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. (2023) "Fuzzy Data Imputation with DIMP and FGAIN" RB/23/2023) 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.
| Version: | 0.5.2 | 
| Depends: | R (≥ 3.5.0) | 
| Imports: | stats, methods, FuzzySimRes, FuzzyNumbers, missForest, miceRanger, VIM, utils, FuzzyResampling, mice | 
| Suggests: | testthat (≥ 3.0.0) | 
| Published: | 2025-10-29 | 
| DOI: | 10.32614/CRAN.package.FuzzyImputationTest | 
| Author: | Maciej Romaniuk  [cre, aut] | 
| Maintainer: | Maciej Romaniuk  <mroman at ibspan.waw.pl> | 
| License: | GPL-3 | 
| NeedsCompilation: | no | 
| Materials: | README | 
| CRAN checks: | FuzzyImputationTest results | 
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