Contains model-based treatment of missing data for regression models with missing values in covariates or the dependent variable using maximum likelihood or Bayesian estimation (Ibrahim et al., 2005; <doi:10.1198/016214504000001844>; Luedtke, Robitzsch, & West, 2020a, 2020b; <doi:10.1080/00273171.2019.1640104><doi:10.1037/met0000233>). The regression model can be nonlinear (e.g., interaction effects, quadratic effects or B-spline functions). Multilevel models with missing data in predictors are available for Bayesian estimation. Substantive-model compatible multiple imputation can be also conducted.
Version: 1.9-22 Depends: R (≥ 3.1) Imports: CDM, coda, graphics, miceadds (≥ 3.2-23), Rcpp, sirt, stats, utils LinkingTo: miceadds, Rcpp, RcppArmadillo Suggests: MASS Enhances: JointAI, jomo, mice, smcfcs Published: 2024-07-15 DOI: 10.32614/CRAN.package.mdmb Author: Alexander Robitzsch [aut, cre], Oliver Luedtke [aut] Maintainer: Alexander Robitzsch <robitzsch at ipn.uni-kiel.de> License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] URL: https://github.com/alexanderrobitzsch/mdmb, https://sites.google.com/site/alexanderrobitzsch2/software NeedsCompilation: yes Citation: mdmb citation info Materials: README NEWS In views: MissingData, MixedModels CRAN checks: mdmb results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=mdmb to link to this page.
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