Supply functions for the creation and handling of
missing data as well as tools to evaluate missing data methods. Nearly
all possibilities of generating missing data discussed by Santos et.
al (2019) <doi:10.1109/ACCESS.2019.2891360> and some additional are
implemented.  Functions are supplied to compare parameter estimates
and imputed values to true values to evaluate missing data methods.
Evaluations of these types are done, for example, by Cetin-Berber et
al. (2019) <doi:10.1177/0013164418805532> and Kim et al. (2005)
<doi:10.1093/bioinformatics/bth499>.
ggplot2, knitr, lpSolve, mvtnorm, norm, rmarkdown, testthat
(>= 2.1.0), tibble

Supply functions for the creation and handling of missing
data as well as tools to evaluate missing data methods. Nearly all
possibilities of generating missing data discussed by Santos et al.
(2019) <doi:10.1109/ACCESS.2019.2891360> and some additional are
implemented.  Functions are supplied to compare parameter estimates
and imputed values to true values to evaluate missing data methods.
Evaluations of these types are done, for example, by Cetin-Berber et
al. (2019) <doi:10.1177/0013164418805532> and Kim et al. (2005)
<doi:10.1093/bioinformatics/bth499>.

ggplot2, knitr, lpSolve, norm, rmarkdown, testthat (>=
2.1.0), tibble

person('Michael', 'Mayer', email = 'mayermichael79@gmail.com', role = c('aut', 'cre', 'cph'))
Alternative implementation of the beautiful 'MissForest' algorithm used to impute mixed-type data sets by chaining tree ensembles, introduced by Stekhoven, D.J. and Buehlmann, P. (2012) <doi:10.1093/bioinformatics/btr597>. Under the hood, it uses the lightning fast random jungle package 'ranger'. Between the iterative model fitting, we offer the option of using predictive mean matching. This firstly avoids imputation with values not already present in the original data (like a value 0.3334 in 0-1 coded variable). Secondly, predictive mean matching tries to raise the variance in the resulting conditional distributions to a realistic level. This would allow e.g. to do multiple imputation when repeating the call to missRanger().