Description: Implement some deep learning architectures and neural network
algorithms, including BP,RBM,DBN,Deep autoencoder and so on.

Packaged: 2014-03-20 08:06:35 UTC; xiaorong.xr

Title: Deep Learning Prediction of Effectors
Description: A tool that contains trained deep learning models
for predicting effector proteins. 'deepredeff' has been trained to
identify effector proteins using a set of known experimentally
validated effectors from either bacteria, fungi, or oomycetes.
Documentation is available via several vignettes, and the paper by
Imports: Biostrings, dplyr, ggplot2, ggthemes, keras, magrittr, purrr,
Suggests: covr, kableExtra, knitr, rmarkdown, stringr, testthat
Config/reticulate: list( packages = list( list(package = "tensorflow",
Author: Ruth Kristianingsih [aut, cre, cph]

Title: Fitting Deep Distributional Regression
person("David", "Ruegamer", , "david.ruegamer@gmail.com", role = c("aut", "cre")),
person("Florian", "Pfisterer", , "florian.pfisterer@stat.uni-muenchen.de", role = c("ctb")),
person("Philipp", "Baumann", , "baumann@kof.ethz.ch", role = c("ctb")),
person("Chris", "Kolb", , "chris.kolb@stat.uni-muenchen.de", role = c("ctb")))
Allows for the specification of semi-structured deep distributional regression models which are fitted in a neural network as
proposed by Ruegamer et al. (2021) <arXiv:2104.02705>.
Predictors can be modeled using structured (penalized) linear effects, structured non-linear effects or using an unstructured deep network model.
Config/reticulate: list( packages = list( list(package = "six", pip =
TRUE), list(package = "tensorflow", version = "2.5.0rc0", pip =
TRUE), list(package = "tensorflow_probability", version =
"0.12", pip = TRUE), list(package = "keras", version =
Imports: tensorflow (>= 2.2.0), tfprobability, keras, mgcv, dplyr,
purrr, R6, reticulate (>= 1.14), Matrix, magrittr, Metrics,

person("Chris", "Kolb", , "chris.kolb@stat.uni-muenchen.de", role = c("ctb")),
person("Lucas", "Kook", , "lucasheinrich.kook@uzh.ch", role = c("ctb")))
TRUE), list(package = "tensorflow", version = "2.10.0", pip =
"0.16", pip = TRUE), list(package = "keras", version =
Depends: R (>= 4.0.0), tensorflow (>= 2.2.0), tfprobability, keras (>=
Imports: mgcv, dplyr, R6, reticulate (>= 1.14), Matrix, magrittr,