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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