Bindings for Additive TidyModels

Fit Generalized Additive Models (GAM) using 'mgcv' with 'parsnip'/'tidymodels' via 'additive' . 'tidymodels' is a collection of packages for machine learning; see Kuhn and Wickham (2020) < https://www.tidymodels.org>). The technical details of 'mgcv' are described in Wood (2017) .

Discrete Prolate Spheroidal (Slepian) Sequence Regression Smoothers

Interface for creation of 'slp' class smoother objects for use in Generalized Additive Models (as implemented by packages 'gam' and 'mgcv').

Interpreting Time Series and Autocorrelated Data Using GAMMs

GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999) as implemented in the R package 'mgcv' (Wood, S.N., 2006; 2011) is a nonlinear regression analysis which is particularly useful for time course data such as EEG, pupil dilation, gaze data (eye tracking), and articulography recordings, but also for behavioral data such as reaction times and response data. As time course measures are sensitive to autocorrelation problems, GAMMs implements methods to reduce the autocorrelation problems. This package includes functions for the evaluation of GAMM models (e.g., model comparisons, determining regions of significance, inspection of autocorrelational structure in residuals) and interpreting of GAMMs (e.g., visualization of complex interactions, and contrasts).

Censored Regression with Smooth Terms

Implementation of Tobit type I and type II families for censored regression using the 'mgcv' package, based on methods detailed in Woods (2016) .

Tidy Prediction and Plotting of Generalised Additive Models

Provides functions that compute predictions from Generalised Additive Models (GAMs) fitted with 'mgcv' and return them as a tibble. These can be plotted with a generic plot()-method that uses 'ggplot2' or plotted as any other data frame. The main function is predict_gam().

Hierarchical Partitioning of Adjusted R2 and Explained Deviance for Generalized Additive Models

Conducts hierarchical partitioning to calculate individual contributions of each predictor towards adjusted R2 and explained deviance for generalized additive models based on output of gam()in 'mgcv' package, applying the algorithm in this paper: Lai(2024) .

Estimate Spatial Population Indices from Ecological Abundance Data

Compute relative or absolute population trends across space and time using predictions from models fitted to ecological population abundance data, as described in Knape (2025) . The package supports models fitted by 'mgcv' or 'brms', and draws from posterior predictive distributions.

Automate the Creation of Generalized Additive Models (GAMs)

This wrapper package for 'mgcv' makes it easier to create high-performing Generalized Additive Models (GAMs). With its central function autogam(), by entering just a dataset and the name of the outcome column as inputs, 'AutoGAM' tries to automate the procedure of configuring a highly accurate GAM which performs at reasonably high speed, even for large datasets.

Visualisations for Generalized Additive Models

Extension of the 'mgcv' package, providing visual tools for Generalized Additive Models that exploit the additive structure of such models, scale to large data sets and can be used in conjunction with a wide range of response distributions. The focus is providing visual methods for better understanding the model output and for aiding model checking and development beyond simple exponential family regression. The graphical framework is based on the layering system provided by 'ggplot2'.

Visualising Statistical Models using Dynamic Nomograms

Demonstrate the results of a statistical model object as a dynamic nomogram in an RStudio panel or web browser. The package provides two generics functions: DynNom, which display statistical model objects as a dynamic nomogram; DNbuilder, which builds required scripts to publish a dynamic nomogram on a web server such as the < https://www.shinyapps.io/>. Current version of 'DynNom' supports stats::lm, stats::glm, survival::coxph, rms::ols, rms::Glm, rms::lrm, rms::cph, and mgcv::gam model objects.