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mgcv (version 1.3-4)

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

1.3-4

License

GPL version 2 or later

Maintainer

Simon Wood

Last Published

November 7th, 2025

Functions in mgcv (1.3-4)

pcls

Penalized Constrained Least Squares Fitting
plot.gam

Default GAM plotting
magic

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

GCV/UBRE score for use within nlm
gam.models

Specifying Generalized Additive Models.
gam.check

Some diagnostics for a fitted gam model.
initial.sp

Starting values for multiple smoothing parameter estimation.
anova.gam

Hypothesis tests related to GAM fits
formula.gam

Extract the formula from a gam object.
exclude.too.far

Exclude prediction grid points too far from data
formXtViX

Form component of GAMM covariance matrix
print.gam

Generalized Additive Model default print statement
notExp

Functions for better-than-log positive parameterization
fixDependence

Detect linear dependencies of one matrix on another
gamObject

Fitted gam object
fix.family.link

Modify families for use in GAM fitting
gam.control

Setting GAM fitting defaults
gam.setup

Generalized Additive Model set up.
gam.selection

Generalized Additive Model Selection
gam

Generalized additive models with integrated smoothness estimation
mgcv

Multiple Smoothing Parameter Estimation by GCV or UBRE
pdTens

Functions implementing a pdMat class for tensor product smooths
gam.convergence

GAM convergence and performance issues.
logLik.gam

Extract the log likelihood for a fitted GAM
mono.con

Monotonicity constraints for a cubic regression spline.
tensor.prod.model.matrix

Utility functions for constructing tensor product smooths
pdIdnot

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

Summary for a GAM fit
magic.post.proc

Auxilliary information from magic fit
uniquecombs

find the unique rows in a matrix
influence.gam

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

Interpret a GAM formula
gam2objective

Objective functions for GAM smoothing parameter estimation.
mgcv.control

Setting mgcv defaults
Predict.matrix

Prediction methods for smooth terms in a GAM
smooth.construct

Constructor functions for smooth terms in a GAM
gam.outer

Minimize GCV or UBRE score of a GAM using `outer' iteration
gam.neg.bin

GAMs with the negative binomial distribution
extract.lme.cov

Extract the data covariance matrix from an lme object
new.name

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

Generalized Additive Mixed Model set up.
vis.gam

Visualization of GAM objects
mroot

Smallest square root of matrix
gamm

Generalized Additive Mixed Models
get.var

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

Identifiability side conditions for a GAM.
step.gam

Alternatives to step.gam
smoothCon

Prediction/Construction wrapper functions for GAM smooth terms
residuals.gam

Generalized Additive Model residuals
null.space.dimension

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

Prediction from fitted GAM model
place.knots

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

Setting GAM fitting method
vcov.gam

Extract parameter (estimator) covariance matrix from GAM fit
gam.fit

GAM P-IRLS estimation with GCV/UBRE smoothness estimation.
gam.fit2

P-IRLS GAM estimation with GCV & UBRE derivative calculation
te

Define tensor product smooths in GAM formulae
s

Defining smooths in GAM formulae