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mgcv (version 0.8-1)

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

57,632

Version

0.8-1

License

GPL version 2 or later

Maintainer

Simon Wood

Last Published

April 4th, 2025

Functions in mgcv (0.8-1)

Setting Generalized Additive Models fitting defaults

Estimate theta of the Negative Binomial by Maximum Likelihood

Generalized Additive Model set up.

Perspective Plot of GAM objects

Default GAM plotting

Summary for a GAM fit

gam.side.conditions

Identifiability side conditions for a GAM.

Defining smooths in GAM formulae

Set up GAM using penalized regression splines

Generalized Additive Models using Negative Binomial errors with unknown theta

Family function for Negative Binomial GAMs

Penalized Constrained Least Squares Fitting

Monotonicity constraints for a cubic regression spline.

Multiple Smoothing Parameter Estimation by GCV or UBRE

Example of simple additive GAM using penalized regression splines.

Generalized Additive Model fitting using penalized regression splines and GCV

Generalized Additive Models fitting using penalized regression splines and GCV

Some diagnostics for a fitted gam model.

null.space.dimension

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

Generalized Additive Models using penalized regression splines and GCV

find the unique rows in a matrix

Prediction from fitted GAM model

Generalized Additive Model Selection

Specifying generalized Additive Models.

Generalized Additive Model default print statement

Generalized Additive Model residuals

QT factorisation of a matrix

Identifies families