Shiny Apps for Automated Data Analysis and Automated Interpretation

Shiny apps for automated data analysis, annotated outputs and human-readable interpretation in natural language. Designed especially for learners and applied researchers. Currently available methods: EDA, EDA with Python, Correlation Analysis, Principal Components Analysis, Confirmatory Factor Analysis.


Reference manual

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1.0 by Denise Welsch, a month ago

Browse source code at

Authors: Denise Welsch [aut, cre] , Berit Hunsdieck [ctb] , Omar Alhelal [ctb]

Documentation:   PDF Manual  

AGPL license

Imports shiny, rmarkdown, data.table, readr, shinydisconnect, knitr, kableExtra, car, DDoutlier, energy, corrplot, ggplot2, gridExtra, reshape2

Suggests MASS, boot, nortest, lmtest, DescTools, psych, Hmisc, PerformanceAnalytics, reticulate, fastDummies, semTools, semPlot, FactoMineR, FactoInvestigate, factoextra, rrcov, methods, parallel, graphics, imputeMissings, onewaytests

System requirements: For all functions resp. apps: pandoc, LaTeX. For the edapy() function resp. Statsomat/EDAPY app: Python (>=3).

See at CRAN