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Changelog

Source: NEWS.md

cobalt 5.0.0

  • New Features

    • Added support for assessing the balance of a censoring model. A treatment marked with the new .cens() function is a censoring indicator rather than a treatment, and bal.tab() compares the units still under observation, weighted, against the full at-risk sample; with un = TRUE it also reports that comparison before weighting, which is the imbalance the weights were estimated to remove. All the statistics available for binary treatments are available, the moment columns are named for the two samples (M.Uncensored, M.Full), and cluster, imp, and subclass all apply – subclassification is itself a way of solving a censoring problem. bal.plot() shows the weighted uncensored sample against the unweighted full sample. bal.tab() accepts a weightit object fit with a censoring model, a .cens(C) ~ x1 + x2 formula, or treat = .cens(C) with a data frame of covariates. cobalt::.cens() is deliberately identical to WeightIt::.cens() so that the same code works whichever package is attached; defines its own only to avoid depending on . See ?.cens and ?class-bal.tab.cens.

    • A censoring indicator can also appear among longitudinal treatments, as in bal.tab(list(A1 ~ x, .cens(C1) ~ x, A2 ~ x)), which is how a joint treatment-and-censoring model is written for WeightIt::weightitMSM(); such an object is now accepted directly, where it previously failed with Missing values must not exist in 'treat'. bal.tab() produces one table per entry, of whichever kind that entry’s model is, and assesses each among the units still under observation entering it. The risk set is accumulated from the censoring indicators themselves rather than from where treatments happen to be missing, so it makes no difference whether the data records a treatment for a unit that has already dropped out or leaves it blank; a missing treatment for a unit that is still under observation is now an error naming the time point it appeared in. A list that mixes censoring with treatment gets no balance summary across time points, as a list mixing continuous and binary treatments already does not. bal.plot() supports this too, showing whichever comparison the requested time point is about. See ?class-bal.tab.msm.

    • Added as.data.frame() and format() methods for bal.tab objects, to make it easier to report balance in a document. as.data.frame() returns the balance statistics as a tidy data frame, one row per covariate, sample, and statistic; segmented data (by cluster, imputation, treatment pair, time point, or subclass) put each level of segmentation in a column rather than a nested list, so the result is a single rectangle whatever the shape of the input. format() returns the balance table exactly as print() displays it, as a data frame of formatted strings, so knitr::kable(format(b)) produces a publication-ready table with no further processing. Both accept every argument print() accepts and resolve them the same way. The group column of the tidy layout names the treatment group a mean or a standard deviation describes – the group’s own name, not the position its column takes in the balance table – and is NA for a statistic that contrasts two groups. A continuous treatment has no groups, so its moments are reported for "All". Because a group’s own moments are the same in every pair of a multi-category treatment that group appears in, they are reported once, with no pair attached; only the contrasts carry a pair. The threshold and threshold.value columns are carried only when a threshold is on display for at least one statistic, since they would otherwise be empty throughout. See ?extract.bal.tab and the FAQ vignette.

    • bal.tab() now supports clustered longitudinal treatments. Previously, supplying cluster with a list of formulas or data frames failed with an uninformative error.

    • bal.tab() now supports clustered subclassification, with the subclass tables nested within each cluster, as is already done for multi-category treatments and imputations. Previously, supplying both subclass and cluster silently ignored cluster.

    • var.names, which supplies alternate names for the variables on display, is no longer confined to love.plot(). It can be given to bal.tab(), in which case it applies to everything that displays the resulting object – print(), format(), as.data.frame(), and love.plot() – so a set of readable names need be settled on only once. Each of those still takes a var.names of its own, which adds to the one bal.tab() was given and replaces any entry it names. It is specified the same way everywhere, including the shorthand by which a name given for a base variable reaches the factor levels, polynomials, and interactions it appears in, and it reaches the greatest-imbalance table along with the balance table. Only the names on display change: the variables are still stored, and still selected, under their own names, so var.names() continues to report those. Giving two variables the same name is now an error rather than silently drawing them on top of each other. Its documentation has moved to ?display-options, where the other display options are described, from ?love.plot, which now links there.

    • factor_sep and int_sep are now taken in the same places var.names is: given to bal.tab() they apply to everything that displays the object afterwards, and given to print(), format(), as.data.frame(), or love.plot() they apply there, replacing the one in force. love.plot(b, factor_sep = ": ") therefore relabels the plot without the object having to be recomputed. A bal.tab object records what each name is made of, so the separators are changed by reassembling the names rather than by editing the strings, and a var.names replacement is still keyed by the name the covariate is stored under whichever separators are on display.

    • bal.tab() now supports subclassification with continuous treatments, which previously failed with “subclasses are not yet compatible with continuous treatments”. The output has the same shape as for a binary treatment: a balance table for each subclass, a balance summary across subclasses, and the subclass sample sizes. Because subclassification cannot be written as a set of weights for a continuous treatment, the summary across subclasses combines the subclass-specific statistics directly, weighting each subclass by its share of the subclassified units; see ?class-bal.tab.subclass. bal.plot() already supported this case.

  • Changes

    • var.names() now reports the names a bal.tab object is displayed under rather than always the stored ones, so a set of names given to bal.tab() comes back out to be edited instead of written out again. The old column, and the names of the vector, are the stored names as before – they are what a replacement is resolved against – and only the new column, and the values of the vector, have changed. With no var.names applied the two agree, as they always did. var.names() also now reports a covariate that appears at only some time points of a longitudinal treatment, which it previously omitted because it read the names of the first time point alone.

    • The Observations component of a bal.tab object for a longitudinal treatment is now always present, where previously it was computed only when the balance summary across time points was. It is still displayed only alongside that summary, since it gathers in one place what each time point’s own table has already reported.

    • The divider naming each segment of a segmented bal.tab – a cluster, an imputation, a subclass, a treatment pair, a time point – is now the label centered in a rule, the form WeightIt::summary.weightitMSM() uses, in place of - - - Label - - -. An unlabeled rule of the same width closes the set when a balance summary across the segments follows it, so that the summary is not read as another part of the last segment. Its width responds to what it introduces: every divider in one set is drawn to the width of the widest line any of those segments prints, which is normally the balance table, and never narrower than 44 characters or than the label itself. The headings within a segment (Balance Measures, Sample sizes, and the rest) are unchanged. Only the presentation changed; the objects bal.tab() returns are untouched.

    • Time points are now named for their position in the list, whether the model there is a treatment or a censoring model, and the variable modeled, as in 1. Treatment: A_1 or 2. Censoring: C_1. This is the form WeightIt::summary.weightitMSM() uses, so the same model goes by the same name in both packages. It replaces Time 1 wherever bal.tab() and bal.plot() identify a time point. which.time is unchanged and still takes a position or a variable name.

    • bal.tab() accepts a weightitMSM object fit with censoring in either of the two shapes WeightIt has returned one: censoring models held among the treatment models, as in WeightIt 2.1.0, or segregated into their own cens.list and cens.covs.list, as in the development versions between 2.0.0 and 2.1.0.

    • bal.tab() now supports multiply imputed data supplied in stacked form (an imp longer than the data) with continuous longitudinal treatments, which previously failed with “treat.list can only contain vectors or data frames”.

    • A processed treatment is now an object of class treat, documented at ?treat-class, carrying its treatment type, its name, the names and values of its groups, and how those groups label the columns of a balance table. [ preserves all of it, so a subset of a treatment is still one. This is the same class WeightIt::.cens() returns, so an indicator tagged by either package is accepted by both.

    • bal.tab() now honors s.d.denom with longitudinal treatments. Each time point’s standardization factor was previously overwritten with the one the ATE implies – "pooled" for binary and multi-category treatments, "all" for continuous ones – so any value supplied to s.d.denom was silently discarded, and an unusable value went unreported. That value is still the default, since longitudinal treatments target the ATE, so results are unchanged unless s.d.denom was supplied.

