GeDS

Spline Regression, Generalized Additive Models, and Component-wise Gradient Boosting, utilizing Geometrically Designed (GeD) Splines. GeDS regression is a non-parametric method inspired by geometric principles, for fitting spline regression models with variable knots in one or two independent variables. It efficiently estimates the number of knots and their positions, as well as the spline order, assuming the response variable follows a distribution from the exponential family. GeDS models integrate the broader category of Generalized (Non-)Linear Models, offering a flexible approach to modeling complex relationships. A description of the method can be found in Kaishev et al. (2016) doi:10.1007/s00180-015-0621-7 and Dimitrova et al. (2023) doi:10.1016/j.amc.2022.127493. Further extending its capabilities, GeDS's implementation includes Generalized Additive Models (GAM) and Functional Gradient Boosting (FGB), enabling versatile multivariate predictor modeling, as discussed in the forthcoming work of Dimitrova et al. (2024).