Imports: jsonlite, httr (>= 1.0.0), plyr, lubridate
Collate: 'auth.R' 'utils.R' 'request.R' 'url.R' 'convert.R'
'experiments.R' 'filters.R' 'profiles.R' 'query.R' 'report.R'
'ga.R' 'firstdate.R' 'goals.R' 'mcf.R' 'metadata.R' 'package.R'

Collate: 'utils.R' 'url.R' 'env.R' 'auth.R' 'request.R' 'convert.R'
'experiments.R' 'filters.R' 'query.R' 'profiles.R' 'report.R'

Imports: jsonlite, httr (>= 1.1.0), plyr, lubridate
Suggests: knitr, rmarkdown, testthat, shiny

Title: Generative Adversarial Nets (GAN) in R
Description: An easy way to get started with Generative Adversarial Nets (GAN) in R. The GAN algorithm was initially
described by Goodfellow et al. 2014 <https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf>. A GAN can be used to learn the joint distribution of complex data by
comparison. A GAN consists of two neural networks a Generator and a Discriminator, where the two
neural networks play an adversarial minimax game.
Built-in GAN models make the training of GANs in R possible in one line and make it easy to
experiment with different design choices (e.g. different network architectures, value functions, optimizers).
The built-in GAN models work with tabular data (e.g. to produce synthetic data) and image data.
Methods to post-process the output of GAN models to enhance the quality of samples are available.
Packaged: 2022-03-28 21:25:13 UTC; marcelneunhoeffer
Author: Marcel Neunhoeffer [aut, cre] (<https://orcid.org/0000-0002-9137-5785>)

Title: LS-TreeBoost and LAD-TreeBoost for Gene Regulatory Network
Authors@R: c(person(given = "Raghvendra",family = "Mall",role = c("aut","cre"), email = "raghvendra5688@gmail.com"),
Description: Provides an implementation of Regularized LS-TreeBoost & LAD-TreeBoost algorithm for Regulatory Network inference from any type of expression data (Microarray/RNA-seq etc).

Author: Raghvendra Mall [aut, cre], Khalid Kunji [aut], Melissa O'Neill [ctb]
Authors@R: c(person(given = "Raghvendra",family = "Mall",role = c("aut","cre"), email = "rmall@hbku.edu.qa"),