    • Errors arising within a single cluster or imputation are now consistently labeled with that cluster or imputation. Previously, errors raised while subsetting the data escaped unlabeled.

    • Improvements to condition messages.

    • Greatly expanded the test suite, including tests for ps and iptw objects fit with version = "xgboost".

  • Bug fixes

    • set.cobalt.options() accepted factor_sep and int_sep, but nothing read them, so setting one had no effect. They are now honored.

    • Fixed a bug in which a longitudinal treatment combined with cluster or imp took its default s.d.denom from the wrapper rather than from the treatment, so a continuous longitudinal treatment with clusters or imputations failed with an error naming "pooled" as an unusable value.

    • Fixed a bug in which bal.plot() silently ignored s.weights for longitudinal treatments, on account of a misspelled name.

    • Fixed a bug in which which.time given as a treatment name selected the wrong time point in bal.plot() when the plotted variable did not appear at every time point.

    • Fixed a bug in which supplying the covariates once alongside an imp covering several imputations produced a balance table with no rows in it. Anything given for a single imputation is replicated up to the full data, and the replication dropped the record of what each column of the covariate matrix was, leaving a matrix that read as having no covariates at all. Sample sizes were reported correctly throughout, so the loss was easy to miss.

    • Fixed a bug in which a covariate set left with no columns – which happens when the only covariates supplied say nothing about the treatment, such as a factor that takes a single value within every cluster – was not cut down along with everything else when the data was split by cluster or imputation, producing a stream of longer object length is not a multiple of shorter object length warnings.

    • Errors raised for one time point of a longitudinal treatment now say which time point they came from, as they already did for clusters and imputations.

    • Fixed a bug in which a formula whose response variable does not exist, as in bal.tab(nope ~ age, data = lalonde), failed with R’s own object 'nope' not found rather than saying which argument was at fault. Whether the response exists was decided from the class of the error raised by evaluating it, and R-devel now gives an unbound name an objectNotFoundError rather than a simpleError; on that version of R every such formula took the path meant for a response that exists but fails to evaluate. It is decided by looking for the binding instead, which does not depend on how R classes or words the error.

    • Fixed a bug in the sample size table for subclassification in which the Total entry of the Discarded column reported the total number of units rather than the number discarded.

    • Fixed a bug in which printing a bal.tab object for a multi-category treatment computed with quick = FALSE failed with 'names' attribute [7] must be the same length as the vector [3].

    • Fixed a bug in which narrowing cluster.fun or imp.fun in print() failed with undefined columns selected when the original call to bal.tab() had requested more than one of them. For example, bal.tab(..., cluster.fun = c("min", "mean", "max")) followed by print(., cluster.fun = "max") now displays the maximum alone, as documented.

    • Fixed a bug in which requesting cluster.summary = TRUE with clustered, subclassified data failed with missing value where TRUE/FALSE needed. A subclassified cluster has no single balance table to summarize across clusters, so the summary is now omitted, as it already is with multiply imputed data.

    • set.cobalt.options() now accepts any statistic for stats. Previously only "mean.diffs" was allowed, even though getOption("cobalt_stats") was honored for all of them, so the option could not be set to anything else.

    • The per-statistic display options (disp.diff, disp.v.ratio, disp.ks, disp.ovl, disp.corr, disp.spear, and disp.dcorr) now take effect when set with set.cobalt.options(). disp.v.ratio and disp.ks were previously accepted but never read; the rest were rejected. The full set is now generated from the statistic registry, and the vestigial target.summary option, which was never read, has been removed.

    • bal.tab() now warns when estimand is not one of "ATT", "ATC", "ATE", "ATO", or "ATM". Previously an unrecognized value was silently ignored, so a typo such as estimand = "ATTT" quietly produced the pooled standardization factor used for the ATE. The value is still ignored rather than treated as an error, and supplying "ATT" or "ATC" with a multi-category treatment remains silent, since the focal group determines the denominator in that case.

    • bal.init() now treats estimand = "ATC" with a multi-category treatment identically to estimand = "ATT", as documented: focal names the group every other group is compared against, and is required. The two were previously handled by separate branches that could disagree about what to do when focal was omitted.

    • The error bal.plot() raises when var.name names one level of a factor rather than the factor itself now says which variable to supply instead. Splitting a factor into dummies is how bal.tab() summarizes it one level at a time, so a name like race_black appears in the balance table; bal.plot() plots the factor, so the dummy’s name is still rejected.

    • Fixed a bug in which supplying imp as the name of a variable in data failed for ps objects, though it worked for every other supported input.

    • Fixed a bug in which treatATT, a documented alias for focal in bal.tab()’s default method, was silently ignored.

    • The warning raised when a threshold falls outside its allowable range now names the argument (e.g., ks.threshold) rather than printing the internal expression used to look it up.

    • Fixed inconsistencies in the sample size table in how discarded units are counted. A discarded unit now contributes only to the Discarded row, so Matched, Unmatched, and Discarded always sum to All; previously, for binary treatments, a discarded unit with a nonzero weight was counted as matched as well as discarded. Relatedly, the effective sample size of a weighted or matched sample now always excludes discarded units and always uses the product of weights and s.weights; for continuous treatments with more than one set of weights, s.weights were previously ignored when method = "matching". These only differ from the previous values when a discarded unit has a nonzero weight, which does not arise for any object bal.tab() accepts.

    • Fixed a bug in love.plot() in which a bal.tab object that did not include the default statistic ("mean.diffs" for binary and multi-category treatments, "correlations" for continuous treatments) could not be plotted at all: love.plot(bal.tab(..., stats = "ks.statistics")) failed with an error saying "mean.diffs" was not requested. love.plot() now falls back to a statistic that was computed. When the default statistic is available it is still used, so plots of objects that include it are unchanged.

    • Fixed a bug in print() for bal.tab objects with subclasses in which supplying both disp.thresholds and disp.call failed with an uninformative error.

    • Fixed a bug in the per-subclass balance tables in which every statistic was compared to its threshold using its absolute value, even when the statistic defines a different one. The across-subclass summary table was already correct.

    • Fixed a bug in bal.compute() and bal.init() in which stat = "r2" failed for all inputs. The same underlying problem affected bal.tab() when int = TRUE was supplied with a single covariate or when poly was greater than 1 and all covariates were binary.

    • Fixed a bug in bal.compute() and bal.init() in which the Spearman correlation statistics (stat = "s.mean", "s.max", or "s.rms") failed for all inputs.

    • Fixed a bug in col_w_smd(), col_w_cov(), col_w_corr(), col_w_dcov(), and col_w_dcorr() in which s.weights were applied twice to the standardization factor when s.d.denom = "weighted" and weighted.weights was left at its default. This made the resulting standardized statistics disagree with the corresponding values from bal.tab(), which were correct. Supplying weighted.weights explicitly was unaffected, as was the case where s.weights were absent.

    • Fixed a bug in col_w_dcov() and col_w_dcorr() in which supplying a mat with more than one column failed unless std was given a value for each column.

    • Fixed a bug in col_w_ovl() in which the steps argument was ignored. steps is now also validated when integrate = TRUE, since it is used if integrate() fails and the Riemann sum is used as a fallback.

    • Fixed a bug in which print() failed on a bal.init object with stat = "r2.2" or "r2.3".

    • Fixed a bug in bal.plot() in which supplying subclass along with which = "both" failed when a subset of subclasses was requested, and dropped the sampling weights from the unadjusted sample otherwise.

    • Fixed a bug in bal.plot() in which longitudinal treatments failed unless which was supplied explicitly.

    • Fixed a bug in get.w() for ps objects in which estimand = "ATC" was ignored and the weights stored in the object were returned instead.

