Measuring Contributions of Component Models to Ensemble Forecast Accuracy R package modelimportance version 0.1.0 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>. Software 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) Minsu Kim <minsu@umass.edu> Comprehensive R Archive Network (CRAN) Minsu Kim [aut, cre, cph] (ORCID: <https://orcid.org/0009-0008-4637-3589>), Li Shandross [aut, ctb] (ORCID: <https://orcid.org/0009-0008-1348-1954>), Zhian Kamvar [ctb] (ORCID: <https://orcid.org/0000-0003-1458-7108>), Nicholas Reich [aut] (ORCID: <https://orcid.org/0000-0003-3503-9899>), Evan Ray [aut] MIT + file LICENSE (https://CRAN.R-project.org/package=modelimportance/LICENSE) 2026-07-16 application/tgz https://CRAN.R-project.org/package=modelimportance doi:10.32614/CRAN.package.modelimportance