find_neighbors

Identifies neighboring partitions by evaluating whether the midpoint between
cluster centers is closer to those centers than to any other center.

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

find_neighbors function

<dl><dt>centers</dt>
<dd>Matrix; rows are cluster center coordinates</dd>
<dt>parallel</dt>
<dd>Logical; use parallel processing</dd>
<dt>cl</dt>
<dd>Cluster object for parallel execution</dd>
<dt>neighbor_tolerance</dt>
<dd>Numeric; scaling factor for distance comparisons</dd></dl>

Arguments

Find Neighboring Cluster Partitions Using Midpoint Distance Criterion — find_neighbors

<dl>

<dt>centers</dt>
<dd>Matrix; rows are cluster center coordinates</dd>


<dt>parallel</dt>
<dd>Logical; use parallel processing</dd>


<dt>cl</dt>
<dd>Cluster object for parallel execution</dd>


<dt>neighbor_tolerance</dt>
<dd>Numeric; scaling factor for distance comparisons</dd>

</dl>

find_neighbors: Find Neighboring Cluster Partitions Using Midpoint Distance Criterion

Description

Usage

Value

Arguments