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mgcv (version 1.1-8)

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

57,632

Version

1.1-8

License

GPL version 2 or later

Maintainer

Simon Wood

Last Published

April 4th, 2025

Functions in mgcv (1.1-8)

gam.check

Some diagnostics for a fitted gam model.
logLik.gam

Extract the log likelihood for a fitted GAM
full.score

GCV/UBRE score for use within nlm
formula.gam

Extract the formula from a gam object.
gamm

Generalized Additive Mixed Models
interpret.gam

Interpret a GAM formula
gam.neg.bin

GAMs with the negative binomial distribution
mroot

Smallest square root of matrix
mgcv

Multiple Smoothing Parameter Estimation by GCV or UBRE
magic.post.proc

Auxilliary information from magic fit
gamm.setup

Generalized Additive Mixed Model set up.
gam

Generalized Additive Models using penalized regression splines and GCV
formXtViX

Form component of GAMM covariance matrix
mono.con

Monotonicity constraints for a cubic regression spline.
vis.gam

Visualization of GAM objects
gam.fit

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

Generalized Additive Model Selection
smooth.construct

Constructor functions for smooth terms in a GAM
residuals.gam

Generalized Additive Model residuals
gamObject

Fitted gam object
extract.lme.cov

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

Generalized Additive Model set up.
get.var

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

Setting mgcv defaults
anova.gam

Hypothesis tests related to GAM fits
pcls

Penalized Constrained Least Squares Fitting
null.space.dimension

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

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

Functions implementing a pdMat class for tensor product smooths
s

Defining smooths in GAM formulae
gam.convergence

GAM convergence issues.
summary.gam

Summary for a GAM fit
tensor.prod.model.matrix

Utility functions for constructing tensor product smooths
gam.control

Setting GAM fitting defaults
te

Define tensor product smooths in GAM formulae
predict.gam

Prediction from fitted GAM model
pdIdnot

Overflow proof pdMat class for multiples of the identity matrix
uniquecombs

find the unique rows in a matrix
print.gam

Generalized Additive Model default print statement
step.gam

Alternatives to step.gam
place.knots

Automatically place a set of knots evenly through covariate values
exclude.too.far

Exclude prediction grid points too far from data
plot.gam

Default GAM plotting
magic

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

Prediction methods for smooth terms in a GAM
gam.models

Specifying generalized Additive Models.
notExp

Functions for better-than-log positive parameterization
gam.side.conditions

Identifiability side conditions for a GAM.
influence.gam

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