A graphical user interface for testing normality visually
The NormalityAssessment R package includes an interactive Shiny application, which is run locally on the user’s machine. It enables the creation of normal quantile-quantile (QQ) plots and histograms for assessing normality. The methods implemented are based on recent developments 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 on these two procedures can be found in the articles included in the References section below.
The NormalityAssessment package can be installed from either CRAN or GitHub.
To install from CRAN, run the following code in R:
To install the package from GitHub, run the following code in R:
install.packages("remotes") # installs the remotes package for accessing the install_github() function
remotes::install_github("ccasement/NormalityAssessment") # installs the NormalityAssessment packageThe NormalityAssessment application can be run using a single line of code in R:
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.
If you happen to find any bugs, we kindly ask that you email us at casementc@gmail.com.
NormalityAssessment is distributed under the MIT license. For details, see the LICENSE.md file.