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mgcv (version 1.2-2)

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

1.2-2

License

GPL version 2 or later

Maintainer

Simon Wood

Last Published

November 7th, 2025

Functions in mgcv (1.2-2)

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

Minimize GCV or UBRE score of a GAM using `outer' iteration

Generalized Additive Mixed Models

Prediction from fitted GAM model

Visualization of GAM objects

Setting GAM fitting defaults

Hypothesis tests related to GAM fits

exclude.too.far

Exclude prediction grid points too far from data

Automatically place a set of knots evenly through covariate values

Generalized Additive Mixed Model set up.

Prediction/Construction wrapper functions for GAM smooth terms

Alternatives to step.gam

fix.family.link

Modify families for use in GAM fitting

Some diagnostics for a fitted gam model.

Fitted gam object

find the unique rows in a matrix

magic.post.proc

Auxilliary information from magic fit

null.space.dimension

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

Overflow proof pdMat class for multiples of the identity matrix

GCV/UBRE score for use within nlm

Functions for better-than-log positive parameterization

Defining smooths in GAM formulae

Interpret a GAM formula

Smallest square root of matrix

Summary for a GAM fit

Functions implementing a pdMat class for tensor product smooths

Identifiability side conditions for a GAM.

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

Starting values for multiple smoothing parameter estimation.

Generalized Additive Models using penalized regression splines and GCV

smooth.construct

Constructor functions for smooth terms in a GAM

gam.convergence

GAM convergence and performance issues.

Monotonicity constraints for a cubic regression spline.

tensor.prod.model.matrix

Utility functions for constructing tensor product smooths

Generalized Additive Model default print statement

GAMs with the negative binomial distribution

Generalized Additive Model set up.

Objective functions for GAM smoothing parameter estimation.

Penalized Constrained Least Squares Fitting

P-IRLS GAM estimation with GCV & UBRE derivative calculation

Extract the log likelihood for a fitted GAM

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

Detect linear dependencies of one matrix on another

Specifying Generalized Additive Models.

Generalized Additive Model Selection

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

Generalized Additive Model residuals

Define tensor product smooths in GAM formulae

Extract the formula from a gam object.

extract.lme.cov

Extract the data covariance matrix from an lme object

Form component of GAMM covariance matrix

Prediction methods for smooth terms in a GAM

Multiple Smoothing Parameter Estimation by GCV or UBRE

Setting GAM fitting method

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

Setting mgcv defaults

Default GAM plotting