Keywords: Error Datacleaning
Webpages:
https://CRAN.R-project.org/package=errorlocate,
https://github.com/data-cleaning An important but undermentioned activity needed for statistical analysis is data-cleaning. No measurement is perfect, so data often contain errors. Obvious errors e.g. negative age are easily detected, but observations that contain variables that are logically related e.g. marital status and age are more tricky. R package errorlocate allows for pin pointing errors in observations using the Feligi-Holt algorithm and validation rules from R package validate. The errors can automatically be removed using a pipe-line syntax.
