Robust Accuracy-Level Metrics for Predictive Model Evaluation R package accuracylevel version 0.1.0 Implements novel accuracy-level metrics for evaluating continuous data prediction models. Four metrics are provided: Counted Squared Error (CSE), Counted Absolute Error (CAE), Counted Absolute Percentage Error (CAPE), and Symmetric Counted Absolute Percentage Error (SCAPE). These metrics offer robust, consistent, and interpretable evaluation on a 0-100% scale, addressing limitations of conventional metrics like RMSE, MAE, and MAPE. The package integrates with 'caret', 'tidymodels', and common forecasting frameworks. Based on Agustini, Fithriasari, and Prastyo (2026) <doi:10.1016/j.dajour.2025.100661>. Software Depends: R (>= 3.5.0) Imports: stats, graphics, utils Suggests: rlang (>= 0.4.0), caret, yardstick, forecast, testthat (>= 3.0.0), knitr, rmarkdown, ggplot2, tibble, dplyr Achmad Syahrul Choir <madsyair@stis.ac.id> Comprehensive R Archive Network (CRAN) Achmad Syahrul Choir [cre, aut], Mety Agustini [aut], Kartika Fithriasari [aut], Dedy Dwi Prastyo [aut] GPL-3 2026-06-18 application/tgz https://CRAN.R-project.org/package=accuracylevel doi:10.32614/CRAN.package.accuracylevel