Abstract | 1. Introduction | 2. Data | 2.1 Dependencies and related software | 2.2 Model output format | 2.3 Forecast data representation | 2.4 Oracle output data | 3. Method description and algorithms | 3.1 Comparison of weighting schemes in LASOMO | 4. Evaluating models with the model_importance() function | model_importance( ) | 5. S3 class and methods | 5.1 Print method | 5.2 Summary method | 5.3 Aggregate method | 6. Examples | 6.1 Example data | Evaluation using LOMO algorithm | Evaluation using LASOMO algorithm | 7. Computational complexity | 8. Implementation and availability | Summary and discussion | Acknowledgements | Appendix | Weights for subsets in LASOMO | References
Package: modelimportance 0.1.0
Minsu Kim
modelimportance: Measuring Contributions of Component Models to Ensemble Forecast Accuracy
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>.
Authors:
modelimportance_0.1.0.tar.gz
modelimportance_0.1.0.tar.gz(r-4.7-any)modelimportance_0.1.0.tar.gz(r-4.6-any)
modelimportance_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
modelimportance/json (API)
| # Install 'modelimportance' in R: |
| install.packages('modelimportance', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mkim425/modelimportance/issues
Datasets:
- forecast_data_example - Example forecast outputs for modelimportance article vignette
- forecast_data_ma_h1 - Forecast outputs for Massachusetts
- forecast_data_raw - Raw forecast outputs for get-started vignette
- target_data_example - Example target data for modelimportance article vignette
- target_data_ma - Target data for Massachusetts used in vignette runtime data
- target_data_raw - Raw target data for get-started vignette
Last updated from:fad92f5cc3. Checks:4 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 248 | ||
| source / vignettes | OK | 279 | ||
| linux-release-x86_64 | OK | 254 | ||
| wasm-release | OK | 143 |
Exports:model_importance
Dependencies:askpassbackportscachemcheckmateclicodetoolscpp11curldata.tabledigestdistfromqdplyrevaluatefarverfastmapfsfurrrfuturegenericsggplot2gitcredsglobalsgluegtablehighrhttr2hubEnsembleshubEvalshubUtilsiniisobandjsonliteknitrlabelinglifecyclelistenvmagrittrMASSmatrixStatsmemoiseopensslparallellypillarpkgconfigpurrrR6RColorBrewerRcppRcppArmadillorlangS7scalesscoringRulesscoringutilsstringistringrsystibbletidyrtidyselectutf8vctrsviridisLitewithrxfunyamlzeallot
modelimportance: Evaluating model importance within a multi-model ensemble in R
Last update: 2026-07-16
Started: 2026-07-16
Simple working examples
Setup | Example data | Evaluation using LOMO algorithm | Evaluation using LASOMO algorithm | References
Last update: 2026-07-16
Started: 2026-07-16
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Aggregate model importance scores across tasks to compute overall importance for each model | aggregate.model_imp_tbl |
| Example forecast outputs for modelimportance article vignette | forecast_data_example |
| Forecast outputs for Massachusetts, horizon 1, used in vignette runtime data | forecast_data_ma_h1 |
| Raw forecast outputs for get-started vignette | forecast_data_raw |
| Quantifies the contribution of ensemble component models to ensemble prediction accuracy for each prediction task. | model_importance |
| Print method for model importance score table | print.model_imp_tbl |
| Print method for summary of model importance score table | print.summary.model_imp_tbl |
| Summary method for model importance score table | summary.model_imp_tbl |
| Example target data for modelimportance article vignette | target_data_example |
| Target data for Massachusetts used in vignette runtime data | target_data_ma |
| Raw target data for get-started vignette | target_data_raw |
