Changelog

Major changes

Fixed a long-standing issue in the internal augment() function that affected ordered factors (#713).

Previously, augment() would:
1. Convert ordered factors into unordered ones, and
2. Reorder their levels alphabetically, ignoring the user-specified order.
The old behavior could degrade imputation quality for ordinal outcomes when using the "polr" method, potentially causing model convergence issues or increased noise in imputations.

The issue did not affect methods for unordered factors ("logreg", "polyreg", "mnar.logreg"), where level order is inconsequential.

Thanks to @mmansolf for identifying the problem and suggesting a fix. The updated augment() now correctly preserves the ordered class and level order of factor variables.
mice will now automatically move all passive variables to the end of the visitSequence for passive methods used without a user-specified visitSequence. This change in behavior ensures greater consistency at the end of each iteration.

The new behavior works well for simple cases. However, for more complex situations — especially when passive variables depend on other passive variables — it is recommended to manually specify a visitSequence that updates each passive variable immediately after one of its right-hand side predictors changes. (#699)
Adds the calltype argument to mice() for mixing predictorMatrix and formulas specifications per variable-block. The calltype argument allows the user to specify some variables (or blocks of variables) by the formulas argument, and other variables by predictorMatrix argument. (Note: This argument was called modeltype in version 3.17.1).

calltype is a character vector of length(blocks) elements that indicates how the imputation model is specified. Entries can one of two values: "pred" or "formula". If calltype = "pred", the predictors of the imputation model for the block are specified by the corresponding row of the predictorMatrix. If calltype = "formula" the imputation model is specified by relevant entry in formulas. The default depends on the presence of the formulas argument. If formulas is present, then mice() sets calltype = "formula" for any block for which a formula is specified. Otherwise, calltype = "pred".
Introduces an optimized matchindex C++ function to improve speed of predictive mean matching (#695)

Minor changes

Updates security dependabot to dawidd6/action-download-artifact@v9
Allow for negative adjusted R2 in pool.r.squared() (#700)
Combines and updates tests for lasso.select.norm() and lasso.norm() into one file test-mice.impute.lasso.norm.R
Combines and updates tests for lasso.select.logreg() and lasso.logreg() into one file test-mice.impute.lasso.logreg.R
Adds support for roxygen markdown documentation

Bug fixes

Adds a patch to resolve a problem - introduced in mice 3.17.0 - with the dfcom argument of pool(..., dfcom = .., ) (#689, #706, #707)
Fix sequence mismatch between method and formulas (#698)

Major changes

Imputing categorical data by predictive mean matching. Predictive mean matching (PMM) is the default method of mice() for imputing numerical variables, but it has long been possible to impute factors. This enhancement introduces better support to work with categorical variables in PMM. The former system translated factors into integers by ynum <- as.integer(f). However, the order of integers in ynum may have no sensible interpretation for an unordered factor. The new system quantifies ynum and could yield better results because of higher $R^2$ . The method calculates the canonical correlation between y (as dummy matrix) and a linear combination of imputation model predictors x. The algorithm then replaces each category of y by a single number taken from the first canonical variate. After this step, the imputation model is fitted, and the predicted values from that model are extracted to function as the similarity measure for the matching step.
The method works for both ordered and unordered factors. No special precautions are taken to ensure monotonicity between the category numbers and the quantifications, so the method should be able to preserve quadratic and other non-monotone relations of the predicted metric. It may be beneficial to remove very sparsely filled categories, for which there is a new trim argument. All you have to use the new technique is specify to mice(..., method = "pmm", ...). Both numerical and categorical variables will then be imputed by PMM.
Potential advantages are:
- Simpler and faster than fitting a generalised linear model, e.g., logistic regression or the proportional odds model;
- Should be insensitive to the order of categories;
- No need to solve problems with perfect prediction;
- Should inherit the good statistical properties of predictive mean matching.
Note that we still lack solid evidence for these claims. (#576). Contributed @stefvanbuuren
New system-independent method for pooling: This version introduces a new function pool.table() that takes a tidy table of parameter estimates stemming from m repeated analyses. The input data must consist of three columns (parameter name, estimate, standard error) and a specification of the degrees of freedom of the model fitted to the complete data. The pool.table() function outputs 14 pooled statistics in a tidy form. The primary use of pool.table() is to support parameter pooling for techiques that have no tidy() or glance() methods, either within R or outside R. The pool.table() function also allows for a novel workflows that 1) break apart the traditional pool() function into a data-wrangling part and a parameters-reducing part, and 2) does not necessarily depend on classed R objects. (#574). Contributed @stefvanbuuren
literanger: Adds support for the literanger package for rf imputation that is about twice as fast as ranger (#648). Thanks @stephematician for the contribution.

Breaking changes

The complete(..., action = "long", ...) command puts the columns named ".imp" and ".id" in the last two positions of the long data (instead of first two positions). In this way, the columns of the imputed data will have the same positions as in the original data, which is more user-friendly and easier to work with. Note that any existing code that assumes that variables ".imp" and ".id" are in columns 1 and 2 will need to be modified. The advice is to modify the code using the variable names ".imp" and ".id". If you want the old behaviour, specify the argument order = "first". (#569). Contributed @stefvanbuuren
Drops support for S4. Convert S4-related code to S3. Syntax as(df, "mids") is deprecated. Use as.mids(df) instead.

