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mgcv (version 1.2-5)

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

1.2-5

License

GPL version 2 or later

Maintainer

Simon Wood

Last Published

November 7th, 2025

Functions in mgcv (1.2-5)

Generalized Additive Mixed Models

gam.convergence

GAM convergence and performance issues.

Specifying Generalized Additive Models.

Form component of GAMM covariance matrix

exclude.too.far

Exclude prediction grid points too far from data

Objective functions for GAM smoothing parameter estimation.

Generalized Additive Model set up.

Identifiability side conditions for a GAM.

Some diagnostics for a fitted gam model.

Setting GAM fitting method

Prediction methods for smooth terms in a GAM

Generalized Additive Model Selection

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

Generalized Additive Mixed Model set up.

Extract the log likelihood for a fitted GAM

Extract the formula from a gam object.

magic.post.proc

Auxilliary information from magic fit

Automatically place a set of knots evenly through covariate values

GAMs with the negative binomial distribution

Interpret a GAM formula

Defining smooths in GAM formulae

Prediction from fitted GAM model

Penalized Constrained Least Squares Fitting

Starting values for multiple smoothing parameter estimation.

find the unique rows in a matrix

Functions implementing a pdMat class for tensor product smooths

P-IRLS GAM estimation with GCV & UBRE derivative calculation

extract.lme.cov

Extract the data covariance matrix from an lme object

GCV/UBRE score for use within nlm

Prediction/Construction wrapper functions for GAM smooth terms

fix.family.link

Modify families for use in GAM fitting

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

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

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

smooth.construct

Constructor functions for smooth terms in a GAM

Summary for a GAM fit

Setting mgcv defaults

null.space.dimension

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

tensor.prod.model.matrix

Utility functions for constructing tensor product smooths

Smallest square root of matrix

Overflow proof pdMat class for multiples of the identity matrix

Monotonicity constraints for a cubic regression spline.

Extract parameter (estimator) covariance matrix from GAM fit

Default GAM plotting

Setting GAM fitting defaults

Functions for better-than-log positive parameterization

Detect linear dependencies of one matrix on another

Generalized additive models with integrated smoothness estimation

Multiple Smoothing Parameter Estimation by GCV or UBRE

Generalized Additive Model residuals

Hypothesis tests related to GAM fits

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

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

Define tensor product smooths in GAM formulae

Alternatives to step.gam

Fitted gam object

Generalized Additive Model default print statement

Visualization of GAM objects