CleanWeights

Clean out near-zeros from a probability weights vector and re-normalize

internal

Implements methods for obtaining kernel density estimates
subject to a variety of shape constraints (unimodality, bimodality,
symmetry, tail monotonicity, bounds, and constraints on the number of
inflection points). Enforcing constraints can eliminate unwanted waves or
kinks in the estimate, which improves its subjective appearance and can
also improve statistical performance. The main function scdensity() is
very similar to the density() function in 'stats', allowing
shape-restricted estimates to be obtained with little effort. The
methods implemented in this package are described in Wolters and Braun
(2017) <doi:10.1080/03610918.2017.1288247>, Wolters (2012)
<doi:10.18637/jss.v047.i06>, and Hall and Huang (2002)
<https://www3.stat.sinica.edu.tw/statistica/j12n4/j12n41/j12n41.htm>.
See the scdensity() help for for full citations.

Mark A Wolters

scdensity

Shape-Constrained Kernel Density Estimation

CleanWeights function

<dl><dt>w</dt>
<dd>A vector of probability weights</dd></dl>

Arguments

Clean out near-zeros from a probability weights vector and re-normalize — CleanWeights

<dl>

<dt>w</dt>
<dd>A vector of probability weights</dd>

</dl>

CleanWeights: Clean out near-zeros from a probability weights vector and re-normalize

Description

Usage

Value

Arguments