Provides tools for evaluating the trustworthiness of machine learning models in production and research settings. Computes a Stability Index that quantifies the consistency of model predictions across multiple runs or resamples, and a Robustness Score that measures model resilience under small input perturbations. Designed for data scientists, ML engineers, and researchers who need to monitor and ensure model reliability, reproducibility, and deployment readiness.
| Version: | 0.1.0 |
| Imports: | stats |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: | 2026-02-20 |
| DOI: | 10.32614/CRAN.package.TrustworthyMLR |
| Author: | Ali Hamza [aut, cre] |
| Maintainer: | Ali Hamza <ahamza.msse25mcs at student.nust.edu.pk> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | TrustworthyMLR results |
| Reference manual: | TrustworthyMLR.html , TrustworthyMLR.pdf |
| Vignettes: |
Introduction to TrustworthyMLR (source, R code) |
| Package source: | TrustworthyMLR_0.1.0.tar.gz |
| Windows binaries: | r-devel: TrustworthyMLR_0.1.0.zip, r-release: TrustworthyMLR_0.1.0.zip, r-oldrel: TrustworthyMLR_0.1.0.zip |
| macOS binaries: | r-release (arm64): TrustworthyMLR_0.1.0.tgz, r-oldrel (arm64): TrustworthyMLR_0.1.0.tgz, r-release (x86_64): TrustworthyMLR_0.1.0.tgz, r-oldrel (x86_64): TrustworthyMLR_0.1.0.tgz |
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