The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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>, 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.
| Version: | 0.1.0 |
| Imports: | glmnet, randomForest, xgboost, lightgbm, e1071, ppclust |
| Suggests: | knitr, rmarkdown, MASS |
| Published: | 2026-02-13 |
| DOI: | 10.32614/CRAN.package.MFF (may not be active yet) |
| Author: | Nihat Tak [aut, cre], Sadık Çoban [aut] |
| Maintainer: | Nihat Tak <nihattak at gmail.com> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| CRAN checks: | MFF results |
| Reference manual: | MFF.html , MFF.pdf |
| Package source: | MFF_0.1.0.tar.gz |
| Windows binaries: | r-devel: MFF_0.1.0.zip, r-release: not available, r-oldrel: not available |
| macOS binaries: | r-release (arm64): MFF_0.1.0.tgz, r-oldrel (arm64): MFF_0.1.0.tgz, r-release (x86_64): not available, r-oldrel (x86_64): not available |
Please use the canonical form https://CRAN.R-project.org/package=MFF to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.