Minor changes

Adds support for the dots argument to ranger::ranger(...) in mice.impute.rf() (#563). Contributed @edbonneville
Prepares for the deprecation of the blocks argument at various places
Removes the need for blocks in initialize_chain()
In rbind(), when formulas are concatenated and duplicate names are found, also rename the duplicated variables in formulas by their new name
Solves problem with the package documentation link
Simplifies NEWS.md formatting to get correct version sequence on CRAN and in-package NEWS
Initialize single-variables blocks in make.method() in a more efficient way (resolves #672)
Prevent as.mids() from filling the imp object for complete variables
Defines S3 class constructors for mids, mads, mira and mipo objects

Bug fixes

Fixes the “large logo” problem. (#574). Contributed @hanneoberman
Patches a bug in complete() that auto-repeated imputed values into cells that should NOT be imputed (occurred as a special case of rbind(), where the first set of rows was imputed and the second was not).
Replaces the internal variable type by the more informative pred (currently active row of predictorMatrix)
Fixes a bug in filter.mids() that incorrectly removed empty components in the imp object
Fixes a bug in ibind() that incorrectly used length(blocks) as the first dimension of the chainMean and chainVar objects
Corrects the description visitSequence, chainMean and chainVar components of the mids object
Fixes problems with zero predictors (#588)
Fixes a problem with the minpuc argument in quickpred() (#634)
Fixes coef() not available on S4 object when using with lavaan (#615, #616)
Adds .github/dependabot.yml configuration to automate daily check (#598)
Update documentation tags to roxygen2 7.3.1 requirements
Repairs lost braces in the documentation
Fixes an installation problem when Rprofile prints to stdout on Fedora, R version 4.1.3 (#646, #647). Thanks @brookslogan for the fix.
Fixes a bug during initialization of factor values
Removes methods and rlang from Depends
Removes export of non-user facing ampute() helpers
Clears \link statements that do not pass CRAN checks

Major changes

Expands futuremice() functionality by allowing for external packages and user-written functions (#550). Contributed @thomvolker
Adds GH issue templates bug_report, feature_request and help_wanted (#560). Contributed @hanneoberman

Minor changes

Removes documentation files for rbind.mids() and cbind.mids() to conform to CRAN policy
Adds mitml and glmnet to imports so that test code conforms to _R_CHECK_DEPENDS_ONLY=true flag in R CMD check
Initializes random number generator in futuremice() if there is no .Random.seed yet.
Updates GitHub actions for package checking and site building
Preserves user settings in predictorMatrix for case F by adding a predictorMatrix argument to make.predictorMatrix()
Polishes mice.impute.mpmm() example code
Adds support for non-syntactic variables names with backticks (#631)

Bug fixes

Adds proper support for factors to mice.impute.2lonly.pmm() (#555)
Solves function naming problems for S3 generic functions tidy(), update(), format() and sum()
Out-comments and weeds example&test code to silence R CMD check with _R_CHECK_DEPENDS_ONLY=true
Fixes small bug in futuremice() that throws an error when the number of cores is not specified, but the number of available cores is greater than the number of imputations.
Solves a bug in mice.impute.mpmm() that changed the column order of the data

Major changes

Adds a function futuremice() with support for parallel imputation using the future package (#504). Contributed @thomvolker, @gerkovink
Adds multivariate predictive mean matching mice.impute.mpmm(). (#460). Contributed @Mingyang-Cai
Adds convergence() for convergence evaluation (#484). Contributed @hanneoberman
Reverts the internal seed behaviour back to mice 3.13.10 (#515). #432 introduced new local seed in response to #426. However, various issues arose with this facility (#459, #492, #502, #505). This version restores the old behaviour using global .Random.seed. Contributed @gerkovink
Adds a custom.t argument to pool() that allows the advanced user to specify a custom rule for calculating the total variance $T$ . Contributed @gerkovink
Adds new argument exclude to mice.impute.pmm() that excludes a user-specified vector of values from matching. Excluded values will not appear in the imputations. Since the observed values are not imputed, the user-specified values are still being used to fit the imputation model (#392, #519). Contributed @gerkovink

Minor changes

Styles all .R and .Rmd files
Makes post-processing assignment consistent with lines 85/86 in sampler.R (#511)
Edit test broken on R<4 (#501). Contributed @MichaelChirico
Adds support for models reporting contrasts rather than terms (#498). Contributed @LukasWallrich
Applies edits to autocorrelation function (#491). Contributed @hanneoberman
Changes p-value calculation to more robust alternative (#494). Contributed @AndrewLawrence
Uses inherits() to check on class membership
Adds decprecation notices to parlmice()
Adapt prop, patterns and weights matrices for pattern with only 1’s
Adds warning when patterns cannot be generated (#449, #317, #451)
Adds warning on the order of model terms in D1() and D2() (#420)
Adds example code to fit model on train data and apply to test data to mice()
Adds example code on synthetic data generation and analysis in make.where()
Adds testfile test-mice.impute.rf.R(#448)

