OVL.LogitDBC

Parametric approach using the delta method after the Box-Cox transformation after switching to a logit scale and then transforming back.

Provides functions to construct confidence intervals for the Overlap Coefficient (OVL). OVL measures the similarity between two distributions through the overlapping area of their distribution functions. Given its intuitive description and ease of visual representation by the straightforward depiction of the amount of overlap between the two corresponding histograms based on samples of measurements from each one of the two distributions, the development of accurate methods for confidence interval construction can be useful for applied researchers. Implements methods based on the work of Franco-Pereira, A.M., Nakas, C.T., Reiser, B., and Pardo, M.C. (2021) <doi:10.1177/09622802211046386> as well as extensions for multimodal distributions proposed by Alcaraz-Peñalba, A., Franco-Pereira, A., and Pardo, M.C. (2025) <doi:10.1007/s10182-025-00545-2>.

Alba M. Franco-Pereira 

OVL.CI

Inference on the Overlap Coefficient

Christos T. Nakas 

Benjamin Reiser 

M.Carmen Pardo 

Alba Alcaraz-Peñalba 

OVL.LogitDBC function

<dl><dt>x</dt>
<dd>Numeric vector. Data from the first group.</dd>
<dt>y</dt>
<dd>Numeric vector. Data from the second group.</dd>
<dt>alpha</dt>
<dd>confidence level.</dd>
<dt>h_ini</dt>
<dd>initial value in the optimization problem.</dd></dl>

Arguments

OVL.LogitDBC — OVL.LogitDBC

<dl>

<dt>x</dt>
<dd>Numeric vector. Data from the first group.</dd>


<dt>y</dt>
<dd>Numeric vector. Data from the second group.</dd>


<dt>alpha</dt>
<dd>confidence level.</dd>


<dt>h_ini</dt>
<dd>initial value in the optimization problem.</dd>

</dl>

OVL.LogitDBC: OVL.LogitDBC

Description

Usage

Value

Arguments

Examples