Create a Hierarchical, Categorical Data Model for a Data Source
Neural networks are applied to create a density value function which approximates density values for a data source. The trained neural network is analysed for different levels. For each level subspaces with density values above a level are determined. The obtained set of subspaces categorizes the data source hierarchically. A prerequisite is the definition of a data source, the generation of generative data and the calculation of density values. These tasks are executed using package 'ganGenerativeData' <https://cran.r-project.org/package=ganGenerativeData>.

Generate Generative Data for a Data Source
Generative Adversarial Networks are applied to generate generative data for a data source. In iterative training steps the distribution of generated data converges to that of the data source. Reference: Goodfellow et al. (2014) <arXiv:1406.2661v1>.

Generative Adversarial Networks are applied to generate generative data for a data source. In iterative training steps the distribution of generated data converges to that of the data source. An application of generative data is the created density value function which is used to classify data records. Reference: Goodfellow et al. (2014) <arXiv:1406.2661v1>.

Generative Adversarial Networks are applied to generate generative data for a data source. In iterative training steps the distribution of generated data converges to that of the data source. Direct applications of generative data are the created functions for outlier detection and missing data completion. Reference: Goodfellow et al. (2014) <arXiv:1406.2661v1>.

person(
given = "Johann",
family = "de Boer",
role = c("aut", "cre"),
email = "johanndeboer@gmail.com")
Functions for querying the 'Google Analytics' core reporting,
real-time, multi-channel funnel and management APIs, as well as the 'Google
Tag Manager' (GTM) API. Write methods are also provided for the management and
GTM APIs so that you can change tag, property or view
settings, for example. Define reporting queries using natural R expressions
instead of being concerned as much about API technical intricacies like
query syntax, character code escaping, and API limitations.
assertthat (>= 0.2.0), httpuv, httr (>= 1.0.0),
googleAnalyticsR (>= 0.5.0), jsonlite, lazyeval, lubridate,
methods, plyr, R6, rvest, stringr (>= 1.0), XML, xml2, scales,
selectr
devtools, ggplot2, knitr, rmarkdown, testthat
'Bool-generics.R' 'utils.R' 'Segment-generics.R'
'GaApiRequest.R' 'meta.R' 'globaldata.R' 'Expr-generics.R'
'operand-classes.R' 'comparator-classes.R' 'var-classes.R'
'expr-classes.R' 'segment-classes.R' 'Bool-methods.R'
'Comparator-generics.R' 'comparator-coerce.R'
'Comparator-methods.R' 'TableFilter-generics.R'
'table-filter-classes.R' 'var-list-classes.R' 'query-classes.R'
'Query-generics.R' 'Creds-methods.R' 'Date-generics.R'
'date-coerce.R' 'ga-api-classes.R' 'management-api-classes.R'
'Date-methods.R' 'operand-coerce.R' 'Operand-generics.R'
'Operand-methods.R' 'Var-list-generics.R' 'Var-generics.R'
'var-list-coerce.R' 'var-coerce.R' 'Var-methods.R'
'expr-coerce.R' 'Expr-methods.R' 'GaGetCoreReport.R'
'view-coerce.R' 'GaView-methods.R' 'ga-api-coerce.R'
'GetGa-methods.R' 'Query-methods.R' 'segment-coerce.R'
'Sequence-generics.R' 'Segment-methods.R' 'Sequence-methods.R'
'table-filter-coerce.R' 'TableFilter-methods.R'
'Var-list-methods.R' 'deprecated.R' 'ganalytics-package.R'
'googleAnalyticsR-coerce.R' 'gtm-api-classes.R' 'sequence.R'
'zzz.R'