    • Fixed a bug in bal.tab()’s default method in which a distance measure supplied as a component of the input object (using any of distance, ps, pscore, p.score, or propensity.score) was silently dropped. This also affected longitudinal treatments processed through the default method.

    • love.plot() now gives an informative error when stats names a statistic that was not requested in the original call to bal.tab(), matching the behavior of print(). Previously this failed with an uninformative error about differing numbers of rows.

    • For ps and iptw objects fit with version = "xgboost", bal.tab() now asks for formula/covs (or formula.list/covs.list) when the fitted model records no feature names at all, rather than failing with an uninformative error from reformulate(). This is the case for models fit with xgboost 3.0.0 or later, which no longer store feature names in the model object.

    • Fixed a bug in splitfactor() in which supplying check = FALSE along with a mix of valid and invalid variable names failed to produce the intended warning.

    • col_w_mean() and col_w_sd() now reject an array with more than two dimensions rather than silently collapsing it into a single column, consistent with the other col_w_*() functions.

    • Fixed several bugs in the display arguments of print() for bal.tab objects:

      • disp was ignored, and produced a spurious warning, whenever the requested quantities had in fact been computed. The subclass method was unaffected.

      • disp.means, disp.sds, and the per-statistic disp.<stat> arguments could only turn a column on, never off. Setting disp.ks = FALSE, for example, displayed the KS statistics rather than hiding them.

      • Supplying disp.thresholds with a name that is not among the object’s thresholds warned and then failed with an error about attribute lengths.

      • A which.subclass containing no valid subclass index warned and then failed rather than displaying no subclasses.

    • Fixed a bug in which a bal.tab object with subclasses and quick = FALSE could not be printed at all, failing with “undefined columns selected”. The columns of the across-subclass table are now selected by name rather than by position, which cannot fall out of step with the table’s width.

    • Fixed a bug in which the sample sizes for longitudinal treatments never collapsed the “All (ESS)” and “All (Unweighted)” rows when the two agreed, though the corresponding “Matched” rows did.

    • Fixed a bug in which bal.plot() failed for a longitudinal treatment combined with cluster, and in the ordering of facets when neither which nor imp is displayed.

    • Fixed bugs in unsplitfactor():

      • A dropped numeric level was not inferred for a data.frame that did not come from splitfactor() (e.g. one built with model.matrix()), because the level was extracted one character too early and so never parsed as a number.

      • dropped.na could not be given as the name of the column holding the missingness indicator, as documented; it was rejected as a non-logical value.

    • The documentation for integrate in bal.compute() now correctly states that the default is TRUE; the documented default had been FALSE since the default changed in 4.6.0. The documentation for steps in col_w_ovl() now notes that it is also used when integrate() fails.

cobalt 4.6.3

CRAN release: 2026-05-30

  • arg is now used for errors and warning messages.

  • Documentation updates.

cobalt 4.6.2

CRAN release: 2026-01-29

  • Fixed documentation issue when a package isn’t available.

  • Converted all condition messages to use cli, which is now a dependency. crayon is no longer a dependency.

cobalt 4.6.1

CRAN release: 2025-08-20

  • Fixed a bug when using love.plot() with multiple stats supplied. Thanks to @jjharden for the report. (#93)

  • Fixed a bug in which the actual R version required was 4.4.0 due to the use of Tailcall(). This functionality has be rewritten to avoid Tailcall(). Thanks to Tanvi Shinkre.

  • Fixed bugs when using pairwise = FALSE with bal.compute().

cobalt 4.6.0

CRAN release: 2025-04-15

  • Added two new functions, col_w_dcov() and col_w_dcorr() for computing univariate (weighted) distance covariances and distance correlations between a treatment and covariates.

  • Changes to bal.compute() and bal.init():

    • "distance.cor" can be requested as an argument to stat to compute the weighted distance correlation between the treatment and the full covariate distribution.

    • "distance.cov" now returns the distance covariance; previously it returned the squared distance covariance. This should not affect the relative ordering of any sets of weights evaluated on this measure.

    • bal.compute() and bal.init() can now compute target balance statistics representing the similarity between a weighted sample and the same sample but unweighted, e.g., to ensure representativeness. This can be requested by omitting the treat argument to bal.compute() and bal.init(). Allowable target balance statistics include those based on the standardized mean difference, KS statistic, overlapping coefficient, Mahalanobis distance, and energy distance. See ?bal.compute or vignette("optimizing-balance") for details. available.stats() now accepts "target" as an argument to treat.type to display the available statistics for assessing target balance.

    • Fixed some bugs in computing the energy distance. Now, the complete energy distance is computed, including the constant term that does not depend on the weights. Previously, the constant term was omitted.

  • In col_w_ovl(), integrate is now TRUE by default to use integrate() to compute the overlapping coefficient. When integrate() fails, a midpoint Riemann sum will be used as a fallback.

  • Target OVL coefficients can be requested for continuous treatments by setting stats = "ovl" in bal.tab().

  • Fixed a bug when printing balance tables with longitudinal treatments that are of different types.

  • The legend title of love.plot() can be now be changed more easily. See ?love.plot for details. (#92)

  • Fixed a bug where supplying var.names to love.plot() with a modified bal.tab input would throw an error. (#89)

  • Added support for ps and iptw objects from twang when version = "xgboost" in the call to ps() or iptw().

  • Code cleaning and refactoring to improve performance.

cobalt 4.5.5

CRAN release: 2024-04-02

  • Minor updates to bal.plot() to prevent warnings due to ggplot2 3.5.0.

  • Improved processing when no covariates are specified.

  • Fixed a bug when multiple weights are specified, s.d.denom is specified, and either all variables are continuous and continuous = "raw", all variables are binary and binary = "raw", or both continuous = "raw" and binary = "raw".

  • Documentation updates

cobalt 4.5.4

CRAN release: 2024-02-27

  • Minor update to accommodate ggplot2 3.5.0. Thanks to @teunbrand. (#80)

  • bal.tab() no longer throws a note about s.d.denom when binary = "raw" and continuous = "raw" (i.e., only raw mean differences are requested).

  • col_w_smd() now correctly includes all data in computing the standardization factor for standardized mean differences when subset is supplied, consistent with the documentation. Previously, only the subsetted units were included in the standardization factor. This does not affect any results from bal.tab() or bal.compute(), which already used the correct units.

  • Added a new vignette for frequently asked questions, which describes in further detail why some choices were made. See vignette("faq").

cobalt 4.5.3

CRAN release: 2024-01-10

  • Fixed a bug when missing values were present in continuous covariates. Thanks to @vnusinfo. (#76)

  • Fixed a bug when using bal.tab() with the cluster argument supplied with the caret package loaded. Thanks to @BorgeJorge. (#77)

  • When cluster is specified, categorical variables that perfectly coincide with the cluster variable are now correctly removed.

  • Perfectly collinear variables are no longer removed (unless they are binary variables split from the same factor). This should speed up evaluation and reduce the probability of false positives being removed.

  • Variables with a single value are now more reliably categorized as “binary” in tables and calculations.

cobalt 4.5.2

CRAN release: 2023-11-20

  • Fixed a bug when using bal.compute() with a treatment variable with levels named “treated” and “control”.

  • Fixed a bug when using bal.tab() with mnps objects from twang. Thanks to @sherwinkuah. (#74)

  • Fixed a bug when using addl without a dataset supplied. (#71)

  • Fixed a bug when using subset to remove clusters lacking full representation in all treatment groups when cluster is specified. (#70)

cobalt 4.5.1

CRAN release: 2023-04-28

  • Added a new function available.stats() which lists the available balance statistics for use with bal.init() and bal.compute().

  • The interfaces to bal.compute() and bal.init() have changed slightly. The arguments have a slightly different order to match other cobalt functions. bal.compute() now is a generic function with a method for bal.init objects and a default method. The default method accepts the same arguments as bal.init() (and optionally an additional weights argument) and computes the (weighted) balance statistic directly. For most uses, the bal.init() |> bal.compute() workflow should be preferred.

