Package: mdmb
Type: Package
Title: Model Based Treatment of Missing Data
Version: 1.4-12
Date: 2020-05-11 18:13:34
Author: 
    Alexander Robitzsch [aut, cre], Oliver Luedtke [aut]
Maintainer: Alexander Robitzsch <robitzsch@leibniz-ipn.de>
Description: 
    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, 2019a, 2019b, <doi:10.1037/met0000233>; 
    <doi:10.1080/00273171.2019.1640104>).
    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.
Depends: R (>= 3.1)
Imports: CDM, coda, graphics, miceadds (>= 3.2-23), Rcpp, sirt, stats,
        utils
Suggests: MASS
LinkingTo: miceadds, Rcpp, RcppArmadillo
Enhances: JointAI, jomo, mice, smcfcs
URL: https://github.com/alexanderrobitzsch/mdmb,
        https://sites.google.com/site/alexanderrobitzsch2/software
License: GPL (>= 2)
NeedsCompilation: yes
Packaged: 2020-05-11 16:15:56 UTC; sunpn563
Repository: CRAN
Date/Publication: 2020-05-12 17:11:06 UTC
Built: R 4.0.2; x86_64-w64-mingw32; 2020-08-02 14:02:55 UTC; windows
Archs: i386, x64
