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 (2024) <doi:10.1007/s10182-023-00490-y>.

Read, analyze, modify, and write 'GAMS' (General Algebraic
    Modeling System) data. The main focus of 'gamstransfer' is the
    highly efficient transfer of data with 'GAMS' <https://www.gams.com/>,
    while keeping these operations as simple as possible for the user. The
    transfer of data usually takes place via an intermediate GDX (GAMS Data
    Exchange) file. Additionally, 'gamstransfer' provides utility
    functions to get an overview of 'GAMS' data and to check its validity.

After fitting a Generalized Additive (Mixed) Model, the next step is often to obtain predicted values for certain combinations of predictors for visualization of estimated effects in the model. It involves constructing a new data frame, add predicted values, and finally makes a (contour) plot. This package is intended to facilitate these steps to visualize estimated effects in a generalized additive model. The underlying modeling methodology is described in Wood (2017, ISBN:9781498728331).

Neural networks are applied to create a density value function which approximates density values for a data source. The trained neural network is analyzed for different levels. For each level metric subspaces with density values above a level are determined. The obtained set of metric subspaces and the trained neural network are assembled into a data model. 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>.