accuracylevel: Robust Accuracy-Level Metrics for Predictive Model Evaluation
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>.
| Version: |
0.1.0 |
| 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 |
| Published: |
2026-06-18 |
| DOI: |
10.32614/CRAN.package.accuracylevel (may not be active yet) |
| Author: |
Achmad Syahrul Choir [cre, aut],
Mety Agustini [aut],
Kartika Fithriasari [aut],
Dedy Dwi Prastyo [aut] |
| Maintainer: |
Achmad Syahrul Choir <madsyair at stis.ac.id> |
| BugReports: |
https://github.com/madsyair/accuracylevel/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/madsyair/accuracylevel |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
accuracylevel results |
Documentation:
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