CHANGES IN OPTMATCH VERSION 0.9-1
* Subsetting of optmatch objects now preserves (and subsets)
the subproblem attribute.
* Performance improvements for match_on applied to glm's.
* The solver update of version 0.9-0 had a bug that in some
circumstances caused hangups or malloc's [Issue #70]. We
believe this is now fixed -- but please notify maintainer if
you continue to experience the problem. (If you do, we'll
reward you with an easy workaround.)
CHANGES IN OPTMATCH VERSION 0.9-0
NEW FEATURES
* Solver limits now depend on machine limits, not arbitrary constants defined
by the optmatch maintainers. For large problems, users will see a warning,
but the solver will attempt to solve.
* fullmatch() and pairmatch() can now take distance generating arguments
directly, instead of having to first call match_on(). See the documentation
for these two functions for more details.
* Infeasibility recovery in fullmatch(). When passing a combination of
constraints (e.g. max.controls) that would make the matching infeasible,
fullmatch() will now attempt to find a feasible match that respects those
constraints, which will likely result in omitting some controls units.
* An additional argument to fullmatch(), mean.controls, is an alternative to
the previous omit.fraction. (Only one of the two arguments can be
presented.) The match will attempt to average mean.controls number of
controls per treatment.
* Each optmatch object now carries with it the constraints used to generate
it (e.g. max.controls) as well as a hashed version of the distance it
matched up, to help with some debugging/error checking but avoiding having
to carry the entire distance matrix around.
* Creating a distance matrix prior to matching is now optional. fullmatch()
now accepts arguments from which match_on() would create a distance, and
create the match behind the scenes.
* Performance enhancements for distance calculations.
* Several new utility functions, including subdim(), optmatch_restrictions(),
optmatch_same_distance(), num_eligible_matches(). See their help
documentation for additional details.
* Arithmetic operations between InfinitySparseMatrices and vectors are
supported. The operation is carried out as column by vector steps.
* scores() function allows including model predictions (such as propensity
scores) in formulas directly (such as combining multiple propensity scores).
The scores() function is preferred to predict() as it makes several smart
choices to avoid dropping observations due to partial missingness and other
useful preparations for matching.
BUG FIXES
* match_on is now a S3 generic function, which solves several bugs using
propensity models from other packages.
* summary() method was giving overly pessimistic warnings about failures.
* fixed bug in how optmatch objects were printing.
DEPRECATED AND DEFUNCT
* mdist() is now deprecated, in favor of match_on().
CHANGES IN OPTMATCH VERSION 0.8-3
* Changes to make examples compatible with PDF manual
CHANGES IN OPTMATCH VERSION 0.8-2
* full() and pair() are now aliases to fullmatch() and pairmatch()
* All match_on() methods take `caliper` arguments (formerly just the numeric
method and derived methods had this argument).
* boxplot methods for fitted propensity score methods (glm and bigglm)
* fill.NAs now takes `contrasts.arg` argument to mimic model.matrix()
* Several bug fixes in examples, documentation
* The methods pscore.dist() and mahal.dist() are now deprecated, with useful
error messages pointing users to replacements.
* Significant performance improvements for sparse matching problems.
* Functions umatched() and matched() were backwards. Corrected.
CHANGES IN OPTMATCH VERSION 0.8-1
* Several small bug fixes
CHANGES IN OPTMATCH VERSION 0.8-0
NEW FEATURES
* More efficient data structure for sparse matching problems, those with
relatively few allowed (finite) distances between units. Sparse problems
often arise when calipers are employed. The new data structure
(`InfinitySparseMatrix`) behaves like a simple matrix, allowing `cbind`,
`rbind`, and `subset` operations, making it easier to work with the older
`optmatch.dlist` data structure.
* match_on: A series of methods to generate matching problems using the new
data structure when appropriate, or using a standard matrix when the problem
is dense. This function is being deployed along side the `mdist` function to
provide complete backward compatibility. New development will focus on this
function for distance creation, and users are encouraged to use it right
away. One difference for `mdist` users is the `within` argument. This
argument takes an existing distance specification and limits the new
comparisons to only those pairs that have finite distances in the `within`
argument. See the `match_on`, `exactMatch`, and `caliper` documentation for
more details.
* exactMatch: A new function to create stratified matching problems (in which
cross strata matches are forbidden). Users can specify the strata using
either a factor vector or a convenient formula interface. The results can be
used in calls `match_on` to limit distance calculations to only with-in
strata treatment-control pairs.
* New `data` argument to `fullmatch` and `pairmatch`: This argument will set
the order of the match to that of the `row.names`, `names`, or contents of
the passed `data.frame` or `vector`. This avoids potential bugs caused when
the `optmatch` objects were in a different order than users' data.
