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^n1) 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
Browse man pages Browse package API and functions Browse package files
Author  Sathish Deevi [aut, cre], 4D Strategies [aut,own] 
Date of publication  20160307 07:59:58 
Maintainer  Sathish Deevi <SathishCDeevi@gmail.com> 
License  CC BYNCSA 4.0 
Version  0.7.0 
URL  http://www.sdeevi.com/modes_package https://github.com/sathishdeevi/modesPackage/ 
Package repository  View on CRAN 
Installation  Install the latest version of this package by entering the following in R:

Package overview 
Functions  

Ashmans_D  Man page Source code 
amps  Man page Source code 
bimodality_amplitude  Man page Source code 
bimodality_coefficient  Man page Source code 
bimodality_ratio  Man page Source code 
bimodality_separation  Man page Source code 
kurtosis  Man page Source code 
modes  Man page Source code 
modespackage  Man page 
nth_highest  Man page Source code 
skewness  Man page Source code 
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.