Parsing, Applying, and Manipulating Data Cleaning Rules
Add dummy variable to the data.frames, these are needed for errorlocat...
Derive adjecency matrix from collection of edits
Coerce an cateditmatrix to a character vector
Coerce a matrix to an edit matrix.
Coerce x to an editset
Coerces a mip object into an lpsolve object
Write an editset into a mip representation
Transform a found solution into a categorical record
Backtracker: a flexible and generic binary search program
Decompose a matrix or edits into independent blocks
Create an editmatrix with categorical variables
Check data against a datamodel
Get condition matrix from an editset.
Determine if a boolean matrix contains var
Determine which edits contain which variable(s)
Summarize data model of an editarray in a data.frame
Decouple a set of conditional edits
Check for duplicate edit rules
Check for duplicate edit rules
Bring an (edit) matrix to reduced row echelon form.
Parse textual, categorical edit rules to an editarray
Read edits edits from free-form textfile
Create an editmatrix
Names of edits
An overview of the function of package editrules
Graphical representation of edits
Read general edits
Determine edittypes in editset based on 'contains(E)'
Eliminate a variable from a set of edit rules
Localize errors using a MIP approach.
Create a backtracker object for error localization
The errorLocation object
Expand an edit expression
Field code forest algorithm
Derive all essentially new implicit edits
Returns the coefficient matrix A of linear (in)equalities
Returns augmented matrix representation of edit set.
Get named logical array from editarray
Returns the constant part b of a linear (in)equality
Returns the derivation history of an edit matrix or array
get index list from editmatrix
retrieve level names from editarray
retrieve edit names from editarray
Returns the operator part of a linear (in)equality editmatrix E
get seprator used to seperate variables from levels in editarray
Get upperbounds of edits, given the boundaries of all variables
Returns the variable names of an (in)equality editmatrix E
get variable names in editarray
get variable names
Returns the variable names of an (in)equality editmatrix E
get names of variables in a set of edits
Retrieve values stricktly implied by rules
Derive textual representation from (partial) indices
Compute index from array part of editarray
Check object class
Check consistency of set of edits
Check if an editmatrix is normalized
Check for obvious contradictions in a set of edits
Find obvious redundancies in set of edits
Check which edits are dominated by other ones.
Workhorse function for localizeErrors
Localize errors on records in a data.frame.
Number of edits Count the number of edits in a collection of edits.
editarray: logical array where every column corresponds to one level o...
Create an editmatrix object from its constituing attributes.
Generate new errorlocation object
Normalizes an editmatrix
Parse a categorical edit expression
parse categorial edit
Parse a character vector of edits
Parse a mixed edit
Parse a numerical edit expression
print a backtracker
print cateditmatrix
print editarray
print editset
print editmatrix
print editset
summary
Print object of class errorLocation
summary
Print violatedEdits
Remove redundant variables and edits.
Remove redundant dummy variables
Separate an editset into its disconnected blocks and simplify
Simplify logical mixed edits in an editset
Derive editmatrix with soft constraints. This is a utility function th...
Derive editmatrix with soft constraints based on boundaries of variabl...
Derive editmatrix with soft constraints based on boundaries of variabl...
Derive editmatrix with soft constraints based on boundaries of variabl...
Row index operator for editmatrix
Replace a variable by a value in a set of edits.
Check data against constraints
Rewrite an editset and reported values into the components needed for ...
Please note: active development has moved to packages 'validate' and 'errorlocate'. Facilitates reading and manipulating (multivariate) data restrictions (edit rules) on numerical and categorical data. Rules can be defined with common R syntax and parsed to an internal (matrix-like format). Rules can be manipulated with variable elimination and value substitution methods, allowing for feasibility checks and more. Data can be tested against the rules and erroneous fields can be found based on Fellegi and Holt's generalized principle. Rules dependencies can be visualized with using the 'igraph' package.
Useful links