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; ). 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.

Model Based Treatment of Missing Data

If you use mdmb and have suggestions for improvement or have found bugs, please email me at [email protected].

CRAN version

The official version of mdmb is hosted on CRAN and may be found here. The CRAN version can be installed from within R using:


GitHub version

The version hosted here is the development version of mdmb. The GitHub version can be installed using devtools as:




------------------------- mdmb NEWS ---------------------------


|\ /||\ |\ /||
| / || || / ||--< | ||/ | ||/

mdmb: Model Based Treatment of Missing Data

Alexander Robitzsch & Oliver Luedtke

Questions or suggestions about mdmb should be sent to [email protected]

In case of reporting a bug, please always provide a reproducible script.



VERSIONS mdmb 0.11 | 2018-10-16 | Last: mdmb 0.11-7

NOTE * included consistency checks of model names for covariate models in frm_em() and frm_fb() NOTE * fixed an error in mdmb_regression() for Yeo-Johnson transformed variables (incompatible dimensions)

DATA * included/modified datasets: --- EXAMP * included/modified examples: ---

VERSIONS mdmb 0.10 | 2018-09-12 | Last: mdmb 0.10-13

NOTE * included argument 'control_optim_fct' in oprobit_regression(). Slight speed improvements in case of many ordered categories. FIXED * fixed a recently introduced bug for linear regression models in frm_fb()

DATA * included/modified datasets: --- EXAMP * included/modified examples: ---

VERSIONS mdmb 0.9 | 2018-08-08 | Last: mdmb 0.9-43

ADDED * extended Yeo-Johnson transformation to include bounded variables on [0,1] by employing a probit transformation. The extension is implemented in fit_yjt_scaled(), yjt_regression(), frm_em() and frm_fb().

DATA * included/modified datasets: data.mb05 EXAMP * included/modified examples: yjt_dist (6), frm_em (11)

VERSIONS mdmb 0.8 | 2018-07-09 | Last: mdmb 0.8-47

ADDED * included option for Gibbs sampling in frm_fb() which can be enabled by the argument 'use_gibbs' ADDED * included multilevel regression for normally distributed and ordinal data in frm_fb() (model 'mlreg')

DATA * included/modified datasets: data.mb04 EXAMP * included/modified examples: frm (10)

VERSIONS mdmb 0.7 | 2018-04-24 | Last: mdmb 0.7-19

NOTE * included argument 'as_mids' for conversion into mids objects in frm2datlist() NOTE * translated some parts of calculations in frm_em() into Rcpp

DATA * included/modified datasets: --- EXAMP * included/modified examples: ---

VERSIONS mdmb 0.6 | 2018-02-16 | Last: mdmb 0.6-17

FIXED * fixed bug in oprobit_regression() when no regression coefficients were estimated in the model FIXED * fixed incorrect matching of MCMC diagnostics informations in summary output FIXED * included correct labelling in frm_em() and frm_fb() if classes 'yjtreg' and 'bctreg' are used NOTE * included dataset data.mb03 from Enders et al. (2014, PsychMeth) FIXED * fixed problems in numerical overflow when calculating Metropolis-Hasting ratios in frm_fb()

DATA * included/modified datasets: data.mb03 EXAMP * included/modified examples: data.mb (1)

VERSIONS mdmb 0.5 | 2018-01-22 | Last: mdmb 0.5-27

ADDED * added example for modelling nonignorable data with frm_em() (see Example 8 in 'frm.Rd') ADDED * added ordinal probit distribution fit_oprobit() and ordinal probit regression oprobit_regression(). ADDED * extended frm_em() and frm_fb() functions to ordinal data NOTE * fixed incorrect variable names in parts of the MCMC summary output in frm_fb()

DATA * included/modified datasets: --- EXAMP * included/modified examples: frm (8), oprobit_dist (1), mdmb_regression (4), frm (9)

VERSIONS mdmb 0.4 | 2017-08-20 | Last: mdmb 0.4-15

NOTE * speeded data processing in frm_em() NOTE * included Example 1.3 in frm() using the jomo package for imputation under a substantive model containing interaction effects NOTE * included example for estimation of model including latent interaction effects with frm_em() function NOTE * included argument 'log' in dt_scaled(), dbct_scaled() and dyt_scaled() NOTE * included more efficient computation of gradient in logistic_regression(), bct_regression() and yjt_regression(). Different computation methods can be chosen by the argument 'use_grad'. These gradients are now also used in frm_em() which results in some speed improvement.
NOTE * changed initial values in logistic_regression(), bct_regression() and yjt_regression() to least-squares solutions

DATA * included/modified datasets: --- EXAMP * included/modified examples: frm (1.3, 7)

VERSIONS mdmb 0.3 | 2017-07-12 | Last: mdmb 0.3-11

NOTE * changed default in 'nodes_control' in 'frm_em' to use a wider integration grid for missing values NOTE * added information about standard error calculation in 'frm_em'; reference Jamshidian and Jennrich (2000, JRSSB) FIXED * fixed an error in the 'frm_em' function which was not yet applicable for 'yjtreg' regression models

DATA * included/modified datasets: --- EXAMP * included/modified examples: ---

Versions 0.2 -- 2017-02-07 -- CRAN mdmb 0.2-0

FIXED * fixed an error in initialization of sigma parameter in mdmb::frm_fb function with 'linreg' imputation

DATA * included/modified datasets: --- EXAMP * included/modified examples: ---

Versions 0.1 -- 2017-01-25 -- CRAN mdmb 0.1-0

INIT * initial version of the package ADDED * added functions for scaled t distribution with Yeo-Johnson transformation ('d_yjt_scaled', 'fit_yjt_scaled') and Box-Cox transformation ('d_bct_scaled', 'fit_bct_scaled') ADDED * added additional regression functions 'logistic_regression', 'yjt_regression' and 'bct_regression' ADDED * added function 'frm_em' for maximum likelihood estimation of regression models with missing covariates ADDED * added function 'frm_fb' for fully Bayesian estimation of regression models with missing covariates. Imputations of missing values are provided. NOTE * included utility functions 'eval_prior_list', 'eval_prior_list_sumlog', 'offset_values_extract' and 'remove_NA_data_frame'

DATA * included/modified datasets: data.mb01, data.mb02 EXAMP * included/modified examples: mbmb_regression (1,2,3,4,5), frm (1,2,3,4,5), yjt_dist (1,2,3,4,5)

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


1.2-4 by Alexander Robitzsch, 12 days ago,

Browse source code at

Authors: Alexander Robitzsch [aut, cre] , Oliver Luedtke [aut]

Documentation:   PDF Manual  

Task views: Missing Data

GPL (>= 2) license

Imports CDM, coda, graphics, MASS, miceadds, Rcpp, sirt, stats, utils

Suggests mice

Linking to miceadds, Rcpp, RcppArmadillo

See at CRAN