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MFF

0.2.0

Meta Fuzzy Functions

0packages depend
2.2Kdownloads / year
test coverage
13/13checks pass

Overview

About
Maintained by Nihat TakFirst published 2026-02-132 releasesCRAN page ↗

Implements Meta Fuzzy Functions (MFFs) for regression Tak and Ucan (2026) doi:10.1016/j.asoc.2026.114592 by aggregating predictions from multiple base learners using membership weights learned in the prediction space of validation set. The package supports fuzzy and crisp meta-ensemble structures via Fuzzy C-Means (FCM) Tak (2018) doi:10.1016/j.asoc.2018.08.009, Possibilistic FCM (PFCM) Tak (2021) doi:10.1016/j.ins.2021.01.024, Gustafson–Kessel (GK) clustering, and k-means, and provides a workflow to (i) generate validation/test prediction matrices from common regression learners (linear and penalized regression via 'glmnet', random forests, gradient boosting with 'xgboost' and 'lightgbm'), (ii) fit cluster-wise meta fuzzy functions and compute membership-based weights, (iii) tune clustering-related hyperparameters (number of clusters/functions, fuzziness exponent, possibilistic regularization) via grid search on validation loss, and (iv) predict on new/test prediction matrices and evaluate performance using standard regression metrics (MAE, RMSE, MAPE, SMAPE, MSE, MedAE). This enables flexible, interpretable ensemble regression where different base models contribute to different meta components according to learned memberships.

Install

Health

CRAN checks
13OK
Slowest check: 1.9 min · r-oldrel-windows-x86_64
Code health
None
Tests · ratio 0.00
not tracked
Coverage
100%
Documentation · exports
9
Dependencies · direct
Check history
  • OK2026-08-05
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • NOTE2026-08-01
    12 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • OK2026-03-10
    14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

Documentation
READMENoVignettesNopkgdown siteNoNEWSNoCode of conductNoContributing guideNo
Examples that run
100%
Documented parameters
99%
Return-value docs
100%
References docs
43%

Downloads

2.2K
CRAN downloads in the past year
Rank #10,845 · ~6/day · ~186/mo
Daily download trend is not available in this view yet.
15830 days
90090 days
2.2K1 year
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Also on46 r2u9 autocran

Dependencies

Declared dependencies
10 external dependencies (excludes base and recommended)
Depends (0)
none
LinkingTo (0)
none
Suggests (3)
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Code & Tests

People & History

People (2)
Maintainer (1)
Author, Maintainer
Authors (2)
Author, Maintainer
Package Timeline

2 releases. Pick two to compare their code metrics. R releases are shown for context.

  • R
    R 4.6.0 released · 2026-04-24
  • 0.2.0Latest
    2026-04-02 · current release · diff ↗
  • 0.1.0
    2026-03-10
  • R
    R 4.5.0 released · 2025-04-11

Package metadata

First published
2026-02-13
Total releases
2 / 1 yrs
License
MIT + file LICENSE OSI
Download size
21 KB
Installed size
not tracked yet
With dependencies
not tracked yet

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("MFF")
Tak, N., & Çoban, S. (2026). MFF: Meta Fuzzy Functions (Version 0.2.0) [Computer software]. https://doi.org/10.32614/CRAN.package.MFF

This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for MFF version 0.2.0 [Data set]. HJJB, LLC. Data release v2026-08-18. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-18, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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