Geometrically Designed Spline Regression
Fitter Function for GeD Spline Regression for Bivariate Data
Base Learner Importance for GeDSboost Objects
Coef Method for GeDSgam, GeDSboost
Coef Method for GeDS Objects
Confidence Intervals for GeDS Models Coefficients
K-Fold Cross-Validation
Crystallographic Scattering Data
Derivative of GeDS Objects
Deviance Method for GeDS, GeDSgam, GeDSboost
Defining the Covariates for the Spline Component in a GeDS Formula
Extract Family from a GeDS, GeDSgam, GeDSboost Object
Formula for the Predictor Model
Geometrically Designed Spline Regression
Generalized Geometrically Designed Spline Regression Estimation
Defined Integral of GeDS Objects
IRLS Estimation
Knots Method for GeDS, GeDSgam, GeDSboost
Lines Method for GeDS Objects
Extract Log-Likelihood from a GeDS Object
Extract Number of Boosting Iterations from a GeDSboost Object
Geometrically Designed Spline Regression Estimation
Component-Wise Gradient Boosting with NGeDS Base-Learners
NGeDSgam: Local Scoring Algorithm with GeD Splines in Backfitting
Plot Method for GeDS Objects
Plot Method for GeDSboost Objects
Plot Method for GeDSgam Objects
Piecewise Polynomial Spline Representation
Predict Method for GeDS Objects
Predict Method for GeDSgam, GeDSboost
Print Method for GeDS, GeDSgam, GeDSboost
Estimation for Models with Spline and Parametric Components
Summary Method for GeDS, GeDSgam, GeDSboost
Functions Used to Fit GeDS Objects with a Univariate Spline Regression
Visualize Boosting Iterations
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 model 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. (2025).
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