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>).
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: |
1.3-18 |
| 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: |
2019-04-16 |
| 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 |
| CRAN checks: |
mdmb results |