NEWS


mice 3.16.10

mice 3.16.9

mice 3.16.8

mice 3.16.7

Minor changes

mice 3.16.6

Minor changes

Bug fixes

mice 3.16.5

Bug fixes

mice 3.16.4

Major changes

mice 3.16.3

Major changes

Bug fixes

mice 3.16.2

Major changes

mice 3.16.1

Minor changes

mice 3.16.0 (2023-06-05)

Major changes

Minor changes

Bug fixes

mice 3.15.0 (2022-11-19)

Major changes

Minor changes

Bug fixes

mice 3.14.0 (2021-11-24)

Major changes

Minor changes

Bug fixes

mice 3.13.0 (2021-01-27)

Major changes

Minor changes

mice 3.12.0 (2020-11-14)

Major changes

Minor changes

mice 3.11.0 (2020-08-05)

Major changes

Minor changes

mice 3.10.0 (2020-07-13)

Major changes

Minor changes

mice 3.9.0 (2020-05-14)

Major changes

Minor changes

mice 3.8.0 (2020-02-21)

Major changes

Bug fixes

mice 3.7.0 (2019-12-13)

mice 3.6.0 (2019-07-10)

mice 3.5.0 (2019-05-13)

mice 3.4.0 (2019-03-07)

mice 3.3.0 (2018-07-27)

mice 3.2.0 (2018-07-24)

mice 3.1.0 (2018-06-20)

mice 3.0.0 (2018-05-25)

Version 3.0 represents a major update that implements the following features:

  1. blocks: The main algorithm iterates over blocks. A block is simply a collection of variables. In the common MICE algorithm each block was equivalent to one variable, which - of course - is the default; The blocks argument allows mixing univariate imputation method multivariate imputation methods. The blocks feature bridges two seemingly disparate approaches, joint modeling and fully conditional specification, into one framework;

  2. where: The where argument is a logical matrix of the same size of data that specifies which cells should be imputed. This opens up some new analytic possibilities;

  3. Multivariate tests: There are new functions D1(), D2(), D3() and anova() that perform multivariate parameter tests on the repeated analysis from on multiply-imputed data;

  4. formulas: The old form argument has been redesign and is now renamed to formulas. This provides an alternative way to specify imputation models that exploits the full power of R's native formula's.

  5. Better integration with the tidyverse framework, especially for packages dplyr, tibble and broom;

  6. Improved numerical algorithms for low-level imputation function. Better handling of duplicate variables.

  7. Last but not least: A brand new edition AND online version of Flexible Imputation of Missing Data. Second Edition.

mice 2.46.9

mice 2.46

mice 2.45

mice 2.44

mice 2.43

mice 2.42

mice 2.41

mice 2.40

mice 2.34

mice 2.31

mice 2.29

mice 2.28

mice 2.27

mice 2.25 (2015-11-09)

mice 2.23

mice 2.22 (2014-06-11)

mice 2.21 (2014-02-05)

mice 2.20 (2014-02-04)

mice 2.19

mice 2.18 (2013-08-01)

mice 2.17 (2013-05-12)

mice 2.16 (2013-04-27)

mice 2.15 (2013-04-03)

mice 2.14 (2013-03-19)

mice 2.13 (2012-07-04)

mice 2.12 (2012-03-25)

mice 2.11 (2011-11-22)

mice 2.10 (2011-09-15)

mice 2.9 (2011-09-01)

mice 2.8 (2011-03-26)

mice 2.7 (2011-03-16)

mice 2.6 (2011-03-04)

mice 2.5 (2011-01-06)

mice 2.4 (2010-10-18)

mice 2.3 (2010-02-14)

mice 2.2 (2010-01-14)

mice 2.1 (2009-09-18)

mice 2.0 (2009-08-27)

mice 1.21 (2009-03-17)

mice 1.16 (2009-02-19)

mice 1.15 (2007-01-09)

mice 1.14 (2006-04-04)

mice 1.13

mice 1.12

mice 1.1

mice 1.0