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deducorrect (version 1.3.7)
Deductive Correction, Deductive Imputation, and Deterministic Correction
Description
A collection of methods for automated data cleaning where all actions are logged.
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Install
install.packages('deducorrect')
Monthly Downloads
455
Version
1.3.7
License
GPL-3
Issues
0
Pull Requests
0
Stars
8
Forks
1
Repository
https://github.com/data-cleaning/deducorrect
Maintainer
Mark der Loo
Last Published
July 15th, 2015
Functions in deducorrect (1.3.7)
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deductiveLevels
Derive imputation values for categorical data
correctTypos
Correct records under linear restrictions using typographical error suggestions
deductiveZeros
Find out which variables can deductively be imputed with 0
status
Create empty status vector
getUsername
Get name of R user.
correctionRules
Rules for deterministic correction
newdeducorrect
Generate an S3
deducorrect
object
imputess
Impute values from solution space
solSpace
Solution space for missing values under equality constraints
raghavachari
Determine if a matrix is unimodular using recursive Raghavachari criterium
damerauLevenshteinDistance
Calculate the Damerau Levenshtein Distance between two strings
scapegoat
Scapegoat algorithm
hellerTompkins
Determine if a matrix is totally unimodular using Heller and Tompkins criterium.
deducorrect-package
Deductive correction methods for sign, rounding and typing errors
deducorrect-object
deducorrect object
reduceMatrix
Apply reduction method from Scholtus (2008)
isTotallyUnimodular
Test for total unimodularity of a matrix.
getSignCorrection
workhorse for correctSigns
allTotallyUnimodular
Test if a list of matrices are all unimodular
applyFix
Apply flips and swaps to a record.
deduImpute
Deductive imputation of numerical or categorical values
correctRounding
Correct records under linear restrictions for rounding errors
correctWithRules
Deterministic correction
getTypoCorrection
Check record validity and suggest typo corrections
correctSigns
Correct sign errors and value interchanges in data records
tree
Solve an optimization problem using a tree algorithm as described in Scholtus (2009)