Tests for Departure from Normality

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.

normality

An R package as a toolkit of statistical approaches for assessing data normality.

There are also other nice alternatives such as nortest, moments, cmstatr, and other friends.

Installation

You can install the package from CRAN with:

install.packages("normality")

or the development version from GitHub with:

if (!require("pak")) install.packages("pak")
pak::pak("P10911004-NPUST/normality")

Quick start

is_normal(rnorm(20), summary = TRUE)

TODO

Implement normality tests based on:

1. Chi-Square type (may not implemented)

2. Empirical distribution function (EDF):

• [ ] Kolmogorov_Smirnov_test()
• [ ] Kuiper_test()
• [x] Anderson_Darling_test()
• [x] Cramer_von_Mises_test()
• [x] Lilliefors_test()

3. Moments:

• [x] skewness()
• [x] kurtosis()
• [x] Jarque_Bera_test()
• [x] D.Agostino_Pearson_test()

4. Regression and correlation:

• [x] Shapiro_Wilk_test() with 3 alternatives:
  • Shapiro-Wilk
  • Shapiro-Francia
  • Shapiro-Wilk-Royston
• [ ] Ryan_Joiner_test() pdf

Graphical analysis:

• [ ] Q-Q plot
• [ ] P-P plot

Miscellaneous:

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