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.

The output gap indicates the percentage difference between the actual output of an economy and its potential. Since potential output is a latent process, the estimation of the output gap poses a challenge and numerous filtering techniques have been proposed. 'RGAP' facilitates the estimation of a Cobb-Douglas production function type output gap, as suggested by the European Commission (Havik et al. 2014) <https://ideas.repec.org/p/euf/ecopap/0535.html>. To that end, the non-accelerating wage rate of unemployment (NAWRU) and the trend of total factor productivity (TFP) can be estimated in two bivariate unobserved component models by means of Kalman filtering and smoothing. 'RGAP' features a flexible modeling framework for the appropriate state-space models and offers frequentist as well as Bayesian estimation techniques. Additional functionalities include direct access to the 'AMECO' <https://economy-finance.ec.europa.eu/economic-research-and-databases/economic-databases/ameco-database_en> database and automated model selection procedures. See the paper by Streicher (2022) <http://hdl.handle.net/20.500.11850/552089> for details.