Bug fixes

Replaces .Random.seed reads from the .GlobalEnv by get(".Random.seed", envir = globalenv(), mode = "integer", inherits = FALSE)
Repairs capitalisation problems with lastSeedValue variable name
Solves x$lastSeedValue problem in cbind.mids() (#502)
Fixes problems with ampute()
Preserves stochastic nature of mice() by smarter random seed initialisation (#459)
Repairs a drop = FALSE buglet in mice.impute.rf() (#447, #448)
@str-amg reported that the new dependency on withr package should have version 2.4.0 (published in January 2021) or higher. Versions withr 2.3.0 and before may give Error: object 'local_seed' is not exported by 'namespace:withr'. Either update manually, or install the patched version mice 3.14.1 from GitHub. (#445). NOTE: withr is no longer needed in mice 3.15.0

Major changes

Adds four new univariate functions using the lasso for automatic variable selection. Contributed by @EdoardoCostantini (#438).
- mice.impute.lasso.norm() for lasso linear regression
- mice.impute.lasso.logreg() for lasso logistic regression
- mice.impute.lasso.select.norm() for lasso selector + linear regression
- mice.impute.lasso.select.logreg() for lasso selector + logistic regression
Adds Jamshidian && Jalal’s non-parametric MCAR test, mice::MCAR() and associated plot method. Contributed by @cjvanlissa (#423).
Adds two new functions pool.syn() and pool.scalar.syn() that specialise pooling estimates from synthetic data. The "reiter2003" pooling rule assumes that synthetic data were created from complete data. Thanks Thom Volker (#436).
By default, mice.impute.rf() now uses the faster ranger package as back-end instead of randomForest package. If you want the old behaviour specify the rfPackage = "randomForest" argument to the mice(...) call. Contributed @prockenschaub (#431).

Minor changes

Avoids changing the global .Random.seed (#426, #432) by implementing withr::local_preserve_seed() and withr::local_seed(). This change provides stabler behavior in complex scripts. The change does not appear to break reproducibility when mice() was run with a seed. Nevertheless, if you run into a reproducibility problem, install mice 3.13.12 or before.
Improves the imputation of parabolic data in mice.impute.quadratic(), adds a parameter quad.outcome containing the name of the outcome variable in the complete-data model. Contributed @Mingyang-Cai, @gerkovink (#408)
Generalises pool() so that it processes the parameters from all gamlss sub-models. Thanks Marcio Augusto Diniz (#406, #405)
Uses the robust standard error estimate for pooling when pool() can extract robust.se from the object returned by broom::tidy() (#310)
Replaces URL to jstatsoft with DOI
Update reference to literature (#442)
Informs the user that pool() cannot take a mids object (#433)
Updates documentation for post-processing functionality (#387)
Adds Rcpp necessities
Solves a problem with “last resort” initialisation of factors (#410)
Documents the “flat-line behaviour” of mice.impute.2l.lmer() to indicate a problem in fitting the imputation model (#385)
Add reprex to test (#326)
Documents that multivariate imputation methods do not support the post parameter (#326)

Bug fixes

Contains an emergency solution as install.on.demand() broke the standard CRAN workflow. mice 3.14.0 does not call install.on.demand() anymore for recommended packages. Also, install.on.demand() will not run anymore in non-interactive mode.
Repairs an error in the mice:::barnard.rubin() function for infinite dfcom. Thanks @huftis (#441).
Solves problem with Xi <- as.matrix(...) in mice.impute.2l.lmer() that occurred when a cluster contains only one observation (#384)
Edits the predictorMatrix to a monotone pattern if visitSequence = "monotone" and maxit = 1 (#316)
Solves a problem with the plot produced by md.pattern() (#318, #323)
Fixes the intercept in make.formulas() (#305, #324)
Fixes seed when using newdata in mice.mids() (#313, #325)
Solves a problem with row names of the where element created in rbind() (#319)
Solves a bug in mnar imputation routine. Contributed by Margarita Moreno Betancur.

Major changes

Updated mids2spss() replaces the foreign by haven package. Contributed Gerko Vink (#291)

Minor changes

Repairs an error in tests\testhat\test-D1.R that failed on mitml 0.4-0
Reverts with.mids() function to old version because the change in commit 4634094 broke downstream package metafor (#292)
Solves a glitch in mice.impute.rf() in finding candidate donors (#288, #289)

Major changes

Much faster predictive mean matching. The new matchindex C function makes predictive mean matching 50 to 600 times faster. The speed of pmm is now on par with normal imputation (mice.impute.norm()) and with the miceFast package, without compromising on the statistical quality of the imputations. Thanks to Polkas https://github.com/Polkas/miceFast/issues/10 and suggestions by Alexander Robitzsch. See #236 for more details.
New ignore argument to mice(). This argument is a logical vector of nrow(data) elements indicating which rows are ignored when creating the imputation model. We may use the ignore argument to split the data into a training set (on which the imputation model is built) and a test set (that does not influence the imputation model estimates). The argument is based on the suggestion in https://github.com/amices/mice/issues/32#issuecomment-355600365. See #32 for more background and techniques. Crafted by Patrick Rockenschaub
New filter() function for mids objects. New filter() method that subsets a mids object (multiply-imputed data set). The method accepts a logical vector of length nrow(data), or an expression to construct such a vector from the incomplete data. (#269). Crafted by Patrick Rockenschaub.
Breaking change: The matcher algorithm in pmm has changed to matchindex for speed improvements. If you want the old behavior, specify mice(..., use.matcher = TRUE).

