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 |
| 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 |
| macOS binaries: | r-release: cleanerR_0.1.1.tgz, r-oldrel: cleanerR_0.1.1.tgz |
| Old sources: | cleanerR archive |
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