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Tests for Kaiser-Meyer-Olkin (KMO) and communalities in a dataset. It provides a final sample by removing variables in a iterable manner while keeping account of the variables that were removed in each step. It follows the best practices and assumptions according to Hair, Black, Babin & Anderson (2018, ISBN:9781473756540).
Version: | 2.0.1 |
Depends: | R (≥ 3.6.0), MASS, psych |
Suggests: | knitr, rmarkdown, testthat (≥ 2.1.0) |
Published: | 2022-03-08 |
DOI: | 10.32614/CRAN.package.FactorAssumptions |
Author: | Jose Storopoli [aut, cre] |
Maintainer: | Jose Storopoli <jstoropoli at protonmail.com> |
BugReports: | https://github.com/storopoli/FactorAssumptions/issues |
License: | GPL-3 |
URL: | https://github.com/storopoli/FactorAssumptions |
NeedsCompilation: | no |
Materials: | README NEWS |
CRAN checks: | FactorAssumptions results |
Reference manual: | FactorAssumptions.pdf |
Vignettes: |
How to use FactorAssumptions |
Package source: | FactorAssumptions_2.0.1.tar.gz |
Windows binaries: | r-devel: FactorAssumptions_2.0.1.zip, r-release: FactorAssumptions_2.0.1.zip, r-oldrel: FactorAssumptions_2.0.1.zip |
macOS binaries: | r-release (arm64): FactorAssumptions_2.0.1.tgz, r-oldrel (arm64): FactorAssumptions_2.0.1.tgz, r-release (x86_64): FactorAssumptions_2.0.1.tgz, r-oldrel (x86_64): FactorAssumptions_2.0.1.tgz |
Old sources: | FactorAssumptions archive |
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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.