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mgcv (version 0.8-9)

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

0.8-9

License

GPL version 2 or later

Maintainer

Simon Wood

Last Published

April 4th, 2025

Functions in mgcv (0.8-9)

Generalized Additive Models fitting using penalized regression splines and GCV

Set up GAM using penalized regression splines

Prediction from fitted GAM model

find the unique rows in a matrix

Generalized Additive Model residuals

Generalized Additive Models using penalized regression splines and GCV

Generalized Additive Model default print statement

Family function for Negative Binomial GAMs

QT factorisation of a matrix

Penalized Constrained Least Squares Fitting

Default GAM plotting

Summary for a GAM fit

Generalized Additive Model Selection

Multiple Smoothing Parameter Estimation by GCV or UBRE

Some diagnostics for a fitted gam model.

Specifying generalized Additive Models.

Defining smooths in GAM formulae

gam.side.conditions

Identifiability side conditions for a GAM.

Generalized Additive Model set up.

Setting Generalized Additive Models fitting defaults

Monotonicity constraints for a cubic regression spline.

Estimate theta of the Negative Binomial by Maximum Likelihood

Generalized Additive Model fitting using penalized regression splines and GCV

Generalized Additive Models using Negative Binomial errors with unknown theta

null.space.dimension

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

Perspective Plot of GAM objects

Identifies families