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mdmb

Model Based Treatment of Missing Data

If you use mdmb and have suggestions for improvement or have found bugs, please email me at robitzsch@leibniz-ipn.de. Please always provide a minimal dataset, necessary to demonstrate the problem, a minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and all necessary information on the used librarys, the R version, and the OS it is run on, perhaps a sessionInfo().

Manual

The manual may be found here https://alexanderrobitzsch.github.io/mdmb/

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:

utils::install.packages("mdmb")

GitHub version

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

devtools::install_github("alexanderrobitzsch/mdmb")

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Version

Install

install.packages('mdmb')

Monthly Downloads

1,053

Version

1.9-22

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Maintainer

Alexander Robitzsch

Last Published

July 15th, 2024

Functions in mdmb (1.9-22)

yjt_dist

Scaled \(t\) Distribution with Yeo-Johnson and Box-Cox Transformations
remove_NA_data_frame

Removes Rows with Some Missing Entries in a Data Frame
data.mb

Example Datasets for mdmb Package
eval_prior_list

Evaluates Several Prior Distributions
mdmb_regression

Several Regression Models with Prior Distributions and Sampling Weights
mdmb-package

tools:::Rd_package_title("mdmb")
frm

Factored Regression Model: Generalized Linear Regression Model with Missing Covariates
offset_values_extract

Extracts Offset Values
oprobit_dist

Ordinal Probit Models