  • Added a new multivariate balance statistic, the kernel distance as described by Zhu, Savage, and Ghosh (2018). This can be requested in bal.compute() and bal.init() by setting stat = "kernel.dist". In most cases, this will perform similarly to the energy distance.

  • s.weights are more compatible with matching methods and can now be supplied to bal.tab() with mimids and wimids objects from MatchThem. Thanks to Helen Wright for pointing out this issue.

  • Fixed a bug when using bal.init() with non-NULL s.weights.

  • Fixed bugs when using bal.init() and bal.compute() with multi-category treatments.

  • Fixed a bug when using col_w_ovl() with missing data.

  • Fixed a bug when using bal.tab() with stat = "spearman.correlations".

  • Fixed a bug when using bal.plot() with longitudinal treatments.

  • Fixed a bug in which the display options factor_sep and int_sep were not functioning correctly.

  • Fixed a bug when no covariates are supplied.

  • Improved some errors all around, and particularly in col_w_smd() and friends, bal.init(), and var.names().

cobalt 4.5.0

CRAN release: 2023-03-22

  • Added new functions bal.compute() and bal.init(), which are used for compute scalar balance statistics efficiently for use in optimizing balance. A new vignette, vignette("optimizing-balance") is available as well.

  • When focal is specified with multi-category treatments (by the user or implicitly by the supplied object), pairwise can be set to TRUE to request balance between each pair of treatment groups and to FALSE to request balance only between each non-focal group and the focal group. Previously only the behavior of setting pairwise to FALSE was supported. Now the default is for pairwise to be TRUE. To recover balance results for version prior to this one, set pairwise = FALSE with non-NULL focal.

  • With optmatch objects, the estimand argument can now be supplied to bal.tab(), etc., to control how the matching weights are computed from the subclass/pair membership. This is consistent with how get.w() uses the same argument.

  • Fixed a bug in which using . in formulas incorrectly included the treatment among the covariates.

  • Fixed a bug in which formulas supplied as character strings were not correctly interpreted as formulas. Thanks to @istallworthy.

  • Fixed a bug in which a spurious warning about dropping weights would occur when using bal.plot() with a density.

  • Documentation updates, including some new pages and the use of roxygen2.

cobalt 4.4.1

CRAN release: 2022-11-03

  • Fixed a bug when covariates with nonstandard names are extracted from model objects (#63). Thanks to @markdanese.

  • Fixed a bug when “0” and “1” are the names of two of the treatment levels in a multinomial treatment.

  • Fixed a bug with the default method of bal.tab() which was ignoring components of the supplied object.

  • Fixed a bug where bal.plot() would ignore s.weights. They are now included correctly.

  • The call to the original balancing function is now hidden by default. To request it be displayed, set disp.call = TRUE in the call to bal.tab() or print.bal.tab() or use set.cobalt.options(disp.call = TRUE) to display it for the session.

cobalt 4.4.0

CRAN release: 2022-08-15

  • Added support in bal.plot() for negative weights with type = "density".

  • Added support for ps.cont() objects from the twangContinuous package. ps.cont objects from WeightIt are no longer supported.

  • Major documentation overhaul. More arguments are explained at help("bal.tab") and a new package help page can be found at help("cobalt-package").

  • The function call is no longer included in the bal.tab() results for objects from twang.

  • Fixed a bug when some predictors were binary in some clusters and continuous in others. Variables now have a stable type across partitions.

  • Fixed a bug where binary variables were not being correctly processed when using the formula interface.

  • When using poly, orthogonal polynomials can be requested by setting orth = TRUE.

  • Improved appearance of conditional examples in pkgdown site.

  • Removed mlogit from Suggests.

  • Returned sbw to Suggests.

  • Updated the logo, thanks to Ben Stillerman.

cobalt 4.3.2

CRAN release: 2022-01-19

  • When pairwise = FALSE with binary or multi-category treatments, the balance statistics now refer to the difference between each group and the original full sample, unadjusted except possibly by s.weights. Previously, they referred to the difference between each group and the combined adjusted sample.

  • When subclassification is used and some units are discarded, bal.tab() now reports the number of discarded units along with with the number of units in each subclass in the sample sizes table.(#59)

  • Fixed several bugs when using love.plot() with subclassification that were caused by the last update. Thanks to Mario Lawes for pointing them out.

  • Fixed a bug in how get.w() computed weights for Match objects resulting from Matching::Match() with estimand = "ATE". Results now agree with Matching::MatchBalance().

  • Fixed a bug that would occur when using cobalt functions without attaching the package (e.g., cobalt::bal.tab()). (#53)

  • Fixed a bug that would occur with ordinal treatments.

  • Added better support for negative weights.

  • Fixed typos (#54, many identified and fixed by @jessecambon).

cobalt 4.3.1

CRAN release: 2021-03-30

  • Added support for objects from the new version of MatchThem.

  • Fixed a bug and improved speed when using match.strata.

cobalt 4.3.0

CRAN release: 2021-02-20

  • Returned cem to Suggests.

  • Added ability to display threshold summaries with multiply imputed datasets, clustered datasets, multi-category treatments, and longitudinal treatments.

  • Added pairwise argument for binary treatments. When set to FALSE, bal.tab() will display balance between each treatment group and the full sample (i.e., the target population). This functionality already existed for multi-category treatments; indeed, for binary treatments, it works by treating the treatment as multi-category.

  • Added two new stats options in bal.tab() and love.plot() for continuous treatments: "mean.diffs.target" (abbreviated as "m") and "ks.statistics.target" (abbreviated as "ks"). These compute (standardized) mean differences and KS statistics between the weighted and unweighted samples to ensure the weighted sample is representative of the original population. These statistics are only computed for the adjusted sample (i.e., they will not appear in the absence of adjustment).

  • With subclassification methods, the arguments which.subclass and subclass.summary have been added to display balance on individual subclasses and control output of the balance across subclasses summary. These arguments replace the disp.subclass argument, which can still be used.

  • When using bal.plot() with clustered or multiply imputed data, the which.cluster and which.imp arguments can be set to .none to display balance ignoring cluster membership and combining across imputations.

  • Changed processing of the print() method. Now there is only one print() method (print.bal.tab()) for all bal.tab objects. Processing is a little smoother and some printing bugs have been fixed. "as.is" can no longer be supplied to keep the print setting as-is; simply omit the corresponding argument to use the options as specified in the call to bal.tab().

  • An additional argument, disp.call, can be supplied to bal.tab() and print.bal.tab() to control printing of the call component of the input object, which contains the original function call. Set to FALSE to hide the call. This option is documented in ?display_options and can also be set using set.cobalt.options().

  • The balance table component of bal.tab objects is smaller because some extraneous columns are no longer produced. In particular, if no threshold is requested, no threshold columns will be produced. This does not affect display, but makes it easier to extract balance statistics from bal.tab objects (e.g., for exporting as a table). This does mean that previously saved bal.tab objects produced by earlier versions of cobalt will not be able to be printed correctly.

  • Fixed a bug where bal.plot() would incorrectly process 2-level factor variables (#48).

  • Fixed a bug where love.plot() would not display variables in the correct order when using aggregation and setting var.order = NULL. Thanks to Florian Kaiser.

  • Fixed a bug in love.plot() where the color of points could be incorrect.

  • Fixed a bug in love.plot() where samples were not always displayed in the right order. Now they are displayed in the same order they are in bal.tab().

  • Fixed a bug in love.plot() when the weight names had spaces in them.

  • Added an error message when not all clusters contain all treatment levels. Thanks to Rachel Visontay.

  • Fixed a bug when supplying the weights argument as a list of supported objects (e.g., weightit objects) if they were unnamed. Samples are more conveniently named.