Minor changes

Corrected installation problem related to cpp11 package (#286)
Simplifies with.mids() by calling eval_tidy() on a quosure. Does not yet solve #265.
Improve documentation for pool() and pool.scalar() (#142, #106, #190 and others)
Makes tidy.mipo more flexible (#276)
Solves a problem if nelsonaalen() gets a tibble (#272)
Add explanation to how NAs can appear in the imputed data (#267)
Add warning to quickpred() documentation (#268)
Styles all sources files with styler
Improves consistency in code and documentation
Moves internally defined functions to global namespace
Solves bug in internal sum.scores()
Adds deprecated messages to lm.mids(), glm.mids(), pool.compare()
Removes .pmm.match() and expandcov()
Strips out all return() calls placed just before end-of-function
Remove all trailing spaces
Repairs a bug in the routine for finding the printFlag value (#258)
Update URL’s after transfer to organisation amices

Major changes

The Cox model does not return df.residual, which caused problematic behavior in the D1(), D2(), D3(), anova() and pool(). mice now extracts the relevant information from other parts of the objects returned by survival::coxph(), which solves long-standing issues with the integration of the Cox model (#246).

Minor changes

Adds missing Rccp dependency to work with tidyr 1.1.1 (#248).
Addresses warnings: Non-file package-anchored link(s) in documentation object.
Updates on ampute documentation (#251).
Ask user permission before installing a package from suggests.

Major changes

New functions tidy.mipo() and glance.mipo() return standardized output that conforms to broom specifications. Kindly contributed by Vincent Arel Bundock (#240).

Minor changes

Solves a problem with the D3 testing script that produced an error on CRAN (#244).

Major changes

The D3() function in mice gave incorrect results. This version solves a problem in the calculation of the D3-statistic. See #226 and #228 for more details. The documentation explains why results from mice::D3() and mitml::testModels() may differ.
The pool() function is now more forgiving when there is no glance() function (#233)
It is possible to bypass remove.lindep() by setting eps = 0 (#225)

Minor changes

Adds reference to Leacy’s thesis
Adds an example to the plot.mids() documentation

Major changes

This version adds two new NARFCS methods for imputing data under the Missing Not at Random (MNAR) assumption. NARFCS is generalised version of the so-called $\delta$ -adjustment method. Margarita Moreno-Betancur and Ian White kindly contributes the functions mice.impute.mnar.norm() and mice.impute.mnar.logreg(). These functions aid in performing sensitivity analysis to investigate the impact of different MNAR assumptions on the conclusion of the study. An alternative for MNAR is the older mice.impute.ri() function.
Installation of mice is faster. External packages needed for imputation and analyses are now installed on demand. The number of dependencies as estimated by rsconnect::appDepencies() decreased from 132 to 83.
The name clash with the complete() function of tidyr should no longer be a problem.
There is now a more flexible pool() function that integrates better with the broom and broom.mixed packages.

Bug fixes

Deprecates pool.compare(). Use D1() instead (#220)
Removes everything in utils::globalVariables()
Prevents name clashes with tidyr by defining complete.mids() as an S3 method for the tidyr::complete() generic (#212)
Extends the pool() function to deal with multiple sets of parameters. Currently supported keywords are: term (all broom functions), component (some broom.mixed functions) and y.values (for multinom() model) (#219)
Adds a new install.on.demand() function for lighter installation
Adds toenail2 and remove dependency on HSAUR3
Solves problem with ampute in extreme cases (#216)
Solves problem with pool with mgcv::gam (#218)
Adds .gitattributes for consistent line endings

Solves a bug that made polr() always fail (#206)
Aborts if one or more columns are a data.frame (#208)
Update mira-class documentation (#207)
Remove links to deprecated package CALIBERrfimpute
Adds check on partial missing level-2 data to 2lonly.norm and 2lonly.pmm
Change calculation of a2 to elementwise division by a matrix of observations
Extend documentation for 2lonly.norm and 2lonly.pmm
Repair return value from 2lonly.pmm
Imputation method 2lonly.mean now also works with factors
Replace deprecated imputationMethod argument in examples by method
More informative error message when stopped after pre-processing (#194)
Updated URL’s in DESCRIPTION
Fix string matching in check.predictorMatrix() (#191)

Copy toenail data from orphaned DPpackage package
Remove DPpackage from Suggests field in DESCRIPTION
Adds support for rotated names in md.pattern() (#170, #177)

