# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "mixgb" in publications use:' type: software license: GPL-3.0-or-later title: 'mixgb: Multiple Imputation Through ''XGBoost''' version: 1.5.2 doi: 10.1080/10618600.2023.2252501 identifiers: - type: doi value: 10.32614/CRAN.package.mixgb abstract: Multiple imputation using 'XGBoost', subsampling, and predictive mean matching as described in Deng and Lumley (2023) . The package supports various types of variables, offers flexible settings, and enables saving an imputation model to impute new data. Data processing and memory usage have been optimised to speed up the imputation process. authors: - family-names: Deng given-names: Yongshi email: agnes.yongshideng@gmail.com orcid: https://orcid.org/0000-0001-5845-859X preferred-citation: type: article title: Multiple Imputation Through XGBoost authors: - family-names: Deng given-names: Yongshi email: agnes.yongshideng@gmail.com orcid: https://orcid.org/0000-0001-5845-859X - family-names: Lumley given-names: Thomas journal: Journal of Computational and Graphical Statistics volume: '33' issue: '2' year: '2023' publisher: name: Taylor & Francis doi: 10.1080/10618600.2023.2252501 start: 352-363 repository: https://CRAN.R-project.org/package=mixgb repository-code: https://github.com/agnesdeng/mixgb url: https://github.com/agnesdeng/mixgb date-released: '2024-12-02' contact: - family-names: Deng given-names: Yongshi email: agnes.yongshideng@gmail.com orcid: https://orcid.org/0000-0001-5845-859X