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>). T The regression model can be nonlinear (e.g., interaction quadratic effects or spline functions). Multilevel models with missing data in predictors is also available for Bayesian estimation. Substantive-model compatible multiple imputation can be also conducted.

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Package
mdmb
Type
Package
Title
Model Based Treatment of Missing Data
Version
0.8-47
Date
2018-07-09 18:39:51
Author
Alexander Robitzsch [aut, cre], Oliver Luedtke [aut]
Maintainer
Alexander Robitzsch
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; ). T The regression model can be nonlinear (e.g., interaction quadratic effects or spline functions). Multilevel models with missing data in predictors is also available for Bayesian estimation. Substantive-model compatible multiple imputation can be also conducted.
Depends
R (>= 3.1)
Imports
CDM, coda, graphics, MASS, miceadds (>= 2.13-60), Rcpp, sirt, stats, TAM, utils
Suggests
mice
LinkingTo
miceadds, Rcpp, RcppArmadillo
URL
License
GPL (>= 2)
NeedsCompilation
yes
Packaged
2018-07-09 16:40:27 UTC; sunpn563
Repository
CRAN
Date/Publication
2018-07-09 17:10:03 UTC

install.packages('mdmb')

0.8-47

7 days ago

https://github.com/alexanderrobitzsch/mdmb

Alexander Robitzsch

GPL (>= 2)

Depends on

R (>= 3.1)

Imports

CDM, coda, graphics, MASS, miceadds (>= 2.13-60), Rcpp, sirt, stats, TAM, utils

Suggests

mice

Discussions