This package uses the ranger package [1] to do fast missing value imputation by chained tree ensembles, see [2] and [3]. Between the iterative model fitting, it offers the option of using predictive mean matching. This firstly avoids the imputation with values not present in the original data (like a value 0.3334 in a 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(). Package mice utilizes the randomForest package with only ten trees as default.

Please check the help ?missRanger for how to call the function and to see all options.


This example first generates a data set with about 10% missing values in each column. Then those gaps are filled by missRanger. In the end, the resulting data frame is displayed.

# Generate data with missing values in all columns
irisWithNA <- generateNA(iris)
# Impute missing values with missRanger
irisImputed <- missRanger(irisWithNA, pmm.k = 3, num.trees = 100)
# Check results

# With extra trees algorithm
irisImputed_et <- missRanger(irisWithNA, pmm.k = 3, splitrule = "extratrees", num.trees = 100)


Release 1.0.2 on CRAN



[1] Wright, M. N. & Ziegler, A. (2016). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software, in press. http://arxiv.org/abs/1508.04409.

[2] Stekhoven, D.J. and Buehlmann, P. (2012), ‘MissForest - nonparametric missing value imputation for mixed-type data’, Bioinformatics, 28(1) 2012, 112-118, doi: 10.1093/bioinformatics/btr597

[3] Van Buuren, S., Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1-67. http://www.jstatsoft.org/v45/i03/