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mgcv (version 1.2-0)

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

1.2-0

License

GPL version 2 or later

Maintainer

Simon Wood

Last Published

November 7th, 2025

Functions in mgcv (1.2-0)

GCV/UBRE score for use within nlm

Visualization of GAM objects

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

Default GAM plotting

Summary for a GAM fit

Generalized Additive Model residuals

Some diagnostics for a fitted gam model.

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

Generalized Additive Model default print statement

extract.lme.cov

Extract the data covariance matrix from an lme object

find the unique rows in a matrix

Fitted gam object

Setting GAM fitting defaults

fix.family.link

Modify families for use in GAM fitting

Automatically place a set of knots evenly through covariate values

null.space.dimension

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

Define tensor product smooths in GAM formulae

Setting GAM fitting method

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

Prediction/Construction wrapper functions for GAM smooth terms

Functions implementing a pdMat class for tensor product smooths

Smallest square root of matrix

GAMs with the negative binomial distribution

Form component of GAMM covariance matrix

exclude.too.far

Exclude prediction grid points too far from data

Functions for better-than-log positive parameterization

Starting values for multiple smoothing parameter estimation.

Specifying Generalized Additive Models.

Extract the log likelihood for a fitted GAM

Interpret a GAM formula

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

Generalized Additive Models using penalized regression splines and GCV

smooth.construct

Constructor functions for smooth terms in a GAM

Multiple Smoothing Parameter Estimation by GCV or UBRE

Prediction methods for smooth terms in a GAM

Identifiability side conditions for a GAM.

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

P-IRLS GAM estimation with GCV & UBRE derivative calculation

Objective functions for GAM smoothing parameter estimation.

Monotonicity constraints for a cubic regression spline.

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

Generalized Additive Model set up.

Generalized Additive Mixed Model set up.

magic.post.proc

Auxilliary information from magic fit

gam.convergence

GAM convergence and performance issues.

Prediction from fitted GAM model

Detect linear dependencies of one matrix on another

Generalized Additive Mixed Models

Penalized Constrained Least Squares Fitting

Defining smooths in GAM formulae

Generalized Additive Model Selection

Hypothesis tests related to GAM fits

Extract the formula from a gam object.

Alternatives to step.gam

tensor.prod.model.matrix

Utility functions for constructing tensor product smooths

Overflow proof pdMat class for multiples of the identity matrix

Setting mgcv defaults