Package 'NormalityAssessment'

Title: A Graphical User Interface for Testing Normality Visually
Description: Package including an interactive Shiny application for testing normality visually.
Authors: Christopher Casement [cre, aut], Laura McSweeney [aut]
Maintainer: Christopher Casement <[email protected]>
License: MIT + file LICENSE
Version: 0.1.0
Built: 2024-11-10 05:31:05 UTC
Source: https://github.com/ccasement/normalityassessment

Help Index

NormalityAssessment: A Graphical User Interface for Testing Normality Visually

Description

The NormalityAssessment package creates plots for assessing normality. The methods implemented are based on recent development made in graphical inference. In the app, the features in the 'Explore Simulated Data' tab enable the user to run the Rorschach procedure, and those in the 'Include Your Data' tab allow the user to run the line-up procedure.

Details

Package: NormalityAssessment
Type: Package
Version: 0.1.0
Date: 2022-11-04
Depends: R (>= 3.5.0)
Imports: dplyr, DT, ggplot2, rio, rmatio, shiny, shinyalert, shinyBS, stringi, stringr License: MIT
BugReports: https://github.com/ccasement/NormalityAssessment/issues
Encoding: UTF-8

Function

Author(s)

Christopher Casement
Department of Mathematics
Fairfield University
[email protected]

Laura McSweeney
Department of Mathematics
Fairfield University

See Also

Useful links:

Run the NormalityAssessment Shiny application

Description

Runs the NormalityAssessment Shiny application.

Usage

runNormalityAssessmentApp()

Value

There is no return value.

Author(s)

Christopher Casement
Department of Mathematics
Fairfield University
[email protected]

Laura McSweeney
Department of Mathematics
Fairfield University

References

Buja, A., Cook, D., Hofmann, H., Lawrence, M., Lee, E. K., Swayne, D. F., & Wickham, H. (2009). Statistical inference for exploratory data analysis and model diagnostics. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 367(1906), 4361-4383.

Majumder, M., Hofmann, H., & Cook, D. (2013). Validation of visual statistical inference, applied to linear models. Journal of the American Statistical Association, 108(503), 942-956.

Wickham, H., Cook, D., Hofmann, H., & Buja, A. (2010). Graphical inference for infovis. IEEE Transactions on Visualization and Computer Graphics, 16(6), 973-979.

Examples

## only run the app in an interactive R session
if (interactive()) {runNormalityAssessmentApp()}