  • Fixed a bug in col_w_mean(), col_w_smd(), and friends that occurred when few nonzero weights were present. Now an informative error is thrown.

  • Updates to documentation.

cobalt 4.2.4

CRAN release: 2020-11-05

  • Sampling weights now function correctly with subclassification.

  • Fixed a bug in print.bal.tab() when no units were unmatched but some were discarded.

  • Fixed an issue with the version number for gridExtra in DESCRIPTION. (#47)

  • cem removed from Suggests because it has been removed from CRAN.

  • Updated to support MatchIt 4.0.0, which includes sampling weights and improved processing of the covariates.

cobalt 4.2.3

CRAN release: 2020-08-31

  • Fixed bugs in processing functions in formulas, including rms functions and poly(). (#40)

  • Fixed a bug in how KS statistics were computed with col_w_ks(). Results now agree with those from MatchIt and twang.

  • Fixed bugs in processing small and partially empty subclasses.

  • In functions that compute weights from matching strata (e.g., get.w() for some types of objects), an estimand argument can be supplied to choose which formula is used to compute the weights. Subclass propensity scores are computed as the number of treated units in each subclass, and then stabilized weights are computed from those propensity scores using the standard formulas.

  • Effective sample sizes now print only up to two digits (believe me, you don’t need three) and print more cleanly with whole numbers.

cobalt 4.2.2

CRAN release: 2020-06-26

  • Fixed a bug due to new version of sbw.

  • Minor improvements to error messages and documentation.

cobalt 4.2.1

CRAN release: 2020-06-20

  • Fixed a bug where int and poly were ignored with binary and continuous treatments.

  • Fixed a bug where subclass balance statistics were incorrectly computed. Thanks to Mario Lawes.

  • Improved processing of inappropriately given S4 objects.

  • Removed bal.tab() methods for atomic vectors (which were undocumented). The errors they would provide when inappropriately supplied were unhelpful.

  • Fixed a bug with backports 1.1.7 not running correctly.

  • Fixed a bug with str2expression() for R versions below 3.6.0. Thanks to @kthohr and @jimmyg909.

  • When data is segmented (i.e., with a multi-category or longitudinal treatment or when clusters or multiple imputations are specified), the balance summary across segments will not be computed or displayed when individual segment balance is requested. See ?display_options to see the defaults for the different segment types, some of which have changed.

  • Updated some warnings.

cobalt 4.2.0

CRAN release: 2020-06-04

  • Added support for Matchby objects resulting from a call to Matchby() in the Matching package. These function identically to Match objects.

  • When using formula inputs, interaction terms (e.g., X1 * X2) will now correctly be resolved and displayed as an interaction term. This makes it easier to check balance on specific interactions rather than having to set int = TRUE or create a separate interaction variable in the data. Interaction terms specified in this way will be ignored by int and poly when they are used. When using var.names with love.plot(), changing the names of the base components of the interaction will also change their name in the interaction term, consistent with int behavior. If a formula was supplied in the input object to bal.tab() or other functions, terms in that formula will now also be included in balance reports.

  • Arguments to addl can now be specified as a one-sided formula (e.g., ~ X1 + X2 * X3). This makes it easy to take advantage of the above changes to the formula interface to add additional interaction terms. The formula will look at all available datasets in the conditioning object or supplied to bal.tab() and at the global environment. If supplying a single variable that exists in the global environment, it makes sense to supply it as a formula (e.g., addl = ~ X1) rather than as just the variable (e.g., addl = X1). Doing the former will retain the name of the variable. The same can be done with distance. If variables in addl are perfectly correlated with or have the same name as supplied covariates, those variables will be removed from addl.

  • If only one argument is provided to f.build() (e.g., f.build("x")), it will be treated as the right-hand-side of the formula with no left-hand-side (e.g., the above will evaluate to ~ x).

  • Fixed bug that caused match.strata input to be ignored.

  • Improved processing and error reporting when using the default bal.tab() method.

  • Speed improvements due to changes in how formulas are processed (now using model.matrix() directly rather than splitfactor() to process factors) and other small fixes. This is what enables the above changes to the formula capabilities.

  • Improved documentation for weightitMSM objects from WeightIt and CBMSM objects from CBPS.

  • Fixed bug when printing bal.tab objects with continuous treatments.

  • Fixed bug when using multi-category treatments with numbers as the level names.

  • Fixed bug when using mnps objects from twang with multiple stop methods.

  • Fixed bug when requesting means or standard deviations with segmented data.

  • Fixed bug where the x-axis in love.plot() was always “Standardized Mean Differences” even when it wasn’t supposed to be for stats = "mean.diffs".

  • rlang is now in IMPORTS.

  • General speed and stability improvements.

cobalt 4.1.0

CRAN release: 2020-04-11

  • Added support for sbwcau objects from sbw. See vignette("other-packages") or ?bal.tab.sbw for an example.

  • Added support for cem.match objects from cem. See vignette("other-packages") or ?bal.tab.cem.match for an example.

  • Added stats argument to bal.tab() and print() to replace disp.v.ratio and disp.ks. This argument functions similarly to how it does in love.plot(); for example, to request mean differences and variance ratios, one can enter stats = c("m", "v"). One consequence of this is that it is possible to request statistics that don’t include mean differences. See ?display_options for more details. The old arguments still work (and probably always will) but you should use stats instead. The goal here was to unify syntax across bal.tab(), print(), and love.plot(). A new help page specifically for the stats argument can be viewed at ?balance.stats.

  • Added thresholds argument to bal.tab() to replace m.threshold, v.threshold, etc. This argument functions similarly to how it does in love.plot(); for example, to request thresholds for mean differences and variance ratios, one can enter thresholds = c(m = .1, v = 2). The old arguments still work (and probably always will) but you should use thresholds instead. The goal here was to unify syntax across bal.tab(), print(), and love.plot().

  • Added disp.means option to bal.plot() to display the mean of the covariate as a line on density plots and histograms.

  • Added "hedges" as an option to s.d.denom. This will compute the standardized mean difference using the formula for the small sample-corrected Hedge’s G as described in the What Works Clearinghouse Procedures Handbook.

  • With multi-category treatments when pairwise = FALSE, rather than computing balance between each treatment group and the other treatment groups, balance is now computed between each treatment group and the entire sample.

  • In print(), the arguments disp.m.threshold, disp.v.threshold, disp.ks.threshold, and disp.r.threshold, which could be set to FALSE to prevent the corresponding balance thresholds and summaries from being printed, have been replaced with disp.thresholds. Named entries can be set to FALSE. The goal here was to unify syntax across bal.tab() and print().

  • A new balance statistic, the overlapping coefficient (OVL), is allowed with binary and multi-category treatments. This is described in Belitser et al. (2011) and Franklin et al. (2014) for assessing balance. Generally, for each covariate, the overlapping coefficient is the area of the probability density functions for each sample that overlap. Here I follow Franklin et al. (2014) and report 1 - (OVL) so that values close to zero indicate good balance (i.e., completely overlapping distributions) and values close to 1 indicate poor balance (i.e., completely non-overlapping distributions). To estimate and display the OVL, set include "ovl" in the stats argument in a call to bal.tab() or love.plot() (or you can use the old syntax by setting disp.ovl = TRUE). The balance threshold can be requested by including "ovl" in the thresholds argument (or you can use the old syntax by using the ovl.threshold argument).

  • Spearman correlations can be requested for continuous treatments by adding "sp" to the stats argument.

  • The argument weights can now be supplied to any bal.tab() call to request balance on additional weights beyond the weights from the object on which bal.tab() is called. This argument takes a named list, where each element is a vector of weights, the name of a variable containing weights in an available dataset, or an object with a get.w() method (e.g., the output of another preprocessing function). This should make it easier to compare balancing methods without having to specify the covariates and treatment using the formula or data.frame methods.

