them in an elegant way.
The deepdep_shiny() function runs shiny application that helps to produce a nice 'deepdep' plot.

Author: Dominik Rafacz [aut, cre] (<https://orcid.org/0000-0003-0925-1909>),

person("Laura", "Bakala", role = c("aut")),
person("Dirk", "Eddelbuettel", role = c("ctb")))

Suggests: BiocManager, covr, devtools, ggplot2, ggraph, graphlayouts,
igraph, knitr, miniCRAN, plyr, rmarkdown, scales, shiny,
shinycssloaders, spelling, stringi, testthat (>= 2.1.0), vcr

Title: Deep Learning for General Purpose
Description: Aims to provide simple intuitive functions to create quick prototypes of artificial neural network or deep learning models. In addition novel ensemble models like 'deeptree' and 'deepforest' has been included which combines decision trees and neural network.

Author: Cinzia Viroli, Geoffrey J. McLachlan
Description: Deep Gaussian mixture models as proposed by Viroli and McLachlan (2019)
<doi:10.1007/s11222-017-9793-z> provide a generalization of classical Gaussian mixtures
to multiple layers. Each layer contains a set of latent variables that follow
a mixture of Gaussian distributions. To avoid overparameterized solutions, dimension
reduction is applied at each layer by way of factor models.

to multiple layers. Each layer contains a set of latent variables that follow a mixture of
Gaussian distributions. To avoid overparameterized solutions, dimension reduction is
applied at each layer by way of factor models.

Title: Sequential Design for Deep Gaussian Processes using MCMC
Description: Performs model fitting and sequential design for deep Gaussian
processes using MCMC and elliptical slice sampling.  Models extend up to
three layers deep; a one layer model is equivalent to typical Gaussian
process regression.  Sequential design criteria include integrated mean
square prediction error (IMSPE), active learning Cohn (ALC), and expected
improvement (EI).  Covariance structure is based on inverse exponentiated
squared euclidean distance.  Applicable to noisy and deterministic functions.
Incorporates SNOW parallelization and utilizes C under the hood.  Manuscript
forthcoming; see Damianou and Lawrence (2013) <arXiv:1211.0358> for deep
Gaussian process models and Murray, Adams, and MacKay (2010) <arXiv:1001.0175>
Imports: grDevices, graphics, stats, doParallel, foreach, parallel
Packaged: 2020-10-20 18:38:46 UTC; anniesauer