Package: FuzzyImputationTest 0.2.0

FuzzyImputationTest: Imputation Procedures and Quality Tests for Fuzzy Data

Special procedures for the imputation of missing fuzzy numbers are still underdeveloped. The goal of the package is to provide the new d-imputation method (DIMP for short, Romaniuk, M. and Grzegorzewski, P. "Fuzzy Data Imputation with DIMP and FGAIN" RB/23/2023 (2023)) and covert some classical ones applied in R packages ('missForest','miceRanger','knn') for use with fuzzy datasets. Additionally, specially tailored benchmarking tests are provided to check and compare these imputation procedures with fuzzy datasets.

Authors:Maciej Romaniuk [cre, aut]

FuzzyImputationTest_0.2.0.tar.gz
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FuzzyImputationTest.pdf |FuzzyImputationTest.html✨
FuzzyImputationTest/json (API)

Peer review:

Bug tracker:https://github.com/mroman-ibs/fuzzyimputationtest/issues

On CRAN:

15 exports 1.24 score 118 dependencies 2 scripts

Last updated 14 days agofrom:3b50fa6ec4. Checks:OK: 7. Indexed: yes.

Exports:ApplyStatisticalTestsCalculateFuzzyMeasuresErrorMatrixFuzzifyMatrixFuzzyImputationFuzzyNumbersToMatrixImputationDimpImputationTestsIntroducingNAMatrixToFuzzyNumbersMeasureAHDMeasureEuclideanMeasureHSDRemoveNotFuzzyStatisticalMeasures

Dependencies:abindaskpassbackportsbootbroomcarcarDatacellrangerclassclicodetoolscolorspacecorrplotcowplotcpp11crayoncurldata.tableDEoptimRDerivDescToolsdigestdoBydoRNGdplyre1071ExactexpmfansifarverFNNforeachFuzzyNumbersFuzzySimResgenericsggplot2ggpubrggrepelggsciggsignifgldglmnetgluegridExtragtablehmshttrisobanditeratorsitertoolsjsonlitelabelinglaekenlatticelifecyclelme4lmomlmtestmagrittrMASSMatrixMatrixModelsmgcvmiceRangermicrobenchmarkmimeminqamissForestmodelrmunsellmvtnormnlmenloptrnnetnumDerivopensslpalassopbkrtestpillarpkgconfigpolynomprettyunitsprogressproxypurrrquantregR6randomForestrangerRColorBrewerRcppRcppEigenreadxlrematchrlangrngtoolsrobustbaserootSolverstatixrstudioapiscalesshapespSparseMstringistringrsurvivalsystibbletidyrtidyselectutf8vcdvctrsVIMviridisLitewithrzoo