Description I. Define edits II. Check and find errors in data IV. Manipulate and check edits V. Plot and coerce edits

The `editrules`

package aims to provide an environment to conveniently
define, read and check recordwise data constraints including

Linear (in)equality constraints for numerical data,

Constraints on value combinations of categorical data

Conditional constraints on numerical and/or mixed data

In literature these constraints, or restrictions are refered to as “edits”.
`editrules`

can perform common rule
set manipulations like variable elimination and value substitution, and
offers error localization functionality based on the
(generalized) paradigm of Fellegi and Holt. Under this paradigm, one determines
the smallest (weighted) number of variables to adapt such that no (additional or derived)
rules are violated. The paradigm is based on the assumption that errors
are distributed randomly over the variables and there is no detectable cause of
error. It also decouples the detection of corrupt variables from their
correction. For some types of error, such as sign flips, typing errors or
rounding errors, this assumption does not hold. These errors can be detected
and are closely related to their resolution. The reader is referred to the
deducorrect package for treating such errors.

`editrules`

provides several methods for creating edits from a `character`

, `expression`

, `data.frame`

or a text file.

`editfile` | Read conditional numerical, numerical and categorical constraints from textfile |

`editset` | Create conditional numerical, numerical and categorical constraints |

`editmatrix` | Create a linear constraint matrix for numerical data |

`editarray` | Create value combination constraints for categorical data |

`editrules`

provides several method for checking `data.frame`

s with edits

`violatedEdits` | Find out which record violates which edit. |

`localizeErrors` | Localize erroneous fields using Fellegi and Holt's principle. |

`errorLocalizer` | Low-level error localization function using B&B algorithm |

Note that you can call `plot`

, `summary`

and `print`

on results of these functions.

`editrules`

provides several methods for manipulating edits

`substValue` | Substitute a value in a set of rules |

`eliminate` | Derive implied rules by variable elimination |

`reduce` | Remove unconstraint variables |

`isFeasible` | Check for contradictions |

`duplicated` | Find duplicated rules |

`blocks` | Decompose rules into independent blocks |

`disjunct` | Decouple conditional edits into disjunct edit sets |

`separate` | Decompose rules in blocks and decouple conditinal edits |

`generateEdits` | Generate all nonredundant implicit edits (`editarray` only) |

`editrules`

provides several methods for plotting and coercion.

`editrules.plotting` | Plot edit-variable connectivity graph |

`as.igraph` | Coerce to edit-variable connectivity `igraph` object |

`as.character` | Coerce edits to `character` representation |

`as.data.frame` | Store `character` representation in `data.frame` |

editrules documentation built on July 2, 2018, 1 a.m.

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