compute_gram_block_diagonal

Compute Gram Matrix for Block Diagonal Structure

internal

Implements Lagrangian multiplier smoothing splines for flexible
nonparametric regression and function estimation. Provides tools for fitting,
prediction, and inference using a constrained optimization approach to
enforce smoothness.
Supports generalized linear models, Weibull accelerated failure time (AFT) models,
quadratic programming problems, and customizable arbitrary correlation structures.
Options for fitting in parallel are provided. The method builds upon the framework described
by Ezhov et al. (2018) <doi:10.1515/jag-2017-0029> using Lagrangian multipliers
to fit cubic splines. For more information on correlation structure estimation,
see Searle et al. (2009) <ISBN:978-0470009598>. For quadratic programming and
constrained optimization in general, see Nocedal & Wright (2006) <doi:10.1007/978-0-387-40065-5>.
For a comprehensive background on smoothing splines, see Wahba (1990)
<doi:10.1137/1.9781611970128> and Wood (2006) <ISBN:978-1584884743>
"Generalized Additive Models: An Introduction with R".

Matthew Davis

lgspline

Lagrangian Multiplier Smoothing Splines for Smooth Function
Estimation

compute_gram_block_diagonal function

<dl><dt>list_in</dt>
<dd>List of matrices</dd>
<dt>parallel</dt>
<dd>Use parallel processing</dd>
<dt>cl</dt>
<dd>Cluster object</dd>
<dt>chunk_size</dt>
<dd>Chunk size for parallel</dd>
<dt>num_chunks</dt>
<dd>Number of chunks</dd>
<dt>rem_chunks</dt>
<dd>Remaining chunks</dd></dl>

Arguments

Compute Gram Matrix for Block Diagonal Structure — compute_gram_block_diagonal

<dl>

<dt>list_in</dt>
<dd>List of matrices</dd>


<dt>parallel</dt>
<dd>Use parallel processing</dd>


<dt>cl</dt>
<dd>Cluster object</dd>


<dt>chunk_size</dt>
<dd>Chunk size for parallel</dd>


<dt>num_chunks</dt>
<dd>Number of chunks</dd>


<dt>rem_chunks</dt>
<dd>Remaining chunks</dd>

</dl>

compute_gram_block_diagonal: Compute Gram Matrix for Block Diagonal Structure

Description

Usage

Value

Arguments

Details