FuzzyLogit
Fuzzy Logistic Regression
Fits logistic regression models in which the binary response is represented by a triangular fuzzy number rather than an exact crisp label, allowing uncertainty in class membership to be encoded directly in the outcome. Model parameters are estimated using the Fuzzy Least Squares approach of Diamond (1988) <doi:10.1016/0020-0255(88)90047-3>, following the integrated fuzzy logistic regression method of Yapici Pehlivan and Sahin (2018) <https://dergipark.org.tr/en/pub/jssa/issue/37877/437725>. Provides fitting, prediction, classification, cross-validation, and diagnostic plotting methods, along with tools for comparing model behaviour across different assumed levels of label uncertainty.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.1 |
rolling linux/jammy R-4.5 | FuzzyLogit_0.1.1.tar.gz |
154.5 KiB |
0.1.1 |
rolling linux/noble R-4.5 | FuzzyLogit_0.1.1.tar.gz |
154.5 KiB |
0.1.1 |
rolling source/ R- | FuzzyLogit_0.1.1.tar.gz |
35.9 KiB |
0.1.1 |
latest linux/jammy R-4.5 | FuzzyLogit_0.1.1.tar.gz |
154.5 KiB |
0.1.1 |
latest linux/noble R-4.5 | FuzzyLogit_0.1.1.tar.gz |
154.5 KiB |
0.1.1 |
latest source/ R- | FuzzyLogit_0.1.1.tar.gz |
35.9 KiB |
0.1.1 |
2026-04-23 source/ R- | FuzzyLogit_0.1.1.tar.gz |
0 B |