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mgcv (version 0.8-6)

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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Install

install.packages('mgcv')

Monthly Downloads

82,900

Version

0.8-6

License

GPL version 2 or later

Maintainer

Simon Wood

Last Published

November 7th, 2025

Functions in mgcv (0.8-6)

Multiple Smoothing Parameter Estimation by GCV or UBRE

Perspective Plot of GAM objects

Summary for a GAM fit

gam.side.conditions

Identifiability side conditions for a GAM.

Defining smooths in GAM formulae

Prediction from fitted GAM model

Generalized Additive Model fitting using penalized regression splines and GCV

Family function for Negative Binomial GAMs

Identifies families

Generalized Additive Model residuals

Some diagnostics for a fitted gam model.

Generalized Additive Models fitting using penalized regression splines and GCV

Specifying generalized Additive Models.

Default GAM plotting

Generalized Additive Models using penalized regression splines and GCV

Estimate theta of the Negative Binomial by Maximum Likelihood

Set up GAM using penalized regression splines

Generalized Additive Model Selection

find the unique rows in a matrix

Generalized Additive Model default print statement

Generalized Additive Model set up.

Monotonicity constraints for a cubic regression spline.

Penalized Constrained Least Squares Fitting

Generalized Additive Models using Negative Binomial errors with unknown theta

QT factorisation of a matrix

Setting Generalized Additive Models fitting defaults

null.space.dimension

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