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booklet: Multivariate Exploratory Data Analysis

Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) when variables are categorical, Multiple Factor Analysis (MFA) when variables are structured in groups.

Version:1.0.0
Depends:R (≥ 4.1.0)
Suggests:covr,devtools,factoextra,FactoMineR,knitr,renv,testthat
Published:2025-04-24
DOI:10.32614/CRAN.package.booklet
Author:Alex Yahiaoui MartinezORCID iD [aut, cre]
Maintainer:Alex Yahiaoui Martinez <yahiaoui-martinez.alex at outlook.com>
BugReports:https://github.com/alexym1/booklet/issues
License:MIT + fileLICENSE
URL:https://github.com/alexym1/booklet,https://alexym1.github.io/booklet/
NeedsCompilation:no
Materials:README
CRAN checks:booklet results

Documentation:

Reference manual:booklet.html ,booklet.pdf
Vignettes:Comparison with FactoMineR (source,R code)
Introduction to booklet (source,R code)
Data visualization with factoextra (source,R code)

Downloads:

Package source: booklet_1.0.0.tar.gz
Windows binaries: r-devel:booklet_1.0.0.zip, r-release:booklet_1.0.0.zip, r-oldrel:booklet_1.0.0.zip
macOS binaries: r-release (arm64):booklet_1.0.0.tgz, r-oldrel (arm64):booklet_1.0.0.tgz, r-release (x86_64):booklet_1.0.0.tgz, r-oldrel (x86_64):booklet_1.0.0.tgz

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=bookletto link to this page.


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