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. The regression
model can be nonlinear (e.g., including interaction or quadratic
effects). Multiple imputation can be also conducted.

CDM, coda, graphics, MASS, miceadds, sirt, stats, TAM, utils

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., including interaction or quadratic effects).
Multiple imputation can be also conducted.
CDM, coda, graphics, MASS, miceadds, Rcpp, sirt, stats, TAM,
utils

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.

CDM (>= 7.0-12), coda, graphics, MASS, miceadds (>= 2.13-60),
Rcpp, sirt, stats, utils

CDM (>= 7.1-2), coda, graphics, MASS, miceadds (>= 2.13-60),
Rcpp, sirt, stats, utils

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

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.