cleanerR: How to Handle your Missing Data

How to deal with missing data?Based on the concept of almost functional dependencies, a method is proposed to fill missing data, as well as help you see what data is missing. The user can specify a measure of error and how many combinations he wish to test the dependencies against, the closer to the length of the dataset, the more precise. But the higher the number, the more time it will take for the process to finish. If the program cannot predict with the accuracy determined by the user it shall not fill the data, the user then can choose to increase the error or deal with the data another way.

Version: 0.1.1
Imports: plyr, data.table
Suggests: knitr, rmarkdown, testthat
Published: 2019-02-10
Author: Rafael Silva Pereira
Maintainer: Rafael Silva Pereira <r.s.p.models at gmail.com>
License: MIT + file LICENSE
NeedsCompilation: no
CRAN checks: cleanerR results

Downloads:

Reference manual: cleanerR.pdf
Vignettes: Vignette Title
Package source: cleanerR_0.1.1.tar.gz
Windows binaries: r-devel: cleanerR_0.1.1.zip, r-release: cleanerR_0.1.1.zip, r-oldrel: cleanerR_0.1.1.zip
OS X binaries: r-release: cleanerR_0.1.1.tgz, r-oldrel: cleanerR_0.1.1.tgz
Old sources: cleanerR archive

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