skewness

Should the finite sample correction (bias correction)
be used? Defaults to TRUE.

finite


This function calculates the skewness of a data with optional 
bias correction. The skewness is a measure of the symmetry of a 
distrbution. A negative skewness means the data is left skewed or 
has a fat left tail. The converse is true for a positive skew.


MOMENT

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.

skewness: Skewness Function

Description

Usage

Arguments

Examples