jsonlite, httr (>= 1.0.0), plyr, lubridate
'auth.R' 'utils.R' 'request.R' 'url.R' 'convert.R'
'get-data.R' 'mgmt.R' 'accounts.R' 'custom-dims.R'
'custom-mets.R' 'custom-sources.R' 'date-ranges.R'
'experiments.R' 'filters.R' 'profiles.R' 'query.R' 'report.R'
'ga.R' 'firstdate.R' 'goals.R' 'mcf.R' 'metadata.R' 'package.R'
'realtime.R' 'segments.R' 'unsampled.R' 'uploads.R'
'webproperties.R' 'zzz.R'

'utils.R' 'url.R' 'env.R' 'auth.R' 'request.R' 'convert.R'
'get-data.R' 'mgmt.R' 'accounts.R' 'custom-dims.R'
'custom-mets.R' 'custom-sources.R' 'date-ranges.R'
'experiments.R' 'filters.R' 'query.R' 'profiles.R' 'report.R'
'ga.R' 'firstdate.R' 'goals.R' 'mcf.R' 'metadata.R' 'package.R'
'realtime.R' 'segments.R' 'unsampled.R' 'uploads.R'
'webproperties.R' 'zzz.R'

jsonlite, httr (>= 1.1.0), plyr, lubridate
'auth.R' 'utils.R' 'request.R' 'url.R' 'convert.R'
'get-data.R' 'mgmt.R' 'accounts.R' 'customDataSources.R'
'customDimensions.R' 'customMetrics.R' 'date-ranges.R'
'experiments.R' 'filters.R' 'profiles.R' 'query.R' 'report.R'
'ga.R' 'firstdate.R' 'goals.R' 'mcf.R' 'metadata.R' 'package.R'
'realtime.R' 'segments.R' 'unsampledReports.R' 'uploads.R'
'webproperties.R' 'zzz.R'

person(given = "Marcel",
family = "Neunhoeffer",
role = c("aut", "cre"),
email = "marcel.neunhoeffer@gmail.com",
comment = c(ORCID = "0000-0002-9137-5785"))
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.
Marcel Neunhoeffer [aut, cre] (<https://orcid.org/0000-0002-9137-5785>)