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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 Martinez ORCID iD [aut, cre]
Maintainer: Alex Yahiaoui Martinez <yahiaoui-martinez.alex at outlook.com>
BugReports: https://github.com/alexym1/booklet/issues
License: MIT + file LICENSE
URL: https://github.com/alexym1/booklet, https://alexym1.github.io/booklet/
NeedsCompilation: no
Materials: README
CRAN checks: booklet results

Documentation:

Reference manual: 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: not available, r-release: not available, r-oldrel: not available
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 form https://CRAN.R-project.org/package=booklet to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.