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.

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Description file content

Package
mdmb
Type
Package
Title
Model Based Treatment of Missing Data
Version
1.3-18
Date
2019-04-16 13:17:19
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; ). 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
License
GPL (>= 2)
NeedsCompilation
yes
Packaged
2019-04-16 11:19:17 UTC; sunpn563
Repository
CRAN
Date/Publication
2019-04-16 11:53:09 UTC

install.packages('mdmb')

1.3-18

a month ago

https://github.com/alexanderrobitzsch/mdmb

Alexander Robitzsch

GPL (>= 2)

Depends on

R (>= 3.1)

Imports

CDM, coda, graphics, miceadds (>= 3.2-23), Rcpp, sirt, stats, utils

Suggests

MASS

Discussions