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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | accuracylevel_0.1.0.tar.gz |
206.9 KiB |
0.1.0 |
rolling linux/noble R-4.5 | accuracylevel_0.1.0.tar.gz |
206.4 KiB |
0.1.0 |
rolling source/ R- | accuracylevel_0.1.0.tar.gz |
99.7 KiB |
0.1.0 |
latest linux/jammy R-4.5 | accuracylevel_0.1.0.tar.gz |
206.9 KiB |
0.1.0 |
latest linux/noble R-4.5 | accuracylevel_0.1.0.tar.gz |
206.4 KiB |
0.1.0 |
latest source/ R- | accuracylevel_0.1.0.tar.gz |
99.7 KiB |
0.1.0 |
2026-04-23 source/ R- | accuracylevel_0.1.0.tar.gz |
0 B |