S.LPR

Compute the Local Polynomial Regression smoother matrix of degree p.
Special cases: p=0 is Nadaraya-Watson, p=1 is Local Linear Regression.

Functional data analysis tools with a high-performance 'Rust' backend.
Provides methods for functional data manipulation, depth computation,
distance metrics, regression, and statistical testing. Supports both
1D functional data (curves) and 2D functional data (surfaces).
Methods are described in Ramsay and Silverman (2005,
ISBN:978-0-387-40080-8) "Functional Data Analysis" and Ferraty and
Vieu (2006, ISBN:978-0-387-30369-7) "Nonparametric Functional Data
Analysis".

Dr. Mueller

fdars

Functional Data Analysis in 'Rust'

Simon Müller

S.LPR function

<dl><dt>tt</dt>
<dd>Evaluation points (numeric vector).</dd>
<dt>h</dt>
<dd>Bandwidth parameter.</dd>
<dt>p</dt>
<dd>Polynomial degree (default 1 for local linear).</dd>
<dt>Ker</dt>
<dd>Kernel function or name.</dd>
<dt>w</dt>
<dd>Optional weights vector.</dd>
<dt>cv</dt>
<dd>Logical. If TRUE, compute leave-one-out cross-validation matrix.</dd></dl>

Arguments

Local Polynomial Regression Smoother Matrix — S.LPR

<dl>

<dt>tt</dt>
<dd>Evaluation points (numeric vector).</dd>


<dt>h</dt>
<dd>Bandwidth parameter.</dd>


<dt>p</dt>
<dd>Polynomial degree (default 1 for local linear).</dd>


<dt>Ker</dt>
<dd>Kernel function or name.</dd>


<dt>w</dt>
<dd>Optional weights vector.</dd>


<dt>cv</dt>
<dd>Logical. If TRUE, compute leave-one-out cross-validation matrix.</dd>

</dl>

S.LPR: Local Polynomial Regression Smoother Matrix

Description

Usage

Value

Arguments

Examples