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

Documentation:

Reference manual:

mdmb.pdf

Downloads:

Package source:	mdmb_1.9-22.tar.gz
Windows binaries:	r-devel: mdmb_1.9-22.zip, r-release: mdmb_1.9-22.zip, r-oldrel: mdmb_1.9-22.zip
macOS binaries:	r-release (arm64): mdmb_1.9-22.tgz, r-oldrel (arm64): mdmb_1.9-22.tgz, r-release (x86_64): mdmb_1.9-22.tgz, r-oldrel (x86_64): mdmb_1.9-22.tgz
Old sources:	mdmb archive

Reverse dependencies:

Reverse suggests:

miceadds

Linking:

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