mdmb: Model Based Treatment of Missing Data
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 |
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