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mgcv (version 1.3-13)

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

1.3-13

License

GPL version 2 or later

Maintainer

Simon Wood

Last Published

November 7th, 2025

Functions in mgcv (1.3-13)

Predict.matrix

Prediction methods for smooth terms in a GAM
influence.gam

Extract the diagonal of the influence/hat matrix for a GAM
vis.gam

Visualization of GAM objects
gamObject

Fitted gam object
mgcv-package

GAMs with GCV smoothness estimation and GAMMs by REML/PQL
logLik.gam

Extract the log likelihood for a fitted GAM
extract.lme.cov

Extract the data covariance matrix from an lme object
mroot

Smallest square root of matrix
mgcv

Multiple Smoothing Parameter Estimation by GCV or UBRE
vcov.gam

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

Extract the formula from a gam object
anova.gam

Hypothesis tests related to GAM fits
full.score

GCV/UBRE score for use within nlm
gam.side

Identifiability side conditions for a GAM
interpret.gam

Interpret a GAM formula
gam.models

Specifying generalized additive models
magic

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

Starting values for multiple smoothing parameter estimation
smoothCon

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

Alternatives to step.gam
gam.convergence

GAM convergence and performance issues
summary.gam

Summary for a GAM fit
fixDependence

Detect linear dependencies of one matrix on another
gam.control

Setting GAM fitting defaults
gam.outer

Minimize GCV or UBRE score of a GAM using `outer' iteration
tensor.prod.model.matrix

Utility functions for constructing tensor product smooths
notExp2

Alternative to log parameterization for variance components
gam2objective

Objective functions for GAM smoothing parameter estimation
null.space.dimension

The basis of the space of un-penalized functions for a TPRS
pdTens

Functions implementing a pdMat class for tensor product smooths
place.knots

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

Generalized Additive Model Selection
gam.fit2

P-IRLS GAM estimation with GCV & UBRE derivative calculation
smooth.construct

Constructor functions for smooth terms in a GAM
gam.fit

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

Defining smooths in GAM formulae
predict.gam

Prediction from fitted GAM model
gam.method

Setting GAM fitting method
mgcv.control

Setting mgcv defaults
formXtViX

Form component of GAMM covariance matrix
mono.con

Monotonicity constraints for a cubic regression spline
notExp

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

Generalized Additive Model residuals
gam.neg.bin

GAMs with the negative binomial distribution
plot.gam

Default GAM plotting
exclude.too.far

Exclude prediction grid points too far from data
gamm

Generalized Additive Mixed Models
pcls

Penalized Constrained Least Squares Fitting
print.gam

Generalized Additive Model default print statement
gam.setup

Generalized additive model set up
get.var

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

Overflow proof pdMat class for multiples of the identity matrix
uniquecombs

find the unique rows in a matrix
magic.post.proc

Auxilliary information from magic fit
gamm.setup

Generalized additive mixed model set up
gam.check

Some diagnostics for a fitted gam model
new.name

Obtain a name for a new variable that is not already in use
fix.family.link

Modify families for use in GAM fitting
gam

Generalized additive models with integrated smoothness estimation
te

Define tensor product smooths in GAM formulae