An R package which provides a a neural network framework based on Generalized Additive Models
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Updated
Sep 13, 2024 - R
An R package which provides a a neural network framework based on Generalized Additive Models
ggplot-based graphics and useful functions for GAMs fitted using the mgcv package
Bindings for Additive TidyModels
A document introducing generalized additive models.📈
The parsnip backend for GAM Models.
GAM-based model that predicts FIP based on expected whiff rate, command and expected contact from Statcast data
👓 Functions related to R visualizations
Functions for using mgcv for mixed models. 📈
Code for full subsets model fitting using GA(M)M
My scripts from BL5233 lectures and practicals.
Boosting models for fitting generalized additive models for location, shape and scale (GAMLSS) to potentially high dimensional data. The current relase version can be found on CRAN (https://cran.r-project.org/package=gamboostLSS).
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