CRAN Task View: Missing Data

Maintainer:Julie Josse, Nicholas Tierney, Nathalie Vialaneix (r-miss-tastic team)
Contact:r-miss-tastic at

Missing data are very frequently found in datasets. Base R provides a few options to handle them using computations that involve only observed data ( na.rm = TRUE in functions mean, var, ... or use = complete.obs|na.or.complete|pairwise.complete.obs in functions cov, cor, ...). The base package stats also contains the generic function na.action that extracts information of the NA action used to create an object.

These basic options are complemented by many packages on CRAN, which we structure into main topics:

In addition to the present task view, this reference website on missing data might also be helpful.

If you think that we missed some important packages in this list, please contact the maintainer.

Exploration of missing data

Likelihood based approaches

Single imputation

Multiple imputation

Some of the above mentioned packages can also handle multiple imputations.

In addition, mitools provides a generic approach to handle multiple imputation in combination with any imputation method. And NADIA provides a uniform interface to compare the performances of several imputation algorithms.

Weighting methods

Specific types of data

Specific application fields

CRAN packages:

Related links: