Package: mdmb
Type: Package
Title: Model Based Treatment of Missing Data
Version: 1.10-1
Date: 2024-07-15 23:06:34
Author: Alexander Robitzsch [aut, cre], Oliver Luedtke [aut]
Maintainer: Alexander Robitzsch <robitzsch@ipn.uni-kiel.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,
        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.
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/view/alexander-robitzsch/software
License: GPL (>= 2)
Config/pak/sysreqs: cmake make libicu-dev libx11-dev zlib1g-dev
Repository: https://alexanderrobitzsch.r-universe.dev
Date/Publication: 2024-07-16 07:20:16 UTC
RemoteUrl: https://github.com/alexanderrobitzsch/mdmb
RemoteRef: HEAD
RemoteSha: 809cbf6e2a6b1dbd363d7a92269909b65799f7b7
NeedsCompilation: yes
Packaged: 2026-06-24 04:16:33 UTC; root