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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 Polkas/miceFast#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 #32 (comment). 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)