JBGTest

Performs the Jarque-Bera test for normality with optional corrections proposed by Glinskiy et al. (2024),
depending on whether the mean, variance, or both are known a priori.

Provides a comprehensive set of tools for descriptive statistics,
graphical data exploration, outlier detection, homoscedasticity testing, and
multiple comparison procedures. Includes manual implementations of Levene's test,
Bartlett's test, and the Fligner-Killeen test, as well as post hoc comparison
methods such as Tukey, Scheffé, Games-Howell, Brunner-Munzel, and others.
This version introduces two new procedures: the Jonckheere-Terpstra trend test
and the Jarque-Bera test with Glinskiy's (2024) correction. Designed for use in
teaching, applied statistical analysis, and reproducible research.
Additionally you can find a post hoc Test Planner, which helps you to make a
decision on which procedure is most suitable.

Carlos Jiménez-Gallardo

Analitica

Exploratory Data Analysis, Group Comparison Tools, and Other
Procedures

JBGTest function

<dl><dt>y</dt>
<dd>A numeric vector to test for normality.</dd>
<dt>mu</dt>
<dd>Optional known mean value. Default is <code>NULL</code>.</dd>
<dt>sigma2</dt>
<dd>Optional known variance value. Default is <code>NULL</code>.</dd>
<dt>alpha</dt>
<dd>Significance level for the test (default is 0.05).</dd></dl>

Arguments

Jarque-Bera Test with Glinskiy Corrections — JBGTest

<dl>

<dt>y</dt>
<dd>A numeric vector to test for normality.</dd>


<dt>mu</dt>
<dd>Optional known mean value. Default is <code>NULL</code>.</dd>


<dt>sigma2</dt>
<dd>Optional known variance value. Default is <code>NULL</code>.</dd>


<dt>alpha</dt>
<dd>Significance level for the test (default is 0.05).</dd>

</dl>

JBGTest: Jarque-Bera Test with Glinskiy Corrections

Description

Usage

Value

Arguments

References

Examples