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mgcv (version 1.0-8)

Multiple smoothing parameter estimation and GAMs by GCV

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

1.0-8

License

GPL version 2 or later

Maintainer

Simon Wood

Last Published

November 7th, 2025

Functions in mgcv (1.0-8)

exclude.too.far

Exclude prediction grid points too far from data
pdIdnot

Overflow proof pdMat class for multiples of the identity matrix
new.name

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

Generalized Additive Mixed Models
mroot

Smallest square root of matrix
te

Define tensor product smooths in GAM formulae
tensor.prod.model.matrix

Utility functions for constructing tensor product smooths
uniquecombs

find the unique rows in a matrix
Predict.matrix

Prediction methods for smooth terms in a GAM
gam.fit

Generalized Additive Models fitting using penalized regression splines and GCV
mgcv.control

Setting mgcv defaults
gam.side.conditions

Identifiability side conditions for a GAM.
full.score

GCV/UBRE score for use within nlm
gam.selection

Generalized Additive Model Selection
pcls

Penalized Constrained Least Squares Fitting
magic

Stable Multiple Smoothing Parameter Estimation by GCV or UBRE, with optional fixed penalty
place.knots

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

Summary for a GAM fit
extract.lme.cov

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

Interpret a GAM formula
vis.gam

Visualization of GAM objects
gam.setup

Generalized Additive Model set up.
gam.control

Setting GAM fitting defaults
mono.con

Monotonicity constraints for a cubic regression spline.
plot.gam

Default GAM plotting
residuals.gam

Generalized Additive Model residuals
gam.check

Some diagnostics for a fitted gam model.
smooth.construct

Constructor functions for smooth terms in a GAM
gamm.setup

Generalized Additive Mixed Model set up.
mgcv

Multiple Smoothing Parameter Estimation by GCV or UBRE
get.var

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

Specifying generalized Additive Models.
gam.neg.bin

GAMs with the negative binomial distribution
predict.gam

Prediction from fitted GAM model
null.space.dimension

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

Generalized Additive Models using penalized regression splines and GCV
pdTens

Functions implementing a pdMat class for tensor product smooths
magic.post.proc

Auxilliary information from magic fit
s

Defining smooths in GAM formulae
print.gam

Generalized Additive Model default print statement
notExp

Functions for better-than-log positive parameterization