These are experimental sparse smoothing functions, and should 
be left well alone!

spasm.construct

Generalized additive (mixed) models, some of their extensions and
other generalized ridge regression with multiple smoothing
parameter estimation by (Restricted) Marginal Likelihood,
Cross Validation and similar, or using iterated nested Laplace
approximation for fully Bayesian inference. See Wood (2025)
<doi:10.1146/annurev-statistics-112723-034249> for an overview.
Includes a gam() function, a wide variety of smoothers, 'JAGS'
support and distributions beyond the exponential family.

Simon Wood

mgcv

Mixed GAM Computation Vehicle with Automatic Smoothness
Estimation

spasm.construct function

Experimental sparse smoothers — spasm.construct

<dl>

<dt>object</dt>
<dd>sparse smooth object</dd>

<dt>data</dt>
<dd>data frame</dd>

<dt>sp</dt>
<dd>smoothing parameter value</dd>

<dt>w</dt>
<dd>optional weights</dd>

<dt>get.trH</dt>
<dd>Should (estimated) trace of sparse smoother matrix be returned</dd>

<dt>block</dt>
<dd>index of block, 0 for all blocks</dd>

<dt>centre</dt>
<dd>should sparse smooth be centred?</dd>

<dt>X</dt>
<dd>what to smooth</dd>

<dt>residual</dt>
<dd>apply residual operation?</dd>

</dl>

Arguments

Simon N. Wood <a href='mailto:simon.wood@r-project.org'>simon.wood@r-project.org</a>

Author

It is not recommended to use these yet

spasm.construct: Experimental sparse smoothers

Description

Usage

Arguments