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mgcv (version 0.9-3)

Multiple smoothing parameter estimation and GAMs by GCV

Description

Routines for GAMs and other generalized ridge regression problems with multiple smoothing parameter selection by GCV or UBRE. Includes an implementation (not a clone) of gam().

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Version

Install

install.packages('mgcv')

Monthly Downloads

97,913

Version

0.9-3

License

GPL version 2 or later

Maintainer

Simon Wood

Last Published

November 7th, 2025

Functions in mgcv (0.9-3)

QT factorisation of a matrix

Set up GAM using penalized regression splines

Default GAM plotting

Visualization of GAM objects

GAMs with the negative binomial distribution

Generalized Additive Model residuals

Multiple Smoothing Parameter Estimation by GCV or UBRE

Summary for a GAM fit

Monotonicity constraints for a cubic regression spline.

Specifying generalized Additive Models.

exclude.too.far

Exclude prediction grid points too far from data

Setting GAM fitting defaults

null.space.dimension

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

magic.post.proc

Auxilliary information from magic fit

gam.side.conditions

Identifiability side conditions for a GAM.

Defining smooths in GAM formulae

Prediction from fitted GAM model

find the unique rows in a matrix

GCV/UBRE score for use withn nlm

Generalized Additive Model default print statement

Generalized Additive Models fitting using penalized regression splines and GCV

Generalized Additive Models using penalized regression splines and GCV

Penalized Constrained Least Squares Fitting

Some diagnostics for a fitted gam model.

Generalized Additive Model fitting using penalized regression splines and GCV

Generalized Additive Model set up.

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

Smallest square root of matrix

Generalized Additive Model Selection