Author: Alexandra Chouldechova [aut, cre],

Description: Using overlap grouped-lasso penalties, 'gamsel' selects whether a term in a 'gam' is nonzero, linear, or a non-linear spline (up to a specified max df per variable). It fits the entire regularization path on a grid of values for the overall penalty lambda, both for gaussian and binomial families.

Title: Bayesian Generalized Additive Model Selection
Authors@R: c(person("Virginia X.", "He", role = "aut",
Description: Generalized additive model selection via approximate Bayesian inference is provided. Bayesian mixed model-based penalized splines with spike-and-slab-type coefficient prior distributions are used to facilitate fitting and selection. The approximate Bayesian inference engine options are: (1) Markov chain Monte Carlo and (2) mean field variational Bayes. Markov chain Monte Carlo has better Bayesian inferential accuracy, but requires a longer run-time. Mean field variational Bayes is faster, but less accurate. The methodology is described in He and Wand (2021) <arXiv:2201.00412>.

Title: Create a Hierarchical, Categorical Data Model for a Data Source
Description: 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>.
Imports: Rcpp (>= 1.0.3), tensorflow (>= 2.0.0)

Title: Generate Generative Data for a Data Source
Description: 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>.