  • ggplot2 version 3.3.0 is required, which removes some warnings and makes it so ggstance doesn’t need to be imported.

  • When there are more than 900 variables to compute balance statistics on in bal.tab() (which can happen quickly when int = TRUE and categorical variables have many categories), to avoid major slowdowns, checks for redundancy of variables are forgone. This will dramatically increase the speed of bal.tab() in these scenarios. This option can be changed with the cobalt option "remove_perfect_col" which can be set to TRUE or or FALSE. Set to FALSE to improve speed at the expense of possibly having redundant variables appear.

  • Fixed a bug when using the default bal.tab() method with objects containing longitudinal treatments.

  • Fixed a bug when using bal.tab() with continuous treatments and clusters.

  • Fixed a bug in love.plot() when using subclassification.

  • Fixed a bug when using bal.tab() with longitudinal treatments and multiple sets of weights.

  • Fixed a bug when using col_w_ovl(). OVL values are now more accurate.

  • Speedups and other small fixes.

cobalt 4.0.0

CRAN release: 2020-01-08

Major Updates

  • Added support for mimids and wimids objects from MatchThem.

  • Major restructuring so that clusters, longitudinal treatments, multi-category treatments, and multiply imputed data can all be used with each other. These are layers in the following order: clusters, time points, treatment categories, and imputations. Summaries across these layers are handled slightly differently from how they used to be; importantly, summaries are not nested, and only the lowest layer present can have a summary. For example, if multiply imputed data is used with multi-category treatments, there will be a summary across imputations (the lowest layer) but not across treatment pairs. love.plot allows multiple forms of faceting and aggregating and is extremely flexible in this regard.

  • Major changes to appearance of bal.plot() to be more in line with love.plot(), including new grid and position options to control the presence of the grid and the position of the legend.

  • Formula interfaces now accept poly(x, .) and other matrix-generating functions of variables, including the rms-class-generating functions from the rms package (e.g., pol(), rcs(), etc.) (the rms package must be loaded to use these latter ones) and the basis-class-generating functions from the splines package (i.e., bs() and ns()). A bug in an early version of this was found by @ahinton-mmc.

Minor Updates and Bug Fixes

s.d.denom and estimand

  • s.d.denom can now use the name of a treatment rather than just "treated" or "control". In addition, s.d.denom can be "weighted" to use the weighted sample’s standardization factors, an option available for continuous treatments, too.

  • Improved guessing of the estimand when not provided.

  • Estimands besides ATT can now be used with subclasses. The estimand can be inferred from the provided subclasses. Works with match.strata as well, which function like subclasses. In addition, it is not always assumed that MatchIt objects are targeting the ATT, for example, with subclassification or calipers.

bal.plot

  • Added sample.names argument in bal.plot() in response to this post on Cross Validated.

  • Added functionality to the which argument in bal.plot(), allowing more specificity when multiple sets of weights are used.

  • Added type = "ecdf" option to bal.plot() for categorical treatments with continuous covariates to display empirical cumulative density plots as an alternative to density plots.

  • When using bal.plot() with continuous treatments and continuous covariates, the points are shaded based on their weights; this behavior is controlled by the new alpha.weight argument, which replaces the functionality of size.weight (which was kind of ugly and not very informative) and is TRUE by default. Now it’s more apparent which points are influential in the weighted sample. In addition, a line illustrating the unweighted covariate mean is present.

  • The default of the grid argument is now FALSE in bal.plot() and love.plot(). Previously it was TRUE. This make the plots cleaner at the outset.

Other improvements

  • Added new function col_w_cov() to compute treatment-covariate covariances (i.e., unstandardized correlations) for continuous treatments. continuous and binary can be set to "raw" in bal.tab() and std can be set to FALSE in col_w_cov() to request treatment-covariate covariances instead of correlations. col_w_corr() is now a wrapper for col_w_cov() with std = TRUE. To get more functionality out of the std argument (e.g., to standardize the covariances for some covariates but not others), use col_w_cov().

  • Balance summary functions (e.g., col_w_sd(), col_w_smd(), etc.) process binary variables slightly differently. If bin.vars is missing, the function will figure out which variables are binary. If NULL, it will be assumed no variables are binary. Entering values for bin.vars can be done more flexibly. When a factor variable is supplied as part of mat and is split internally by splitfactor(), extra values will be automatically added to bin.vars with the newly created dummies considered binary variables.

  • Bug fixes when binary factor treatments are used, thanks to Moaath Mustafa Ali.

  • bal.tab() no longer tells you whether it assumes matching or weighting when certain non-package-related methods are used.

  • Improvements to assessment of subclass balance. For binary treatments, balance statistics other than mean differences can now be requested. The across-subclass balance summary uses subclassification weights (processed in the same way match.strata is) instead of simply taking a weighted average across subclasses (which is not valid for non-additive statistics like variance ratios or KS statistics). For continuous treatments, a balance summary across subclasses can now be produced. This uses a weighted average of the subclass-specific balance statistics.

  • The default in love.plot() for abs is now to be whatever it is in the (implicit) call to bal.tab(), which is usually FALSE. Previously abs was not aligned between love.plot() and bal.tab().

  • s.weights can now be manually supplied to methods that usually come with their own sampling weights, such as twang and WeightIt.

  • Speedup of splitfactor().

  • splitfactor() now has a split.with option to split one or more vectors in concert with the data set being split.

  • splitfactor() and unsplitfactor() are a little smarter and more in sync.

  • All functions work better inside other functions like lapply() or purrr::map(), thanks to @the-Zian.

  • Updates to the vignettes; Appendix 2 is particularly different.

  • Other bug fixes and performance improvements here and there.

cobalt 3.9.0

CRAN release: 2019-10-06

  • Added vignette for use of love.plot.

  • Changed grid version requirement.

  • Updated README.

  • Fixed bugs that would occur when using love.plot() with various combinations of var.order, multiple stats, and agg.fun = "range".

  • Fixed bugs that would occur when using bal.tab() with objects from the Matching package. Calculated statistics are now the same as those generated using Matching::MatchBalance(). Changes based on updates to get.w.Match().

  • Added balance summary functions col_w_mean(), col_w_sd(), col_w_smd(), col_w_vr(), col_w_ks(), col_w_ovl(), and col_w_corr(). These make it easier to get quick, simple summaries of balance without calling bal.tab(), for example, for use in programming other functions. Some of these are now used inside bal.tab() to increase speed and simplify internal syntax.

  • Other small bug fixes.

cobalt 3.8.0

CRAN release: 2019-09-12

  • Added the ability to display balance on multiple measures (e.g., mean differences, variance ratios, KS statistics) at the same time with love.plot().

  • Bug fixes that make bal.tab() and love.plot() more usable within other functions and especially when called with do.call().

  • Made it easier to get proper bal.tab() output when using matchit() with an argument to distance (in the call to matchit()). Include the original dataset in the data argument of bal.tab() to get the variables to display correctly.

  • Changed the default shape in love.plot() to "circle", which is a solid circle. I found this a prettier alternative to the open circle, especially on Windows. To get back open circles you set shapes = "circle filled" (yes, that is a bit confusing).

  • Added ability to hide the gridlines easily in love.plot().

  • Changed the calculation of standard deviations (and standardized differences in proportion) for binary variables to be more in line with recommendations, as noted by @mbloechl05. Note this will make these values different from those in MatchIt::summary() by a small amount.

  • The KS statistic is now computed for binary variables. It is simply the difference in proportion.

  • Allowed some methods to accept mids objects (the output of a call to mice::mice()) in the data argument to supply multiply imputed data. This essentially replaces data = complete(imp.out, "long"), imp = ".imp" with data = imp.put, assuming imp.out is a mids object.

