R package scam. Routines for generalized additive modelling under shape constraints on the component functions of the linear predictor. Models can contain multiple shape constrained (univariate and/or bivariate) and unconstrained terms. The routines of gam() in package ’mgcv’ are used for setting up the model matrix, printing and plotting the results. Penalized likelihood maximization based on Newton-Raphson method is used to fit a model with multiple smoothing parameter selection by GCV or UBRE/AIC.
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References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Mirrelijn M. van Nee, Lodewyk F.A. Wessels, Mark A. van de Wiel: ecpc: An R-package for generic co-data models for high-dimensional prediction (2022) arXiv
- Sy Han Chiou, Gongjun Xu, Jun Yan, Chiung-Yu Huang: Regression Modeling for Recurrent Events Using R Package reReg (2021) arXiv
- Spiegel, Elmar; Kneib, Thomas; Otto-Sobotka, Fabian: Generalized additive models with flexible response functions (2019)
- Hannah Frick; Ioannis Kosmidis: trackeR: Infrastructure for Running and Cycling Data from GPS-Enabled Tracking Devices in R (2017) not zbMATH
- Subbey, Sam: Regularization of a parameter estimation problem using monotonicity and convexity constraints (2017)
- Chen, Yining; Samworth, Richard J.: Generalized additive and index models with shape constraints (2016)
- Hofner, Benjamin; Kneib, Thomas; Hothorn, Torsten: A unified framework of constrained regression (2016)