R/errorlocate-package.r

In data-cleaning/errorlocate: Locate Errors with Validation Rules

#' Find errors in data given a set of validation rules.
#'
#' Find errors in data given a set of validation rules.
#' The `errorlocate` helps to identify obvious errors in raw datasets.
#'
#' It works in tandem with the package [validate()].
#' With `validate` you formulate data validation rules to which the data must comply.
#' For example:
#'
#' "age cannot be negative": `age >= 0`
#'
#' While `validate` can identify if a record is valid or not, it does not identify
#' which of the variables are responsible for the invalidation. This may seem a simple task,
#' but is actually quite tricky:  a set of validation rules form a web
#' of dependent variables: changing the value of an invalid record to repair for rule 1, may invalidate
#' the record for rule 2.
#'
#' Errorlocate provides a small framework for record based error detection and implements the Felligi Holt
#' algorithm. This algorithm assumes there is no other information available then the values of a record
#' and a set of validation rules. The algorithm minimizes the (weighted) number of values that need
#' to be adjusted to remove the invalidation.
#'
#' The `errorlocate` package translates the validation and error localization problem into
#' a mixed integer problem and uses a mip solver to find a solution.
#' @name errorlocate-package
#' @import methods validate
#' @importFrom stats runif setNames
#' @docType package
#' @references
#'  T. De Waal (2003) Processing of Erroneous and Unsafe Data. PhD thesis, University of Rotterdam.
#'
#'  Van der Loo, M., de Jonge, E, Data Cleaning With Applications in R
#'
#'  E. De Jonge and Van der Loo, M. (2012) Error localization as a mixed-integer program in
#'  editrules.
#'
#'  lp_solve and Kjell Konis. (2011). lpSolveAPI: R Interface for
#'  lp_solve version 5.5.2.0. R package version 5.5.2.0-5.
#'  http://CRAN.R-project.org/package=lpSolveAPI
"_PACKAGE"