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mgcv (version 1.1-2)

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

1.1-2

License

GPL version 2 or later

Maintainer

Simon Wood

Last Published

November 7th, 2025

Functions in mgcv (1.1-2)

gam.convergence

GAM convergence issues.
mgcv

Multiple Smoothing Parameter Estimation by GCV or UBRE
mono.con

Monotonicity constraints for a cubic regression spline.
summary.gam

Summary for a GAM fit
step.gam

Alternatives to step.gam
gam.side.conditions

Identifiability side conditions for a GAM.
interpret.gam

Interpret a GAM formula
predict.gam

Prediction from fitted GAM model
te

Define tensor product smooths in GAM formulae
smooth.construct

Constructor functions for smooth terms in a GAM
print.gam

Generalized Additive Model default print statement
gam

Generalized Additive Models using penalized regression splines and GCV
new.name

Obtain a name for a new variable that is not already in use
extract.lme.cov

Extract the data covariance matrix from an lme object
magic

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

Setting GAM fitting defaults
influence.gam

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

Default GAM plotting
gam.neg.bin

GAMs with the negative binomial distribution
s

Defining smooths in GAM formulae
pcls

Penalized Constrained Least Squares Fitting
Predict.matrix

Prediction methods for smooth terms in a GAM
gam.setup

Generalized Additive Model set up.
tensor.prod.model.matrix

Utility functions for constructing tensor product smooths
gam.selection

Generalized Additive Model Selection
null.space.dimension

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

GCV/UBRE score for use within nlm
mgcv.control

Setting mgcv defaults
residuals.gam

Generalized Additive Model residuals
place.knots

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

Some diagnostics for a fitted gam model.
gam.models

Specifying generalized Additive Models.
mroot

Smallest square root of matrix
get.var

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

Extract the log likelihood for a fitted GAM
exclude.too.far

Exclude prediction grid points too far from data
gam.fit

Generalized Additive Models fitting using penalized regression splines and GCV
vis.gam

Visualization of GAM objects
magic.post.proc

Auxilliary information from magic fit
notExp

Functions for better-than-log positive parameterization
formula.gam

Extract the formula from a gam object.
anova.gam

Hypothesis tests related to GAM fits
uniquecombs

find the unique rows in a matrix
gamm.setup

Generalized Additive Mixed Model set up.
gamObject

Fitted gam object
formXtViX

Form component of GAMM covariance matrix
pdTens

Functions implementing a pdMat class for tensor product smooths
pdIdnot

Overflow proof pdMat class for multiples of the identity matrix
gamm

Generalized Additive Mixed Models