* Test suite expanded and now uses the testthat library.
* fill.NAs allows (optionally) filling in all columns (previously, the first
column was assumed to be an outcome or treatment indicator and was not filled
in).
* New tools to find minimum feasible constraints: Large matching problems could
exceed the upper limit for a matching problem. The functions `minExactmatch`
and `maxCaliper` find the smallest interaction of potential factors for
stratified matchings or the largest (most generous) caliper, respectively,
that make the problem small enough to fit under the maximum problem size
limit. See the help pages for these functions for more information.
BUG FIXES
* Unmatched units are always NA (instead of being labeled "1.NA" or similar).
This avoids some obscure bugs when feeding the results of `fullmatch` to
other functions.
FOR A DETAILED CHANGELOG, SEE https://github.com/markmfredrickson/optmatch
CHANGES IN OPTMATCH VERSION 0.7-1
NEW FEATURES
* pairmatch() has a new option, "remove.unmatchables," that may be
useful in conjunction with caliper matching. With
"remove.unmatchables=TRUE", prior to matching any units with no
counterparts within caliper distance are removed. Pair matching can
still fail, if for example for two distinct treatment units only a
single control, the same one, is available for matching to them; but
remove.unmatchables eliminates one simple and common reason for pair
matching to fail.
* Applying summary() to an optmatch object now creates a
"summary.optmatch" containing the summary information, in addition
to reporting it to the console (via a summary.optmatch method for
print() ).
* mdist.formula() no longer requires an explicit data argument. I.e.,
you can get away with a call like "mdist(Treat~X1+X2|S)" if the
variables Treat, X1, X2 and S are available in the environment
you're working from (or in one of its parent environments).
Previously you would have had to do "mdist(Treat~X1+X2|S,
data=mydata)". (The latter formulation is still to be preferred,
however, in part because with it mdist() gets to use data's row
names, whereas otherwise it would have to make up row names.)
CHANGES IN OPTMATCH VERSION 0.7
NEW FEATURES
* New function fill.NAs replaces missing observations (ie. NA values)
with minimally informative values (ie. the mean of observed
columns). Fill.NAs handles functions in formulas intelligently and
provides missing indicators for each variable. See the help
documentation for more information and examples.
BUG FIXES
* mdist.function method now properly returns an optmatch.dlist object
for use in summary.optmatch, etc.
* mdist.function maintains label on grouping factor.
CHANGES IN OPTMATCH VERSION 0.6-1
NEW FEATURES
* New mdist method to extract propensity scores from models fitted
using bigglm in package "biglm".
* mdist's formula method now understands grouping factors indicated
with a pipe ("|")
* informative error message for mdist called on numeric vectors
* updated mdist documentation
CHANGES IN OPTMATCH VERSION 0.6
NEW FEATURES
* There is a new generic function, mdist(), for creating matching
distances. It accepts: fitted glm's, which it uses to extract
propensity distances; formulas, which it uses to construct squared
Mahalanobis distances; and functions, with which a user can construct
his or her own type of distance. The function method is more
intuitive to work with than the older makedist() function.
* A new function, caliper(), builds on the mdist() structure to
provide a convenient way to add calipers to a distance. In contrast
to earlier ways of adding calipers, caliper() has an optional
argument specify observations to be excluded from the caliper
requirement --- this permits one to relax it for just a few
observations, for instance.
* summary.optmatch() now removes strata in which matching failed (b/c
the matching problem was found to be infeasible) before summarizing.
It also indicates when such strata are present, and how many
observations fall in them.
* Demo has been updated to reflect changes as of version 0.4, 0.5, 0.6.
DEPRECATED & DEFUNCT
* The vignette is sufficiently out of date that it's been removed.
BUG FIXES
* subsetting of objects of class optmatch now preserves matched.distances attribute.
* fixed bug in maxControlsCap/minControlsCap whereby they behaved
unreliably on subclasses within which some subjects had no
permissible matches.
* Removed unnecessary panic in fullmatch when it was given a
min.controls argument with attributes other than names (as when it
is created by tapply()).
* fixed bug wherein summary.optmatch fails to retrieve balance tests
if given a propensity model that had function calls in its formula.
* Documentation pages for fullmatch, pairmatch filled out a bit.
CHANGES IN OPTMATCH VERSION 0.5
NEW FEATURES:
* summary.optmatch() completely revised. It now reports information
about the configuration of the matched sets and about matched
distances. In addition, if given a fitted propensity model as a
second argument it summarizes covariate balance.