This version has some error fixes
Fixes a bug in the sampler that ignored imputed values in variables outside the active block (#175, @alexanderrobitzsch)
Adds a note to the documenation of as.mids() (#173)
Removes a superfluous warning from process_mipo() (#92)
Fixes an error in the degrees of freedom of the P-value calculation (#171)

Add a hex sticker to the mice package. Designed by Jaden M. Walters.
Specify the R3.5.0 random generator in order to pass CRAN tests
Remove test-fix.coef.R from tests
Adds a rotate.names argument to md.pattern() (#154, #160)
Fix to solve the name-matching problem (#156, #149, #147)
Fix that removes the pre-check for existence of mice.impute.xxx() so that mice::mice() works as expected (#55)
Solves a bug that crashed mids2spss(), thanks Edgar Schoreit (#149)
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
Adds a routine contributed by Simon Grund that checks for deprecated arguments #137
Improves the nelsonaalen() function for data where variables time or status have already been defined (#140), thanks matthieu-faron

Solves bug in passive imputation (#130). Warning: This bug may have caused invalid imputations in mice 3.0.0 - mice 3.2.0 under passive imputation.
Updates code to broom 0.5.0 (#128)
Solves problem with mice.impute.2l.norm() (#129)
Use explicit foreign function calls in tests

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)

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)

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

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;
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;
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;
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.
Better integration with the tidyverse framework, especially for packages dplyr, tibble and broom;
Improved numerical algorithms for low-level imputation function. Better handling of duplicate variables.
Last but not least: A brand new edition AND online version of Flexible Imputation of Missing Data. Second Edition.

simplify code for mids object in mice (thanks stephematician) (#61)
simplify code in rbind.mids (thanks stephematician) (#59)
repair bug in pool.compare() in handling factors (#60)
fixed bug in rbind.mids in handling where (#59)
add new arguments to as.mids(), add as()
update contact info
resolved problem cart not accepting a matrix (thanks Joerg Drechsler)
Adds generalized pool() to list of models
Switch to 3-digit versioning
Date: 2017-12-08

Allow for capitals in imputation methods
Date: 2017-10-22

Reorganized vignettes to land on GitHUB pages
Date: 2017-10-21

Code changes for robustness, style and efficiency (Bernie Gray)
Date: 2017-10-18

Updates the ampute function and vignettes (Rianne Schouten)
Date: 2017-07-20

Rename mice.impute.2l.sys to mice.impute.2l.lmer
Date: 2017-07-11

Add new feature: whereargument to mice
Add new wy argument to imputation functions
Add mice.impute.2l.sys(), author Shahab Jolani
Update with many simplifications and code enhancements
Fixed broken cbind() function
Fixed Bug that made the pad element disappear from mids object
Date: 2017-07-10

Fixed integration with lattice package
Updates colors in xyplot.mads
Add support for factors in mice.impute.2lonly.pmm()
Create more robust version of as.mids()
Update of ampute() by Rianne Schouten
Fix timestamp problem by rebuilding vignette using R 3.4.0.
Date: 2017-07-07

Update to roxygen 6.0.1
Stylistic changes to mice function (thanks Ben Ogorek)
Calls to cbind.mids() replaced by calls to cbind()
Date: 2017-04-24

Add link to miceVignettes on github (thanks Gerko Vink)
Add package documentation
Add README for GitHub
Add new ampute functions and vignette (thanks Rianne Schouten)
Rename ccn –> ncc, icn –> nic
Change helpers cc(), ncc(), cci(), ic(), nic() and ici() use S3 dispatch
Change issues tracker on Github - add BugReports URL #21
Fixed multinom MaxNWts type fix in polyreg and polr #9
Fix checking of nested models in pool.compare #12
Fix as.mids if names not same as all columns #11
Fix extension for glmer models #5
Date: 2017-02-23

Add midastouch: predictive mean matching for small samples (thanks Philip Gaffert, Florian Meinfelder)
Date: 2016-10-05

Repaired dots problem in rpart call
Date: 2016-10-05

Add ridge to 2l.norm()
Remove .o files
Date: 2016-07-27

Fix as.mids() bug that crashed miceadds::mice.1chain()
Date: 2015-11-09

Update of example code on /doc
Remove lots of dependencies, general cleanup
Fix impute.polyreg() bug that bombed if there were no predictors (thanks Jan Graffelman)
Fix as.mids() bug that gave incorrect $m$ (several users)
Fix pool.compare() error for lmer object (thanks Claudio Bustos)
Fix error in mice.impute.2l.norm() if just one NA (thanks Jeroen Hoogland)
Date: 2015-11-04

Add about six times faster predictive mean matching
pool.scalar() now can do Barnard-Rubin adjustment
pool() now handles class lmerMod from the lme4 package
Added automatic bounds on donors in .pmm.match() for safety
Added donors argument to mice.impute.pmm() for increased visibility
Changes default number of trees in mice.impute.rf() from 100 to 10 (thanks Anoop Shah)
long2mids() deprecated. Use as.mids() instead
Put lattice back into DEPENDS to find generic xyplot() and friends
Fix error in 2lonly.pmm (thanks Alexander Robitzsch, Gerko Vink, Judith Godin)
Fix number of imputations in as.mids() (thanks Tommy Nyberg, Gerko Vink)
Fix colors to mdc() in example mice.impute.quadratic()
Fix error in mice.impute.rf() if just one NA (thanks Anoop Shah)
Fix error in summary.mipo() when names(x$qbar) equals NULL (thanks Aiko Kuhn)
Fix improper testing in ncol() in mice.impute.2lonly.mean()
Date: 2014-06-11

