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.

Version:	0.1.1
Depends:	R (≥ 3.5.0)
Imports:	stats, dplyr, tidyr, ggplot2, MASS
Suggests:	testthat (≥ 3.0.0)
Published:	2026-09-09
DOI:	10.32614/CRAN.package.FuzzyLogit (may not be active yet)
Author:	Md Faruk Hasan [aut, cre], Azizur Rahman [aut]
Maintainer:	Md Faruk Hasan <md_faruk_hasan at sfu.ca>
License:	GPL-3
NeedsCompilation:	no
CRAN checks:	FuzzyLogit results

Documentation:

Reference manual:

FuzzyLogit.html , FuzzyLogit.pdf

Downloads:

Package source:	FuzzyLogit_0.1.1.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): FuzzyLogit_0.1.1.tgz, r-release (x86_64): FuzzyLogit_0.1.1.tgz, r-oldrel (x86_64): FuzzyLogit_0.1.1.tgz

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