  • Other bug fixes and improvements.

cobalt 3.7.0

CRAN release: 2019-05-01

  • Changes to some bal.tab() defaults: quick is now set to TRUE by default. Adjusted and unadjusted means, standard deviations, and mean differences will always be computed, regardless of quick. Variance ratios and KS statistics will only be computed if quick = FALSE or disp.v.ratio or disp.ks, respectively, are TRUE.

  • Variance ratios now respond to abs. When abs = FALSE, the default in bal.tab(), the variance is ratio is the variance of the treated (1) divided by the variance of the control (0). When abs = TRUE, the numerator of the variance ratio is the larger variance and the denominator is the smaller variance, which was the old behavior. v.threshold still responds as if abs was set to TRUE, just like with mean differences. Any time variance ratios are aggregated (e.g., across imputations or clusters), the “mean” variance ratio is the geometric mean to account for the asymmetry in the ratios.

  • love.plot() has several changes that make it much more user-friendly. First, rather than supplying a bal.tab object to love.plot(), you can simply supply the arguments that would have gone into the bal.tab() call straight into love.plot(). Second, if quick = TRUE (the new default) and the first argument to love.plot() is a call to bal.tab() (or arguments provided to bal.tab()) and stat is set to "variance.ratios" or "ks.statistics", bal.tab() will be re-called with the corresponding disp argument set to TRUE so that love.plot() will display those statistics regardless of quick. This will not work if the argument supplied to love.plot() is a bal.tab object. Third, because unadjusted mean differences are computed regardless of quick, there will never be a circumstance in which only adjusted values will be displayed. If quick = TRUE, un = FALSE, and stat is "variance.ratios" or "ks.statistics", un will automatically be set to TRUE in the bal.tab() re-call.

  • When using which. arguments (e.g., which.cluster, which.imp, etc.), instead of supplying NULL and NA, you can supply .all and .none (not in quotes). This should make them easier to use. Note that these new inputs are not variables; they are keywords and are evaluated using nonstandard evaluation. If you actually have objects with those names, they will be ignored.

  • Bugs in scoping related to the formula interface have been solved, in particularly making bal.tab() more usable within other functions.

  • Fixed bug occurring when using matchit objects having set discard to something other than NULL and reestimate = TRUE in the call to matchit(). Thank you to Weiyi Xie for finding this bug.

  • Fixed bug occurring when using balance thresholds with subclassification.

  • Fixed bug occurring when printing bal.tab() output for continuous treatments with clusters.

  • Fixed bug occurring when using bal.tab() on mnps objects with multiple stop methods.

cobalt 3.6.1

CRAN release: 2019-01-16

  • Fixed bug when installed version of R was earlier than 3.5.0.

cobalt 3.6.0

CRAN release: 2018-11-25

  • Added poly argument to bal.tab() to display polynomials of continuous covariates (e.g., squares, cubes, etc.). This used to only be available with the int argument, which also displayed all interactions. Now, the polynomials can be requested separately. When int = TRUE, squares of the covariates will no longer be displayed; to replicate the old behavior, set int = 2, which is equivalent to int = TRUE, poly = 2.

  • Fixed a bug where using subset would produce an error.

  • Fixed a bug when using multiply imputed data with binary treatments that were factors or characters.

  • Updated the bal.tab() documentation to make it easier to navigate to the right page.

  • Small documentation and syntax updates.

  • Added the hidden and undocumented argument center to bal.tab(), which, when set to TRUE, centers the covariates at the mean of the entire unadjusted sample prior to computing interactions and polynomials.

  • Added set.cobalt.options() function to more easily set the global options that can be used as defaults to some arguments. For example, set.global.options(binary = "std") makes it so that standardized mean difference are always displayed for binary covariates (in the present R session). The options can be retrieved with get.cobalt.options().

cobalt 3.5.0

CRAN release: 2018-10-25

  • Several changes to bal.tab() display options (i.e., imbalanced.only, un, disp.means, disp.v.ratio, disp.ks, disp.bal.tab, disp.subclass, and parameters related to the display of balance tables with multinomial treatments, clusters, multiple imputations, and longitudinal treatments). First, the named arguments have been removed from the method-specific functions in order to clean them up and make it easier to add new functions, but they are still available to be specified. Second, a help page devoted just to these functions has been created, which can be accessed with ?options-display. Third, global options for these arguments can be set with options() so they don’t need to be typed each time. For example, if you wanted un = TRUE all the time, you could set options(cobalt_un = TRUE) once and not have to include it in the call to bal.tab().

  • Added disp.sds option to display standard deviations for each group in bal.tab(). This works in all the same places disp.means does.

  • Added cluster.fun and imp.fun options to request that only certain functions (e.g., mean or maximum) of the balance statistics are displayed in the summary across clusters/imputations. Previously this option was only available by call print(). These parameters are part of the display options described above, so they are documented in ?options-display and not in the bal.tab() help files.

  • Added factor_sep and int_sep options to change the separators between variable names when factor variables and interactions are displayed. This functionality had been available since version 3.4.0 but was not documented. It is now documented in the new display_options help page.

  • In bal.tab(), continuous and binary can be specified with the global options "cobalt_continuous" and "cobalt_binary", respectively, so that a global setting (e.g., to set binary = "std" to view standardized mean difference rather than raw differences in proportion for binary variables) can be used instead of specifying the argument each time in the call to bal.tab().

  • Minor updates to f.build() to process inputs more flexibly. The left hand side can now be empty, and the variables on the right hand side can now contain spaces.

  • Fixed a bug when logical treatments were used. Thanks to @victorn1.

  • Fixed a bug that would occur when a variable had only one value. Thanks to @victorn1.

  • Made it so the names of 0/1 and logical variables are not printed with "_1" appended to them. Thanks to @victorn1 for the suggestion.

  • Major updates to the organization of the code and help files. Certain functions have simplified syntax, relying more on ..., and help pages have been shorted and consolidated for some methods. In particular, the code and help documents for the Matching, optmatch, ebal, and designmatch methods of bal.tab() have been consolidated since they all rely on exactly the same syntax.

cobalt 3.4.1

CRAN release: 2018-09-15

  • Fixed a bug that would occur when imabalanced.only = TRUE in bal.tab() but all variables were balanced.

  • Fixed a bug where the mean of a binary variable would be displayed as 1 minus its mean.

  • Fixed a bug that would occur when missingness patterns were the same for multiple variables.

  • Fixed a bug that would occur when a distance measure was to be assessed with bal.tab() and there were missing values in the covariates (thanks to Laura Helmkamp).

  • Fixed a bug that would occur when estimand was supplied by the user when using the default method of bal.tab().

  • Fixed a bug where non-standard variable names (like "I(age^2)") would cause an error.

  • Fixed a bug where treatment levels that had different numbers of characters would yield an error.

  • Added disp.means option to bal.tab() with continuous treatments.

cobalt 3.4.0

CRAN release: 2018-08-14

  • Added default method for bal.tab() so it can be used with specially formatted output from other packages (e.g., from optweight). bal.plot() should work with these outputs too. This, of course, will never be completely bug-free because infinite inputs are possible and cannot all be processed perfectly. Don’t try to break this function :)

  • Fixed some bugs occurring when standardized mean differences are not finite, thanks to Noémie Kiefer.

  • Speed improvements in bal.plot(), especially with multiple facets, and in bal.tab().

  • Added new options to bal.plot(), including the ability to display histograms rather than densities and mirrored rather than overlapping plots. This makes it possible to make the popular mirrored histogram plot for propensity scores. In addition, it’s now easier to change the colors of the components of the plots.

  • Made behavior around binary variables with interactions more like documentation, where interactions with both levels of the variable are present (thanks to @victorn1). Also, replaced _ with * as the delimiter between variable names in interactions. For the old behavior, use int_sep = "_" in bal.tab.

  • Expanded the flexibility of var.names in love.plot() so that replacing the name of a variable will replace it everywhere it appears, including interactions. Thanks to @victorn1 for the suggestion.

