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Mixed GAM Computation Vehicle with Automatic Smoothness Estimation
Generalized additive (mixed) models, some of their extensions and
other generalized ridge regression with multiple smoothing
parameter estimation by (Restricted) Marginal Likelihood,
Generalized Cross Validation and similar, or using iterated
nested Laplace approximation for fully Bayesian inference. See
Wood (2017)
Generalized Additive Mixed Models using 'mgcv' and 'lme4'
Estimate generalized additive mixed models via a version of function gamm() from 'mgcv', using 'lme4' for estimation.
Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv'
Graceful 'ggplot'-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the 'mgcv' package. Provides a reimplementation of the plot() method for GAMs that 'mgcv' provides, as well as 'tidyverse' compatible representations of estimated smooths.
Exact (Restricted) Likelihood Ratio Tests for Mixed and Additive Models
Rapid, simulation-based exact (restricted) likelihood ratio tests for testing the presence of variance components/nonparametric terms for models fit with nlme::lme(),lme4::lmer(), lmeTest::lmer(), gamm4::gamm4(), mgcv::gamm() and SemiPar::spm().
Bindings for Additive TidyModels
Fit Generalized Additive Models (GAM) using 'mgcv' with 'parsnip'/'tidymodels'
via 'additive'
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').
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(2022)
Conditional Akaike Information Criterion for 'lme4' and 'nlme'
Provides functions for the estimation of the conditional Akaike
information in generalized mixed-effect models fitted with (g)lmer()
from 'lme4', lme() from 'nlme' and gamm() from 'mgcv'.
For a manual on how to use 'cAIC4', see Saefken et al. (2021)