T1FF: Type-1 Fuzzy Functions for Classification, Regression, and Forecasting

Fits Type-1 Fuzzy Function models for binary classification, numeric regression, and time-series forecasting with user-supplied temporal predictors. The package combines fuzzy C-means memberships, nonlinear membership transformations, cluster-specific linear or support vector machine models, and membership-weighted predictions. It also provides model evaluation, validation, K-fold and stratified K-fold tuning, and repeated nested cross-validation with task-appropriate metrics. The regression workflow can be used for forecasting when temporal dependence is represented by lagged or seasonal predictors and assessment partitions preserve chronological order.

Version:	0.1.0
Depends:	R (≥ 4.1.0)
Imports:	e1071, kernlab, stats, utils
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
Published:	2026-09-15
DOI:	10.32614/CRAN.package.T1FF (may not be active yet)
Author:	Nihat Tak [aut, cre]
Maintainer:	Nihat Tak <nihattak at gmail.com>
License:	MIT + file LICENSE
NeedsCompilation:	no
Materials:	README, NEWS
CRAN checks:	T1FF results

Documentation:

Reference manual:	T1FF.html , T1FF.pdf
Vignettes:	T1FF: Classification, Regression, Forecasting, Tuning, and Nested Validation (source, R code)

Downloads:

Package source:	T1FF_0.1.0.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): T1FF_0.1.0.tgz, r-release (x86_64): T1FF_0.1.0.tgz, r-oldrel (x86_64): T1FF_0.1.0.tgz

Linking:

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