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mice 3.16.0
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
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
mice 3.15.0
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
mice 3.14.0
Major changes
- Adds four new univariate functions using the lasso for automatic
variable selection:
mice.impute.lasso.norm() |
Lasso linear regression |
mice.impute.lasso.logreg() |
Lasso logistic regression |
mice.impute.lasso.select.norm() |
Lasso selector + linear regression |
mice.impute.lasso.select.logreg() |
Lasso selector + logistic regression |
Contributed by @EdoardoCostantini (#438).
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).
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)
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).
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)
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.
Minor changes
- 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)
mice 3.13.0
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)
mice 3.12.0
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
- 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.
Changes affecting
reproducibility
- 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
NA
s 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
mice 3.11.0
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).
- Adds missing
Rccp
dependency to work with
tidyr 1.1.1
(#248).
Minor changes
- 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
.
mice 3.10.0
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).
mice 3.9.0
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
mice 3.8.0
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
mice 3.7.0
- 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)
mice 3.6.0
- Copy
toenail
data from orphaned DPpackage
package
- Remove
DPpackage
from Suggests
field in
DESCRIPTION
- Adds support for rotated names in
md.pattern()
(#170,
#177)
mice 3.5.0
- 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)
mice 3.4.0
- 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
mice 3.3.0
- 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
mice 3.2.0
- 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)
mice 3.1.0
- 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 3.0.0
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.
mice 2.46.9 (2017-12-08)
- 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
mice 2.46 (2017-10-22)
- Allow for capitals in imputation methods
mice 2.45 (2017-10-21)
- Reorganized vignettes to land on GitHUB pages
mice 2.44 (2017-10-18)
- Code changes for robustness, style and efficiency (Bernie Gray)
mice 2.43 (2017-07-20)
- Updates the
ampute
function and vignettes (Rianne
Schouten)
mice 2.42 (2017-07-11)
- Rename
mice.impute.2l.sys
to
mice.impute.2l.lmer
mice 2.41 (2017-07-10)
- Add new feature:
where
argument 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
mice 2.40 (2017-07-07)
- 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.
mice 2.34 (2017-04-24)
- Update to roxygen 6.0.1
- Stylistic changes to
mice
function (thanks Ben
Ogorek)
- Calls to
cbind.mids()
replaced by calls to
cbind()
mice 2.31 (2017-02-23)
- 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
mice 2.29 (2016-10-05)
- Add
midastouch
: predictive mean matching for small
samples (thanks Philip Gaffert, Florian Meinfelder)
mice 2.28 (2016-10-05)
- Repaired dots problem in
rpart
call
mice 2.27 (2016-07-27)
- Add
ridge
to 2l.norm()
- Remove
.o
files
mice 2.25 (2015-11-09)
- Fix
as.mids()
bug that crashed
miceadds::mice.1chain()
mice 2.23 (2015-11-04)
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)
mice 2.22 (2014-06-11)
- 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()
mice 2.21 02-05-2014 SvB
- FIXED: compilation problem in match.cpp on solaris CC
mice 2.20 02-02-2014 SvB
- ADDED: experimental fastpmm() function using Rcpp
- FIXED: fixes to mice.impute.cart() and mice.impute.rf() (thanks
Anoop Shah)
mice 2.19 21-01-2014 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)
mice 2.18 31-07-2013 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)
mice 2.17 10-05-2013 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)
mice 2.16 27-04-2013 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)
mice 2.15 - 02-04-2013 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)
mice 2.14 - 11-03-2013 / 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()
mice 2.13 - 03-07-2012 / 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
mice 2.12 - 25-03-2012 / 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)
mice 2.11 - 21-11-2011 / 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)
mice 2.10 - 14-09-2011 / SvB
- FIXED: arguments dec and sep in mids2spss (thanks Nicole Haag)
- FIXED: bug in keyword “monotone” in mice() (thanks Alain D)
mice 2.9 - 31-08-2011 / 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)
mice 2.8 - 24-03-2011 / SvB
- FIXED: bug fixed in find.collinear() that bombed when only one
variable was left
mice 2.7 - 16-03-2011 / 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
mice 2.6 - 03-03-2011 / 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
mice 2.5 - 06-01-2011 / 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
mice 2.4 - 17-10-2010 / 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
mice 2.3 - 14-02-2010 / 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
mice 2.2 - 13-01-2010 / 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)
mice 2.1 - 14-09-2009 / 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
mice
2.0 - 26-08-2009 / SvB, KO 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
mice 1.21 -
15/3/2009 SvB Maintainance release
- 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
mice 1.16 - 6/25/2007
- FIXED: The impution function impute.logreg used convergence criteria
that were too optimistic when fitting a GLM with glm.fit. Thanks to
Ulrike Gromping.
mice 1.15 - 01/09/2006
- FIXED: In the lm.mids and glm.mids functions, parameters were not
passed through to glm and lm.
mice 1.14R - 9/26/2005 11:44AM
- 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
mice 1.13
- Changed function checkImputationMethod, Feb 6, 2004
mice 1.12
- Maintainance, S-Plus 6.1 and R 1.8 unicode, January 2004
mice 1.1
- R version (with help of Peter Malewski and Frank Harrell), Feb
2001
mice 1.0
- Original S-PLUS release, June 14 2000
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.