{ "_id": "6a1d365f1d7bb097a0a3d10a", "Package": "MFF", "Type": "Package", "Title": "Meta Fuzzy Functions", "Version": "0.2.0", "Authors@R": "c(\nperson(\n\"Nihat\", \"Tak\",\nemail = \"nihattak@gmail.com\",\nrole = c(\"aut\", \"cre\")\n),\nperson(\n\"Sadık\", \"Çoban\",\nemail = \"sadikc716@gmail.com\",\nrole = \"aut\"\n)\n)", "Description": "Implements Meta Fuzzy Functions (MFFs) for regression Tak\nand Ucan (2026) by aggregating\npredictions from multiple base learners using membership\nweights learned in the prediction space of validation set. The\npackage supports fuzzy and crisp meta-ensemble structures via\nFuzzy C-Means (FCM) Tak (2018)\n, Possibilistic FCM (PFCM) Tak\n(2021) , Gustafson–Kessel (GK)\nclustering, and k-means, and provides a workflow to (i)\ngenerate validation/test prediction matrices from common\nregression learners (linear and penalized regression via\n'glmnet', random forests, gradient boosting with 'xgboost' and\n'lightgbm'), (ii) fit cluster-wise meta fuzzy functions and\ncompute membership-based weights, (iii) tune clustering-related\nhyperparameters (number of clusters/functions, fuzziness\nexponent, possibilistic regularization) via grid search on\nvalidation loss, and (iv) predict on new/test prediction\nmatrices and evaluate performance using standard regression\nmetrics (MAE, RMSE, MAPE, SMAPE, MSE, MedAE). This enables\nflexible, interpretable ensemble regression where different\nbase models contribute to different meta components according\nto learned memberships.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "Config/pak/sysreqs": "make", "Repository": "https://nihattak.r-universe.dev", "Date/Publication": "2026-03-31 06:43:31 UTC", "RemoteUrl": "https://github.com/nihattak/mff", "RemoteRef": "HEAD", "RemoteSha": "387e892a7b9cda496724ceacb17672465716060c", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-01 07:20:21 UTC", "User": "root" }, "Author": "Nihat Tak [aut, cre],\nSadık Çoban [aut]", "Maintainer": "Nihat Tak ", "MD5sum": "e593f81ae8482ea2e5f17049ecd950ab", "_user": "nihattak", "_type": "src", "_file": "MFF_0.2.0.tar.gz", "_fileid": "240ea97ed04fb08a5de3d0643866772e73ca30400eef4fe9af1816d1bd2702bc", "_filesize": 233061, "_sha256": "240ea97ed04fb08a5de3d0643866772e73ca30400eef4fe9af1816d1bd2702bc", "_created": "2026-06-01T07:20:21.000Z", "_published": "2026-06-01T07:35:59.389Z", "_distro": "noble", "_jobs": [ { "job": 78804541204, "time": 142, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7325576082" }, { "job": 78804541261, "time": 144, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7325576173" }, { "job": 78804541295, "time": 204, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7325668194" }, { "job": 78804541258, "time": 135, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7325779440" }, { "job": 78804068968, "time": 218, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7325537678" }, { "job": 78804541411, "time": 102, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7325566066" }, { "job": 78804541288, "time": 136, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7325575092" }, { "job": 78804541263, "time": 121, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7325571168" }, { "job": 78804541217, "time": 95, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7325563958" } ], "_buildurl": "https://github.com/r-universe/nihattak/actions/runs/26740778201", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/nihattak/mff", "_commit": { "id": "387e892a7b9cda496724ceacb17672465716060c", "author": "nihattak ", "committer": "nihattak ", "message": "New Features and Improvements\nNew Functionality: Added the boot.train function to the package core.\nClustering Enhancements: Integrated GK (Gustafson-Kessel) clustering into the MFF clustering stage to provide more flexible cluster shapes.\nParallel Computing: Added parallel processing support to tune.mff and boot.train functions using the doParallel and foreach frameworks. This allows users to significantly reduce computation time on multi-core systems.\nDocumentation and Acknowledgements\nMissing Acknowledgements: Added a formal acknowledgement to TÜBİTAK (The Scientific and Technological Research Council of Turkey), which was inadvertently omitted in the initial version.\nManual Updates: The package manual and function documentation have been thoroughly updated to reflect the new functions, clustering options, and parallelization parameters.\n", "time": 1774939411 }, "_maintainer": { "name": "Nihat Tak", "email": "nihattak@gmail.com", "login": "nihattak", "description": "", "uuid": 27814577 }, "_registered": true, "_dependencies": [ { "package": "glmnet", "role": "Imports" }, { "package": "randomForest", "role": "Imports" }, { "package": "xgboost", "role": "Imports" }, { "package": "lightgbm", "role": "Imports" }, { "package": "e1071", "role": "Imports" }, { "package": "ppclust", "role": "Imports" }, { "package": "doParallel", "role": "Imports" }, { 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