CHANGES IN OPTMATCH VERSION 0.9-6
* New material in vignettes, on general use of the package and on
  import/export of matching results and material between R and SAS
  or Stata (Josh Errickson).
* New `summary` methods for InfinitySparseMatrix, BlockedInfinitySparseMatrix
  and DenseMatrix.  I.e., you can call `summary` on the result of a call to
  `match_on` or `caliper`.  The information this returns may be useful for
  selecting caliper widths, and for managing computational burdens with large
  matching problems.
* Streamlined combinations of exact and propensity score matching.
  If you include "+ strata(fac)" on the right hand side of a propensity
  scoring model formula, then pass the fitted model to `pairmatch()`,
  `fullmatch()` or `match_on`, then the factor "fac" will both serve as
  an independent variable for the propensity model and an exact matching
  variable (#101). See the examples on the help documentation for `fullmatch`.
* `pairmatch` and `fullmatch` no longer generate "matched.distances" attributes
   for their results. To get this information, use `matched.distances`.
* (Internal) methods for sorting of InfinitySparseMatrix's
* Deprecated: support passing the results of `fill.NAs` directly to `glm` or
  similar. Use the traditional formula and `data` argument version. See help
  documentation for `fill.NAs` for examples.
* Fixed: Rcpp incompatibilities for some OSX users (4bbcaca); `boxplot`
  method for fitted propensities ignoring varwidth argument (#113); various
  minor issues affecting package development and deployment (#110,...).


CHANGES IN OPTMATCH VERSION 0.9-5
* Documentation adjustments.
* Explicit print method for output from explicit calls to `stratumStructure`.

CHANGES IN OPTMATCH VERSION 0.9-4

* Significant speed up of math operations for sparse distance objects
  (by Josh Buckner).
* Introducing `contr.match_on`, a new default contrasts function for
  making Mahalanobis and Euclidean distances. Previously we used R
  defaults, which (a) generated different answers for the same factor
  depending on the ordering of the levels and (b) led to different
  distances for {0,1}-valued numeric variables and two level
  factors. (#80)
* match_on now takes strata as element of formula. Now users can write:
    match_on(z ~ x1 + x2 + strata(exactMatchVar))
  Instead of
    match_on(z ~ x1 + x2, within = exactMatch(z ~ exactMatchVar))
* Fixed bug giving spurious infeasibility warnings, sample size
  reductions when using `fullmatch` with feasible combinations of
  `min.controls`, `mean.controls`/`max.controls` and `max.controls`
  (#92)
* Various small bug fixes and documentation improvements.

CHANGES IN OPTMATCH VERSION 0.9-3

* Fixed memory issues, potential segfaults in solver code. (Thank
  you, Peter Solenberger).
* Fixed bug in dropping cases with extraneous NAs when using
  `fullmatch` or `pairmatch` to create distance specifications
  directly.
* Fixed bug (#83) in `glm` method for `match_on` that caused
  observations with fixable NAs to be dropped too often.
* New function `distUnion` allows combining arbitrary distance
  specifications.
* New function `antiExactMatch` provides for matches that may only
  occur between treated and control units with _different_ values on a
  factor variable. This is the opposite of `exactMatch`, which ensures
  matches occur within factor levels.
* Can now infer `data` argument in more cases when using the `summary`
  method when the RItools package is present.
* Additional warnings and clarifications.

CHANGES IN OPTMATCH VERSION 0.9-2

* Fixed issue #74 by properly setting the `omit.fraction` argument
  when there are unmatched controls.
* Improvements to the `minExactMatch` function.
* Added "optmatch_verbose_message" option to provide additional
  warnings.
* Fixed crash when all NULL or NA vectors passed as arguments to
  `fullmatch`.
* Added argument to `caliper` function that allows returning values
  that fit the caliper instead of just indicators of which entries fit
  the caliper width.
* Calipers widths can be given per-treated unit, instead of globally.
* Additional binary operators for sparse matrix representations.
* Added new ranked Mahalanobis method for the formula method of
  `match_on`.

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.
