mdmb1.9-22 package

Model Based Treatment of Missing Data

data.mb

Example Datasets for mdmb Package

eval_prior_list

Evaluates Several Prior Distributions

frm

Factored Regression Model: Generalized Linear Regression Model with Mi...

mdmb_regression

Several Regression Models with Prior Distributions and Sampling Weight...

mdmb-package

tools:::Rd_package_title("mdmb")

offset_values_extract

Extracts Offset Values

oprobit_dist

Ordinal Probit Models

remove_NA_data_frame

Removes Rows with Some Missing Entries in a Data Frame

yjt_dist

Scaled tt Distribution with Yeo-Johnson and Box-Cox Transformations

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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.

  • Maintainer: Alexander Robitzsch
  • License: GPL (>= 2)
  • Last published: 2024-07-15

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