mice 3.6.0

mice 3.6.0 is published on CRAN. It contains minor bug fixes and removes a dependency on the soon-to-be-retired DPpackage.

mice 3.5.0

This release corresponds to version mice 3.5.0 published on CRAN on 14may2019. It contains mainly bug fixes.

mice 3.4.0

This release corresponds to version mice 3.4.0 published on CRAN on 07mar2019. It contains various fixes of bugs and glitches that surfaced since release mice 3.3.0.

Better passive imputation

• Solves a problem in the routing logic (#149) causing that passive
  imputation was not done when no predictors were specified. No passive
  imputation correctly will ignore any the specification of
  predictorMatrix.
• Implements an alternative solution for #93 and #96. Instead of skipping
  imputation of variables without predictors, mice 3.3.1 will impute
  those variables using the intercept only

Increment

mice 3.2.0

This version brings incremental improvements and bug fixes.

• Skip tests for mice.impute.2l.norm() (#129)
• Skip tests for D1() (#128)
• Solve problem with md.pattern (#126)
• Evades warning in rbind and cbind (#114)
• Solves rbind problem when method is a list (#113)
• More efficient use of parlmice (#109)
• Add dfcom argument to pool() (#105, #110)
• Updates to parlmice + bugfix (#107)

Fixxxes and exxxtensions

The version solves some problems in mice 3.0.0, and add new functions:

• New parallel functionality parlmice (#104)
• Incorporate suggestion of @JoergMBeyer to flux (#102)
• Replace duplicate code by estimice (#101)
• Better checking for empty methods (#99)
• Remove problem with parent.frame (#98)
• Set empty method for complete data (#93)
• Add NEWS.md, index.Rmd and online package documentation
• Track .R instead of .r
• Patch issue with updateLog (#8, @alexanderrobitzsch)
• Extend README
• Repair issue md.pattern (#90)
• Repair check on m (#89)

MICE for Flexible Imputation of Missing Data, Second Edition

Very pleased to announce mice 3.0.0, a major redesign of the package. Important changes include:

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: mice 3.0.0 is fully synchronised with a brand new edition AND online version of
   Flexible Imputation of Missing Data. Second Edition.

I have tried to minimise the changes, in the hope that your existing code runs under this version. A (yet incomplete) overview of the changes to the function arguments can be found here.

Hope you find it useful.

All my loving

Highlights of this release:

• Introduces the new where argument that allows the user to specify which cells should be imputed
• Introduces new vignettes, course materials and other documentation
• Reduces packages size by placing vignettes online
• Updates the ampute facility for generating missing data
• Adds method mice.impute.2l.lmer
• Introduces 3-digits versioning

NEWS

V2.46.0 23oct2017 SvB
UPDATE Switch to 3-digit versioning

V2.46 22oct2017 SvB
FIXED Allow for capitals in imputation methods

V2.45 21oct2017 SvB
UPDATE Reorganized vignettes to land on GitHUB pages

V2.44 18oct2017 SvB
UPDATE Code changes for robustness, style and efficiency (Bernie Gray)

V2.43 20jul2017 SvB
UPDATE Updates to ampute function and vignettes (Rianne Schouten)

V2.42 11jul2017 SvB
UPDATE Rename mice.impute.2l.sys to mice.impute.2l.lmer

V2.41 10jul2017 SvB
ADDED New feature: whereargument to mice
ADDED New wy argument to imputation functions
ADDED New mice.impute.2l.sys(), author Shahab Jolani
UPDATE Many simplifications and code enhancements
FIXED Broken cbind() function
FIXED Bug that made the pad element disappear from mids object

V2.40 07jul2017 SvB
FIXED Integration with lattice package

V2.39 27jun2017 SvB
UPDATE Colors in xyplot.mads

V2.38 22may2017 SvB
ADDED Support for factors in mice.impute.2lonly.pmm()

V2.37 22may2017 SvB
UPDATE More robust version of as.mids()

V2.36 15may2017 SvB
UPDATE Substantial update of ampute() by Rianne Schouten

V2.35 03may2017 SvB
FIXED Fix timestamp problem by rebuilding vignette using R 3.4.0.

V2.34 24apr2017 SvB
UPDATE Update to roxygen 6.0.1

V2.33 11apr2017 SvB
CHANGED Stylistic changes to mice function (thanks Ben Ogorek)
CHANGED calls to cbind.mids() replaced by calls to cbind()

V2.31 23feb2017 SvB
ADDED link to miceVignettes on github (thanks Gerko Vink)
ADDED package documentation

V2.30 as on CRAN

MICE V2.30 incorporates new midastouch() and ampute() function, adds a README, and contains many bug fixes.

This is from the NEWS file:

V2.30    04feb2017 SvB
ADDED    README for GitHub
ADDED    new ampute functions and vignette (thanks Rianne Schouten)
CHANGED  rename ccn-->ncc, icn-->nic
CHANGED  helpers cc(), ncc(), cci(), ic(), nic() and ici() use S3 dispatch
CHANGED  issues tracker now on Github - add BugReports URL #21
FIXED    multinom MaxNWts type fix in polyreg and polr #9
FIXED    better checking of nested models in pool.compare #12
FIXED    bug fix in as.mids if names not same as all columns #11
FIXED    extension for glmer models #5

V2.29    05oct2016 SvB
ADDED    midastouch: predictive mean matching for small samples (thanks Philip Gaffert, Florian Meinfelder)

V2.28    05oct2016 SvB
FIXED    repaired dots problem in rpart call

V2.27    27jul2016 SvB
FIXED    add ridge to 2l.norm()

V2.26    12may2016 SvB
FIXED    removed .o files