FIXED: compilation problem in match.cpp on solaris CC
Date: 02-05-2014 SvB

ADDED: experimental fastpmm() function using Rcpp
FIXED: fixes to mice.impute.cart() and mice.impute.rf() (thanks Anoop Shah)
Date: 02-02-2014 SvB

ADDED: mice.impute.rf() for random forest imputation (thanks Lisa Doove)
CHANGED: default number of donors in mice.impute.pmm() changed from 3 to 5. Use mice(…, donors = 3) to get the old behavior.
CHANGED: speedup in .norm.draw() by using crossprod() (thanks Alexander Robitzsch)
CHANGED: speedup in .imputation.level2() (thanks Alexander Robitzsch)
FIXED: define MASS, nnet, lattice as imports instead of depends
FIXED: proper handling of rare case in remove.lindep() that removed all predictors (thanks Jaap Brand)
Date: 21-01-2014 SvB

ADDED: as.mids() for converting long format in a mids object (thanks Gerko Vink)
FIXED: mice.impute.logreg.boot() now properly exported (thanks Suresh Pujar)
FIXED: two bugs in rbind.mids() (thanks Gerko Vink)
Date: 31-07-2013 SvB

ADDED: new form argument to mice() to specify imputation models using forms (contributed Ross Boylan)
FIXED: with.mids(), is.mids(), is.mira() and is.mipo() exported
FIXED: eliminated errors in the documentation of pool.scalar()
FIXED: error in mice.impute.ri() (thanks Shahab Jolani)
Date: 10-05-2013 SvB

ADDED: random indicator imputation by mice.impute.ri() for nonignorable models (thanks Shahab Jolani)
ADDED: workhorse functions .norm.draw() and .pmm.match() are exported
FIXED: bug in 2.14 and 2.15 in mice.impute.pmm() that produced an error on factors
FIXED: bug that crashed R when the class variable was incomplete (thanks Robert Long)
FIXED: bug in 2l.pan and 2l.norm by convert a class factor to integer (thanks Robert Long)
FIXED: warning eliminated caused by character variables (thanks Robert Long)
Date: 27-04-2013 SvB

CHANGED: complete reorganization of documentation and source files
ADDED: source published on GitHub.com
ADDED: new imputation method mice.impute.cart() (thanks Lisa Doove)
FIXED: calculation of degrees of freedom in pool.compare() (thanks Lorenz Uhlmann)
FIXED: error in DESCRIPTION file (thanks Kurt Hornik)
Date: 02-04-2013 SvB

ADDED: mice.impute.2l.mean() for imputing class means at level 2
ADDED: sampler(): new checks of degrees of freedom per variable at iteration 1
ADDED: function check.df() to throw a warning about low degrees of freedom
FIXED: tolower() added in “2l” test in sampler()
FIXED: conversion of factors that have other roles (multilevel) in padModel()
FIXED: family argument in call to glm() in glm.mids() (thanks Nicholas Horton)
FIXED: .norm.draw(): evading NaN imputed values by setting df in rchisq() to a minimum of 1
FIXED: bug in mice.df() that prevented the classic Rubin df calculation (thanks Jean-Batiste Pingaul)
FIXED: bug fixed in mice.impute.2l.norm() (thanks Robert Long)
CHANGED: faster .pmm.match2() from version 2.12 renamed to default .pmm.match()
Date: 11-03-2013 / SvB

ADDED: new multilevel functions 2l.pan(), 2lonly.norm(), 2lonly.pmm() (contributed by Alexander Robitzsch)
ADDED: new quadratic imputation function: quadratic() (contributed by Gerko Vink)
ADDED: pmm2(), five times faster than pmm()
ADDED: new argument data.init in mice() for initialization (suggested by Alexander Robitzsch)
ADDED: mice() now accepts pmm as method for (ordered) factors
ADDED: warning and a note to 2l.norm() that advises to use type=2 for the predictors
FIXED: bug that chrashed plot.mids() if there was only one incomplete variable (thanks Dennis Prangle)
FIXED: bug in sample() in .pmm.match() when donor=1 (thanks Alexander Robitzsch)
FIXED: bug in sample() in mice.impute.sample()
FIXED: fixed ‘?data’ bug in check.method()
REMOVED: wp.twin(). Now available from the AGD package
Date: 03-07-2012 / SvB

