{ "_id": "6a3449493efcd9bda43c1e6f", "Package": "accuracylevel", "Title": "Robust Accuracy-Level Metrics for Predictive Model Evaluation", "Version": "0.1.0", "Authors@R": "c(\nperson(\"Achmad Syahrul\", \"Choir\", email = \"madsyair@stis.ac.id\",\nrole = c(\"cre\", \"aut\")),\nperson(\"Mety\", \"Agustini\", email = \"mety.assahid@bps.go.id\",\nrole = \"aut\"),\nperson(\"Kartika\", \"Fithriasari\", email = \"kartika_f@statistika.its.ac.id\",\nrole = \"aut\"),\nperson(\"Dedy Dwi\", \"Prastyo\", email = \"dedy-dp@statistika.its.ac.id\",\nrole = \"aut\"))", "Author": "Achmad Syahrul Choir [cre, aut], Mety Agustini [aut], Kartika\nFithriasari [aut], Dedy Dwi Prastyo [aut]", "Maintainer": "Achmad Syahrul Choir ", "Description": "Implements novel accuracy-level metrics for evaluating\ncontinuous data prediction models. Four metrics are provided:\nCounted Squared Error (CSE), Counted Absolute Error (CAE),\nCounted Absolute Percentage Error (CAPE), and Symmetric Counted\nAbsolute Percentage Error (SCAPE). These metrics offer robust,\nconsistent, and interpretable evaluation on a 0-100% scale,\naddressing limitations of conventional metrics like RMSE, MAE,\nand MAPE. The package integrates with 'caret', 'tidymodels',\nand common forecasting frameworks. 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