Provides metrics for quantifying the contribution of individual component models to the predictive accuracy of ensemble forecasts. The package implements the Leave-One-Model-Out (LOMO) and Leave-All-Subset-of-One-Model-Out (LASOMO) model importance metrics, enabling users to assess the relative importance of component models and better understand the performance of ensemble forecasting systems. Methods are described in Kim et al. (2026) <doi:10.1016/j.ijforecast.2025.12.006>.
| Version: | 0.1.0 |
| Depends: | R (≥ 4.1.0) |
| Imports: | hubUtils (≥ 0.4.0), dplyr (≥ 1.1.4), hubEvals (≥ 0.3.0), hubEnsembles (≥ 0.1.9), methods (≥ 4.4.3), purrr (≥ 1.0.4), furrr (≥ 0.3.1), future (≥ 1.49.0), checkmate (≥ 2.3.3), rlang (≥ 1.1.6), stats (≥ 4.4.3) |
| Suggests: | knitr, rmarkdown, tidyr (≥ 1.3.1), kableExtra (≥ 1.4.0), ggplot2 (≥ 4.0.1), scoringutils (≥ 2.1.2), testthat (≥ 3.0.0), progressr (≥ 0.15.1) |
| Published: | 2026-07-16 |
| DOI: | 10.32614/CRAN.package.modelimportance |
| Author: | Minsu Kim |
| Maintainer: | Minsu Kim <minsu at umass.edu> |
| BugReports: | https://github.com/mkim425/modelimportance/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/mkim425/modelimportance |
| NeedsCompilation: | no |
| Materials: | README, NEWS |
| CRAN checks: | modelimportance results |
| Reference manual: | modelimportance.html , modelimportance.pdf |
| Vignettes: |
Simple working examples (source, R code) 'modelimportance': Evaluating model importance within a multi-model ensemble in R (source, R code) |
| Package source: | modelimportance_0.1.0.tar.gz |
| Windows binaries: | r-devel: modelimportance_0.1.0.zip, r-release: modelimportance_0.1.0.zip, r-oldrel: modelimportance_0.1.0.zip |
| macOS binaries: | r-release (arm64): modelimportance_0.1.0.tgz, r-oldrel (arm64): modelimportance_0.1.0.tgz, r-release (x86_64): modelimportance_0.1.0.tgz, r-oldrel (x86_64): modelimportance_0.1.0.tgz |
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