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>). 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

Downloads:

Reference manual: mdmb.pdf
Package source: mdmb_1.3-18.tar.gz
Windows binaries: r-devel: mdmb_1.3-18.zip, r-release: mdmb_1.3-18.zip, r-oldrel: mdmb_1.3-18.zip
OS X binaries: r-release: mdmb_1.3-18.tgz, r-oldrel: mdmb_1.3-18.tgz
Old sources: mdmb archive

Reverse dependencies:

Reverse suggests: miceadds

Linking:

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