Learn R Programming

⚠️There's a newer version (1.9-4) of this package.Take me there.

mgcv (version 1.3-3)

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.

Copy Link

Version

Install

install.packages('mgcv')

Monthly Downloads

106,646

Version

1.3-3

License

GPL version 2 or later

Maintainer

Simon Wood

Last Published

November 7th, 2025

Functions in mgcv (1.3-3)

anova.gam

Hypothesis tests related to GAM fits
gamm.setup

Generalized Additive Mixed Model set up.
gam.setup

Generalized Additive Model set up.
gam.side

Identifiability side conditions for a GAM.
gam.neg.bin

GAMs with the negative binomial distribution
gam.fit

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

GAM convergence and performance issues.
exclude.too.far

Exclude prediction grid points too far from data
Predict.matrix

Prediction methods for smooth terms in a GAM
gamObject

Fitted gam object
fix.family.link

Modify families for use in GAM fitting
summary.gam

Summary for a GAM fit
formula.gam

Extract the formula from a gam object.
mgcv

Multiple Smoothing Parameter Estimation by GCV or UBRE
choose.k

Basis dimension choice for smooths
gam.models

Specifying Generalized Additive Models.
extract.lme.cov

Extract the data covariance matrix from an lme object
gam.control

Setting GAM fitting defaults
smooth.construct

Constructor functions for smooth terms in a GAM
gam.selection

Generalized Additive Model Selection
influence.gam

Extract the diagonal of the Influence/Hat matrix for a GAM.
gam.fit2

P-IRLS GAM estimation with GCV & UBRE derivative calculation
logLik.gam

Extract the log likelihood for a fitted GAM
mgcv.control

Setting mgcv defaults
gamm

Generalized Additive Mixed Models
full.score

GCV/UBRE score for use within nlm
gam.outer

Minimize GCV or UBRE score of a GAM using `outer' iteration
null.space.dimension

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

Form component of GAMM covariance matrix
smoothCon

Prediction/Construction wrapper functions for GAM smooth terms
magic

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

Extract parameter (estimator) covariance matrix from GAM fit
fixDependence

Detect linear dependencies of one matrix on another
pcls

Penalized Constrained Least Squares Fitting
gam2objective

Objective functions for GAM smoothing parameter estimation.
place.knots

Automatically place a set of knots evenly through covariate values
residuals.gam

Generalized Additive Model residuals
get.var

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

Interpret a GAM formula
initial.sp

Starting values for multiple smoothing parameter estimation.
mroot

Smallest square root of matrix
step.gam

Alternatives to step.gam
magic.post.proc

Auxilliary information from magic fit
predict.gam

Prediction from fitted GAM model
new.name

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

Some diagnostics for a fitted gam model
uniquecombs

find the unique rows in a matrix
pdIdnot

Overflow proof pdMat class for multiples of the identity matrix
vis.gam

Visualization of GAM objects
mono.con

Monotonicity constraints for a cubic regression spline.
s

Defining smooths in GAM formulae
gam.method

Setting GAM fitting method
plot.gam

Default GAM plotting
mgcv-package

GAMs with GCV smoothness estimation and GAMMs by REML/PQL
tensor.prod.model.matrix

Utility functions for constructing tensor product smooths
notExp

Functions for better-than-log positive parameterization
notExp2

Alternative to log parameterization for variance components
gam

Generalized additive models with integrated smoothness estimation
model.matrix.gam

Extract model matrix from GAM fit
print.gam

Generalized Additive Model default print statement
te

Define tensor product smooths in GAM formulae
pdTens

Functions implementing a pdMat class for tensor product smooths