is_normal

A handy wrapper for data normality assessment using the Shapiro-Wilk-Royston,
D'Agostino-Pearson, and Anderson-Darling tests.

A toolkit for assessing data normality using a comprehensive collection of statistical methods.
It includes descriptive measures and formal hypothesis tests, such as skewness and kurtosis tests,
the Anderson–Darling test, the Shapiro–Wilk test, and the D'Agostino–Pearson K2 omnibus test.

Joon-Keat Lai

normality

Tests for Departure from Normality

is_normal function

<dl><dt>data</dt>
<dd>A data frame or a numeric vector.</dd>
<dt>formula</dt>
<dd>Formula (default: NULL).
If <code>data</code> is a data frame, define the val ~ group.</dd>
<dt>alpha</dt>
<dd>Significance threshold, range from 0 to 1 (default: 0.05).</dd>
<dt>sensitivity</dt>
<dd>Numeric, range from 1 to 3 (default: 2).
The greater the value, the greater chance to consider as non-normal.</dd>
<dt>summary</dt>
<dd>Logical (default: FALSE). If <code>TRUE</code>, show the summary table.</dd></dl>

Arguments

Normality test — is_normal

<dl>

<dt>data</dt>
<dd>A data frame or a numeric vector.</dd>


<dt>formula</dt>
<dd>Formula (default: NULL).
If <code>data</code> is a data frame, define the val ~ group.</dd>


<dt>alpha</dt>
<dd>Significance threshold, range from 0 to 1 (default: 0.05).</dd>


<dt>sensitivity</dt>
<dd>Numeric, range from 1 to 3 (default: 2).
The greater the value, the greater chance to consider as non-normal.</dd>


<dt>summary</dt>
<dd>Logical (default: FALSE). If <code>TRUE</code>, show the summary table.</dd>

</dl>

is_normal: Normality test

Description

Usage

Value

Arguments

Examples