UPDATE: version for launch of Flexible Imputation of Missing Data (FIMD)
ADDED: code fimd1.r-fim9.r to inst/doc for calculating solutions in FIMD
FIXED: more robust version of supports.transparent() (thanks Brian Ripley)
ADDED: auxiliary functions ifdo(), long2mids(), appendbreak(), extractBS(), wp.twin()
ADDED: getfit() function
ADDED: datasets: tbc, potthoffroy, selfreport, walking, fdd, fdgs, pattern1-pattern4, mammalsleep
FIXED: as.mira() added to namespace
ADDED: functions flux(), fluxplot() and fico() for missing data patterns
ADDED: function nelsonaalen() for imputing survival data
CHANGED: rm.whitespace() shortened
FIXED: bug in pool() that crashed on nonstandard behavior of survreg() (thanks Erich Studerus)
CHANGED: pool() streamlined, warnings about incompatibility in lengths of coef() and vcov()
FIXED: mdc() bug that ignored transparent=FALSE argument, now made visible
FIXED: bug in md.pattern() for >32 variables (thanks Sascha Vieweg, Joshua Wiley)
Date: 25-03-2012 / SvB

UPDATE: definite reference to JSS paper
ADDED: rm.whitespace() to do string manipulation (thanks Gerko Vink)
ADDED: function mids2mplus() to export data to Mplus (thanks Gerko Vink)
CHANGED: plot.mids() changed into trellis version
ADDED: code used in JSS-paper
FIXED: bug in check.method() (thanks Gerko Vink)
Date: 21-11-2011 / SvB

FIXED: arguments dec and sep in mids2spss (thanks Nicole Haag)
FIXED: bug in keyword “monotone” in mice() (thanks Alain D)
Date: 14-09-2011 / SvB

FIXED: appropriate trimming of ynames and xnames in Trellis plots
FIXED: exported: spss2mids(), mice.impute.2L.norm()
ADDED: mice.impute.norm.predict(), mice.impute.norm.boot(), mice.impute.logreg.boot()
ADDED: supports.transparent() to detect whether .Device can do semi-transparent colors
FIXED: stringr package is now properly loaded
ADDED: trellis version of plot.mids()
ADDED: automatic semi-transparancy detection in mdc()
FIXED: documentation of mira class (thanks Sandro Tsang)
Date: 31-08-2011 / SvB

FIXED: bug fixed in find.collinear() that bombed when only one variable was left
Date: 24-03-2011 / SvB

CHANGED: check.data(), remove.lindep(): fully missing variables are imputed if allow.na=TRUE (Alexander Robitzsch)
FIXED: bug in check.data(). Now checks collinearity in predictors only (Alexander Robitzsch)
CHANGED: abbreviations of arguments eliminated to evade linux warnings
Date: 16-03-2011 / SvB

ADDED: bwplot(), stripplot(), densityplot() and xyplot() for creating Trellis graphs
ADDED: function mdc() and mice.theme() for graphical parameters
ADDED: argument passing from mice() to lower-level functions (requested by Juned Siddique)
FIXED: erroneous rgamma() replaced by rchisq() in .norm.draw, lowers variance a bit for small n
ADDED: with.mids() extended to handle expression objects
FIXED: reporting bug in summary.mipo()
CHANGED: df calculation in pool(), intervals may become slightly wider
ADDED: internal functions mice.df() and df.residual()
FIXED: error in rm calculation for “likelihood” in pool.compare()
CHANGED: default ridge parameter changed
Date: 03-03-2011 / SvB

ADDED: various stability enhancements and code clean-up
ADDED: find.collinear() function
CHANGED: automatic removal of constant and collinear variables
ADDED: ridge parameter in .norm.draw() and .norm.fix()
ADDED: mice.impute.polr() for ordered factors
FIXED: chainMean and chainVar in mice.mids()
FIXED: iteration counter for mice.mids and sampler()
ADDED: component ‘loggedEvents’ to mids-object for logging actions
REMOVED: annoying warnings about removed predictors
ADDED: updateLog() function
CHANGED: smarter handling of model setup in mice()
CHANGED: .pmm.match() now draws from the three closest donors
ADDED: mids2spss() for shipping a mids-object to SPSS
FIXED: change in summary.mipo() to work with as.mira()
ADDED: function mice.impute.2L.norm.noint()
ADDED: function as.mira()
FIXED: global assign() removed from mice.impute.polyreg()
FIXED: improved handling of factors by complete()
FIXED: improved labeling of nhanes2 data
Date: 06-01-2011 / SvB

ADDED: pool() now supports class ‘polr’ (Jean-Baptiste Pingault)
FIXED: solved problem in mice.impute.polyreg when one of the variables was named y or x
FIXED: remove.lindep: intercept prediction bug
ADDED: version() function
ADDED: cc(), cci() and ccn() convenience functions
Date: 17-10-2010 / SvB

FIXED: check.method: logicals are now treated as binary variables (Emmanuel Charpentier)
FIXED: complete: the NULL imputation case is now properly handled
FIXED: mice.impute.pmm: now creates between imputation variability for univariate predictor
FIXED: remove.lindep: returns ‘keep’ vector instead of data
Date: 14-02-2010 / SvB

ADDED: pool() now supports class ‘multinom’ (Jean-Baptiste Pingault)
FIXED: bug fixed in check.data for data consisting of two columns (Rogier Donders, Thomas Koepsell)
ADDED: new function remove.lindep() that removes predictors that are (almost) linearly dependent
FIXED: bug fixed in pool() that produced an (innocent) warning message (Qi Zheng)
Date: 13-01-2010 / SvB

