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mgcv (version 0.9-6)

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

51,868

Version

0.9-6

License

GPL version 2 or later

Maintainer

Simon Wood

Last Published

December 21st, 2023

Functions in mgcv (0.9-6)

magic.post.proc

Auxilliary information from magic fit

Generalized Additive Model set up.

Specifying generalized Additive Models.

exclude.too.far

Exclude prediction grid points too far from data

find the unique rows in a matrix

Generalized Additive Model Selection

Generalized Additive Model residuals

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

QT factorisation of a matrix

Prediction from fitted GAM model

Summary for a GAM fit

Multiple Smoothing Parameter Estimation by GCV or UBRE

Generalized Additive Models fitting using penalized regression splines and GCV

Setting GAM fitting defaults

Monotonicity constraints for a cubic regression spline.

GAMs with the negative binomial distribution

Visualization of GAM objects

Default GAM plotting

gam.side.conditions

Identifiability side conditions for a GAM.

Generalized Additive Models using penalized regression splines and GCV

Penalized Constrained Least Squares Fitting

Generalized Additive Model default print statement

Generalized Additive Model fitting using penalized regression splines and GCV

null.space.dimension

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

Some diagnostics for a fitted gam model.

Defining smooths in GAM formulae

GCV/UBRE score for use withn nlm

Smallest square root of matrix

Set up GAM using penalized regression splines