MICE: Multivariate Imputation by Chained Equations


  1. mice package at CRAN
  2. mice GitHUB repository


The mice package can be installed from CRAN as follows:


The latest version is can be installed from GitHub as follows:

devtools::install_github(repo = "stefvanbuuren/mice")

Capabilities of mice package

The mice package contains functions to

Main functions

The main functions in the mice package are:

Function name Description
mice() Impute the missing data \(m\) times
with() Analyze completed data sets
pool() Combine parameter estimates
complete() Export imputed data
ampute() Generate missing data

Course materials

  1. Handling Missing Data in R with mice
  2. Statistical Methods for combined data sets


  1. Ad hoc methods and the MICE algorithm
  2. Convergence and pooling
  3. Inspecting how the observed data and missingness are related
  4. Passive imputation and post-processing
  5. Imputing multilevel data
  6. Sensitivity analysis with mice
  7. Generate missing values with ampute

Code from publications

  1. Flexible Imputation of Missing Data
  2. mice: Multivariate Imputation by Chained Equations in R

Further reading

  1. Article in the Journal of Statistical Software (Buuren and Groothuis-Oudshoorn 2011).
  2. The first application on missing blood pressure data (Buuren, Boshuizen, and Knook 1999).
  3. Term Fully Conditional Specification describes a general class of methods that specify imputations model for multivariate data as a set of conditional distributions (Buuren et al. 2006).
  4. Details about imputing mixes of numerical and categorical data can be found in (Buuren 2007).
  5. Wulff and Ejlskov provide a comprehensive overview of MICE.
  6. Many more details and applications can be found in the book Flexible Imputation of Missing Data (Buuren 2012) (see Chapman & Hall/CRC or Amazon).


Buuren, S. van. 2007. “Multiple Imputation of Discrete and Continuous Data by Fully Conditional Specification.” Statistical Methods in Medical Research 16 (3): 219–42.

———. 2012. Flexible Imputation of Missing Data. Boca Raton, FL: Chapman & Hall/CRC Press.

Buuren, S. van, and K. Groothuis-Oudshoorn. 2011. “MICE: Multivariate Imputation by Chained Equations in R.” Journal of Statistical Software 45 (3): 1–67.

Buuren, S. van, H. C. Boshuizen, and D. L. Knook. 1999. “Multiple Imputation of Missing Blood Pressure Covariates in Survival Analysis.” Statistics in Medicine 18 (6): 681–94.

Buuren, S. van, J. P. L. Brand, C. G. M. Groothuis-Oudshoorn, and D. B. Rubin. 2006. “Fully Conditional Specification in Multivariate Imputation.” Journal of Statistical Computation and Simulation 76 (12): 1049–64.