NormalGridFcn

Grid points are specified in g. Returns an m-by-n matrix. The (i,j)th element of
the matrix is the rth derivative of N(mu(j),sd(j)) at g(i).

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

NormalGridFcn function

<dl><dt>g</dt>
<dd>Locations at which to evaluate the normal densities.</dd>
<dt>r</dt>
<dd>Derivative degree of the densities to evaluate (0, 1, 2, or 3).</dd>
<dt>mu</dt>
<dd>Means/locations for the normal densities.</dd>
<dt>sd</dt>
<dd>Standard deviations of the normal densities.</dd></dl>

Arguments

Compute the values of n normal PDFs (or their derivatives at m grid points). — NormalGridFcn

<dl>

<dt>g</dt>
<dd>Locations at which to evaluate the normal densities.</dd>


<dt>r</dt>
<dd>Derivative degree of the densities to evaluate (0, 1, 2, or 3).</dd>


<dt>mu</dt>
<dd>Means/locations for the normal densities.</dd>


<dt>sd</dt>
<dd>Standard deviations of the normal densities.</dd>

</dl>

NormalGridFcn: Compute the values of n normal PDFs (or their derivatives at m grid points).

Description

Usage

Value

Arguments