c(person("Dominik", "Rafacz", email = "dominikrafacz@gmail.com",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-0925-1909")),
person("Hubert", "Baniecki", role = c("aut")),
person("Szymon", "Maksymiuk", role = c("aut")),
person("Mateusz", "Bakala", role = c("aut")))
BiocManager, shiny, shinycssloaders, covr, devtools, knitr,
miniCRAN, plyr, rmarkdown, spelling, stringi, testthat (>=
2.1.0), vcr
Dominik Rafacz [aut, cre] (<https://orcid.org/0000-0003-0925-1909>),
Hubert Baniecki [aut],
Szymon Maksymiuk [aut],
Mateusz Bakala [aut]

c(person("Dominik", "Rafacz", email = "dominikrafacz@gmail.com",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-0925-1909")),
person("Hubert", "Baniecki", role = c("aut")),
person("Szymon", "Maksymiuk", role = c("aut")),
person("Laura", "Bakala", role = c("aut")),
person("Dirk", "Eddelbuettel", role = c("ctb")))
Dominik Rafacz [aut, cre] (<https://orcid.org/0000-0003-0925-1909>),
Hubert Baniecki [aut],
Szymon Maksymiuk [aut],
Laura Bakala [aut],
Dirk Eddelbuettel [ctb]

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

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.

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.

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.