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accuracylevel  

Robust Accuracy-Level Metrics for Predictive Model Evaluation
View on CRAN: Click here


Download and install accuracylevel package within the R console
Install from CRAN:
install.packages("accuracylevel")

Install from Github:
library("remotes")
install_github("cran/accuracylevel")

Install by package version:
library("remotes")
install_version("accuracylevel", "0.1.0")



Attach the package and use:
library("accuracylevel")
Maintained by
Achmad Syahrul Choir
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-06-18
Latest Update: 2026-06-18
Description:
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>.
How to cite:
Achmad Syahrul Choir (2026). accuracylevel: Robust Accuracy-Level Metrics for Predictive Model Evaluation. R package version 0.1.0, https://cran.r-project.org/web/packages/accuracylevel. Accessed 07 Oct. 2026.
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