Description: Alternative implementation of the beautiful 'MissForest' algorithm used to impute
mixed-type data sets by chaining random forests, 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
A formula interface allows to control which variables should be imputed by which.
Imports: stats, FNN (>= 1.1), ranger (>= 0.10)
Suggests: mice, dplyr, survival, ggplot2, knitr, rmarkdown

Description: Alternative implementation of the beautiful
'MissForest' algorithm used to impute mixed-type data sets by chaining
random forests, 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().  A
formula interface allows to control which variables should be imputed
Suggests: dplyr, survival, mice, rmarkdown, knitr, testthat

Description: Alternative implementation of the beautiful 'MissForest'
algorithm used to impute mixed-type data sets by chaining random
forests, introduced by Stekhoven, D.J. and Buehlmann, P. (2012)
Suggests: survival, dplyr, mice, rmarkdown, knitr, testthat