ADDED: pool() now also supports class ‘mer’
CHANGED: nlme and lme4 are now only loaded if needed (by pool())
FIXED: bug fixed in mice.impute.polyreg() when there was one missing entry (Emmanuel Charpentier)
FIXED: bug fixed in plot.mids() when there was one missing entry (Emmanuel Charpentier)
CHANGED: NAMESPACE expanded to allow easy access to function code
FIXED: mice() can now find mice.impute.xxx() functions in the .GlobalEnv
Date: 14-09-2009 / SvB

Major upgrade for JSS manuscript
ADDED: new functions cbind.mids(), rbind.mids(), ibind()
ADDED: new argument in mice(): ‘post’ in post-processing imputations
ADDED: new functions: pool.scaler(), pool.compare(), pool.r.squared()
ADDED: new data: boys, popmis, windspeed
FIXED: function summary.mipo all(object$df) command fixed
REMOVED: data.frame.to.matrix replaced by the internal data.matrix function
ADDED: new imputation method mice.impute.2l.norm() for multilevel data
CHANGED: pool now works for any class having a vcov() method
ADDED: with.mids() provides a general complete-data analysis
ADDED: type checking in mice() to ensure appropriate imputation methods
ADDED: warning added in mice() for constant predictors
ADDED: prevention of perfect prediction in mice.impute.logreg() and mice.impute.polyreg()
CHANGED: mice.impute.norm.improper() changed into mice.impute.norm.nob()
REMOVED: mice.impute.polyreg2() deleted
ADDED: new ‘include’ argument in complete()
ADDED: support for the empty imputation method in mice()
ADDED: new function md.pairs()
ADDED: support for intercept imputation
ADDED: new function quickpred()
FIXED: plot.mids() bug fix when number of variables > 5
Date: 26-08-2009 / SvB, KO

FIXED: Stricter type checking on logicals in mice() to evade warnings.
CHANGED: Modernization of all help files.
FIXED: padModel: treatment changed to contr.treatment
CHANGED: Functions check.visitSequence, check.predictorMatrix, check.imputationMethod are now coded as local to mice()
FIXED: existsFunction in check.imputationMethod now works both under S-Plus and R
Date: 15/3/2009

FIXED: The impution function impute.logreg used convergence criteria that were too optimistic when fitting a GLM with glm.fit. Thanks to Ulrike Gromping.
Date: 6/25/2007

FIXED: In the lm.mids and glm.mids functions, parameters were not passed through to glm and lm.
Date: 01/09/2006

FIXED: Passive imputation works again. (Roel de Jong)
CHANGED: Random seed is now left alone, UNLESS the argument “seed” is specified. This means that unless you specify identical seed values, imputations of the same dataset will be different for multiple calls to mice. (Roel de Jong)
FIXED: (docs): Documentation for “impute.mean” (Roel de Jong)
FIXED: Function ‘summary.mids’ now works (Roel de Jong)
FIXED: Imputation function ‘impute.polyreg’ and ‘impute.lda’ should now work under R
Date: 9/26/2005

Changed function checkImputationMethod
Date: Feb 6, 2004

Maintainance, S-Plus 6.1 and R 1.8 unicode
Date: January 2004

R version (with help of Peter Malewski and Frank Harrell)
Date: Feb 2001

Original S-PLUS release
Date: June 14 2000

mice 3.18.02025-05-27

Major changes

Minor changes

Bug fixes

mice 3.17.02024-11-27

Major changes

Breaking changes

Minor changes

Bug fixes

mice 3.16.02023-06-05

Major changes

Minor changes

Bug fixes

mice 3.15.02022-11-19

Major changes

Minor changes

Bug fixes

mice 3.14.02021-11-24

Major changes

Minor changes

Bug fixes

mice 3.13.02021-01-27

Major changes

Minor changes

mice 3.12.02020-11-14

Major changes

Minor changes

mice 3.11.02020-08-05

Major changes

Minor changes

mice 3.10.02020-07-13

Major changes

Minor changes

mice 3.9.02020-05-14

Major changes

Minor changes

mice 3.8.02020-02-21

Major changes

Bug fixes

mice 3.7.02019-12-13

mice 3.6.02019-07-10

mice 3.5.02019-05-13

mice 3.4.02019-03-07

mice 3.3.02018-07-27

mice 3.2.02018-07-24

mice 3.1.02018-06-20

mice 3.0.02018-05-25

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 2.252015-11-09

mice 2.23

mice 2.222014-06-11

mice 2.212014-02-05

mice 2.202014-02-04

mice 2.19

mice 2.182013-08-01

mice 2.172013-05-12

mice 2.162013-04-27

mice 2.152013-04-03

mice 2.142013-03-19

mice 2.132012-07-04

mice 2.122012-03-25

mice 2.112011-11-22

mice 2.102011-09-15

mice 2.92011-09-01

mice 2.82011-03-26

mice 2.72011-03-16

mice 2.62011-03-04