bimodality_separation

This function calculates the Bimodality Separation of a data 
vector. Similar to Ashman, Bird, and Zepf's D statistic 
("Ashman's D"), the Bimodality Separation statistic measures
how differentiated two distributions (distribution components)
are. However, this statistic uses the added assumption that both
are Gaussian (normal) distributions (or that the distribution is 
a mixture of two Gaussian (normal) components).
For instance, if the two distributions are identical, this 
statistic is zero.

Measure

Bimodality

Modality

Parametric

Designed with a dual purpose of
    accurately estimating the mode (or modes) as well as characterizing
    the modality of data. The specific application area includes complex
    or mixture distributions particularly in a big data environment.
    The heterogeneous nature of (big) data may require deep introspective
    statistical and machine learning techniques, but these statistical tools
    often fail when applied without first understanding the data. In small
    datasets, this often isn't a big issue, but when dealing with large scale
    data analysis or big data thoroughly inspecting each dimension
    typically yields an O(n^n-1) problem. As such, dealing with big data
    require an alternative toolkit. This package not only identifies the
    mode or modes for various data types, it also provides a programmatic
    way of understanding the modality (i.e. unimodal, bimodal, etc.) of
    a dataset (whether it's big data or not). See
    <http://www.sdeevi.com/modes_package> for examples and discussion.

Sathish Deevi

modes

Find the Modes and Assess the Modality of Complex and Mixture
        Distributions, Especially with Big Datasets

bimodality_separation function

Bimodality Separation Function

bimodality_separation: Bimodality Separation Function

Description

Usage

Arguments

References

Examples