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mgcv (version 1.3-5)

GAMs with GCV smoothness estimation and GAMMs by REML/PQL

Description

Routines for GAMs and other generalized ridge regression with multiple smoothing parameter selection by GCV or UBRE. Also GAMMs by REML or PQL. Includes a gam() function.

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Version

Install

install.packages('mgcv')

Monthly Downloads

106,646

Version

1.3-5

License

GPL version 2 or later

Maintainer

Simon Wood

Last Published

November 7th, 2025

Functions in mgcv (1.3-5)

Interpret a GAM formula

Objective functions for GAM smoothing parameter estimation.

Specifying Generalized Additive Models.

Multiple Smoothing Parameter Estimation by GCV or UBRE

Identifiability side conditions for a GAM.

Functions for better-than-log positive parameterization

Some diagnostics for a fitted gam model.

Generalized Additive Model set up.

Prediction from fitted GAM model

Fitted gam object

Form component of GAMM covariance matrix

fix.family.link

Modify families for use in GAM fitting

Detect linear dependencies of one matrix on another

Visualization of GAM objects

Obtain a name for a new variable that is not already in use

Monotonicity constraints for a cubic regression spline.

Setting GAM fitting method

Define tensor product smooths in GAM formulae

Hypothesis tests related to GAM fits

Setting mgcv defaults

Setting GAM fitting defaults

Extract the formula from a gam object.

Prediction methods for smooth terms in a GAM

Generalized Additive Model default print statement

Penalized Constrained Least Squares Fitting

exclude.too.far

Exclude prediction grid points too far from data

P-IRLS GAM estimation with GCV & UBRE derivative calculation

Extract the diagonal of the Influence/Hat matrix for a GAM.

Smallest square root of matrix

GCV/UBRE score for use within nlm

Prediction/Construction wrapper functions for GAM smooth terms

Generalized Additive Mixed Model set up.

GAMs with the negative binomial distribution

tensor.prod.model.matrix

Utility functions for constructing tensor product smooths

Starting values for multiple smoothing parameter estimation.

smooth.construct

Constructor functions for smooth terms in a GAM

Overflow proof pdMat class for multiples of the identity matrix

gam.convergence

GAM convergence and performance issues.

magic.post.proc

Auxilliary information from magic fit

Generalized additive models with integrated smoothness estimation

Automatically place a set of knots evenly through covariate values

null.space.dimension

The basis of the space of un-penalized functions for a t.p.r.s.

Alternatives to step.gam

GAM P-IRLS estimation with GCV/UBRE smoothness estimation.

Alternative to log parameterization for variance components

Stable Multiple Smoothing Parameter Estimation by GCV or UBRE, with optional fixed penalty

extract.lme.cov

Extract the data covariance matrix from an lme object

Default GAM plotting

Generalized Additive Model Selection

Generalized Additive Model residuals

Extract the log likelihood for a fitted GAM

Minimize GCV or UBRE score of a GAM using `outer' iteration

Summary for a GAM fit

Functions implementing a pdMat class for tensor product smooths

Generalized Additive Mixed Models

Defining smooths in GAM formulae

Extract parameter (estimator) covariance matrix from GAM fit

find the unique rows in a matrix

Get named variable or evaluate expression from list or data.frame