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mgcv (version 0.9-2)

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

481,715

Version

0.9-2

License

GPL version 2 or later

Maintainer

Simon Wood

Last Published

April 4th, 2025

Functions in mgcv (0.9-2)

gam.control

Setting GAM fitting defaults
predict.gam

Prediction from fitted GAM model
GAMsetup

Set up GAM using penalized regression splines
mgcv

Multiple Smoothing Parameter Estimation by GCV or UBRE
gam

Generalized Additive Models using penalized regression splines and GCV
print.gam

Generalized Additive Model default print statement
vis.gam

Visualization of GAM objects
mono.con

Monotonicity constraints for a cubic regression spline.
gam.models

Specifying generalized Additive Models.
summary.gam

Summary for a GAM fit
pcls

Penalized Constrained Least Squares Fitting
magic.post.proc

Auxilliary information from magic fit
gam.parser

Generalized Additive Model fitting using penalized regression splines and GCV
gam.selection

Generalized Additive Model Selection
magic

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

Smallest square root of matrix
plot.gam

Default GAM plotting
s

Defining smooths in GAM formulae
QT

QT factorisation of a matrix
gam.setup

Generalized Additive Model set up.
gam.check

Some diagnostics for a fitted gam model.
gam.side.conditions

Identifiability side conditions for a GAM.
uniquecombs

find the unique rows in a matrix
full.score

GCV/UBRE score for use withn nlm
null.space.dimension

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

Generalized Additive Model residuals
gam.fit

Generalized Additive Models fitting using penalized regression splines and GCV
exclude.too.far

Exclude prediction grid points too far from data
gam.neg.bin

GAMs with the negative binomial distribution