  • Added var.names function to extract and save variable names from bal.tab objects. This makes it a lot easier to create replacement names for use in love.plot(). Thanks to @victorn1 for the suggestion.

  • When weighted correlations are computed for continuous treatments, the denominator of the correlation now uses the unweighted standard deviations. See ?bal.tab for the rationale.

cobalt 3.3.0

CRAN release: 2018-06-24

  • Added methods for objects from the designmatch package.

  • Added methods for ps.cont objects from the WeightIt package.

  • Fixed bugs resulting form changes to how formula inputs are handled.

  • Cleaned up some internal functions, also fixing some related bugs

  • Added subset option in all bal.tab() methods (and consequently in bal.plot()) that allows users to specify a subset of the data to assess balance on (i.e., instead of the whole data set). This provides a workaround for methods were the cluster option isn’t allowed (e.g., longitudinal treatments) but balance is desired on subsets of the data. However, in most cases, cluster with which.cluster specified makes more sense.

  • Updated help files, in particular, more clearly documenting methods for iptw objects from twang and CBMSM objects from CBPS.

  • Added pretty printing with crayon, inspired by Jacob Long’s jtools package

  • Added abs option to bal.tab to display absolute values of statistics, which can be especially helpful for aggregated output. This also affects how love.plot() handles aggregated balance statistics.

cobalt 3.2.3

CRAN release: 2018-05-04

  • Added support for data with missing covariates. bal.tab() will produce balance statistics for the non-missing values and will automatically create a new variable indicating whether the variable is missing or not and produce balance statistics on this variable as well.

  • Fixed a bug when displaying maximum imbalances with subclassification.

  • Fixed a bug where the unadjusted statistics were not displayed when using love.plot() with subclasses. (Thanks to Megha Joshi.)

  • Add the ability to display individual subclass balance using love.plot() with subclasses.

  • Under-the-hood changes to how weightit objects are handled.

  • Objects in the environment are now handled better by bal.tab() with the formula interface. The data argument is now optional if all variables in the formula exist in the environment.

cobalt 3.2.2

CRAN release: 2018-03-13

  • Fixed a bug when using get.w() (and bal.tab()) with mnps objects from twang with only one stop method.

  • Fixed a bug when using bal.tab() with twang objects that contained missing covariate values.

  • Fixed a bug when using int = TRUE in bal.tab() with few covariates.

  • Fixed a bug when variable names had special characters.

  • Added ability to check higher order polynomials by setting int to a number.

  • Changed behavior of bal.tab() with multinomial treatments and s.d.denom = "pooled" to use the pooled standard deviation from the entire sample, not just the paired treatments.

  • Restored some vignettes that required WeightIt.

cobalt 3.2.1

CRAN release: 2018-02-20

  • Edits to vignettes and help files to respond to missing packages. Some vignette items may not display if packages are (temporarily) unavailable.

  • Fixed issue with sampling weights in CBPS objects. (Thanks to @kkranker on Github.)

  • Added more support for sampling weights in get.w() and help files.

cobalt 3.2.0

CRAN release: 2018-01-17

  • Added support for longitudinal treatments in bal.tab(), bal.plot(), and love.plot(), including output from iptw() in twang, CBMSM() from CBPS, and weightitMSM() from WeightIt.

  • Added a vignette to explain use with longitudinal treatments.

  • Edits to help files.

  • Added ability to change density options in bal.plot().

  • Added support for imp in bal.tab() for weightit objects.

  • Fixed bugs when limited variables were present. (One found and fixed by @sumtxt on Github.)

  • Fixed bug with multiple methods when weights were entered as a list.

cobalt 3.1.0

CRAN release: 2017-11-12

  • Added full support for tibbles.

  • Examples for weightit methods in documentation and vignette now work.

  • Improved speed and performance.

  • Added pairwise option for bal.tab() with multinomial treatments.

  • Increased flexibility for displaying balance using love.plot() with clustered or multiply imputed data.

  • Added imbalanced.only and disp.bal.tab options to bal.tab().

  • Fixes to the vignettes. Also, creation of a new vignette to simplify the main one.

cobalt 3.0.0

CRAN release: 2017-10-16

  • Added support for multinomial treatments in bal.tab(), including output from CBPS and twang.

  • Added support for weightit objects from WeightIt, including for multinomial treatments.

  • Added support for ebalance.trim objects from ebal.

  • Fixes to the vignette.

  • Fixes to splitfactor() to handle tibbles better.

  • Fixed bug when using bal.tab() with multiply imputed data without adjustment. Fixed bug when using s.weights with the formula method of bal.tab().

cobalt 2.2.0

CRAN release: 2017-09-05

  • Added disp.ks and ks.threshold options to bal.tab() to display Kolmogorov-Smirnov statistics before and after preprocessing.

  • Added support for sampling weights, which are applied to both control and treated units, using option s.weights in bal.tab(). Sampling weights are also now compatible with the sampling weights in ps objects from twang; the default is to apply the sampling weights before and after adjustment, mimicking the behavior of bal.table() in twang.

  • Changed behavior of bal.tab() for ps objects to allow displaying balance for more than one stop method at a time, and to default to displaying balance for all available stop methods. The full.stop.method argument in bal.tab() has been renamed stop.method, but full.stop.method still works. get.w() for ps objects has also gone through some changes to be more like twang’s get.weights().

  • Added support in bal.tab() and bal.plot() for subclassification with continuous treatments.

  • Added support in splitfactor() and unsplitfactor() for NA values

  • Fixed a bug in love.plot() caused when var.order was specified to be a sample that was not present.

cobalt 2.1.0

CRAN release: 2017-05-31

  • Added support in bal.tab(), bal.plot(), and love.plot() for examining balance on multiple weight specifications at a time

  • Added new utilities splitfactor(), unsplitfactor(), and get.w()

  • Added option in bal.plot() to display points sized by weights when treatment and covariate are continuous

  • Added which = "both" option in bal.plot() to simultaneously display plots for both adjusted and unadjusted samples; changed argument syntax to accommodate

  • Allowed bal.plot() to display balance for multiple clusters and imputations simultaneously

  • Allowed bal.plot() to display balance for multiple subclasses simultaneously with which.sub

  • Fixes to love.plot() to ensure adjusted points are in front of unadjusted points; changed colors and shape defaults and allowable values

  • Fixed bug where s.d.denom and estimand were not functioning correctly in bal.tab()

  • distance, addl, and weights can now be specified as lists of the usual arguments

cobalt 2.0.0

CRAN release: 2017-05-14

  • Added support for matching using the optmatch package or by specifying matching strata.

  • Added full support (bal.tab(), love.plot(), and bal.plot()) for multiply imputed data, including for clustered data sets.

  • Added support for multiple distance measures, including special treatment in love.plot()

  • Adjusted specifications in love.plot() for color and shape of points, and added option to generate a line connecting the points.

  • Adjusted love.plot() display to perform better on Windows.

  • Added capabilities for love.plot() and bal.plot() to display plots for multiple groups at a time

  • Added flexibility to f.build().

  • Updated bal.plot(), giving the capability to view multiple plots for subclassified or clustered data. Multinomial treatments are also supported.

  • Created a new vignette for clustered and multiply imputed data

  • Speed improvements

  • Fixed a bug causing mislabeling of categorical variables

  • Changed calculation of weighted variance to be in line with recommendations; CBPS can now be used with standardized weights

cobalt 1.3.1

CRAN release: 2016-12-18

  • Added support for entropy balancing through the ebal package.

  • Changed default color scheme of love.plot() to be black and white and added options for color, shape, and size of points.

  • Added sample size calculations for continuous treatments.

  • Edits to the vignette.

cobalt 1.3.0

CRAN release: 2016-10-23

  • Increased capabilities for cluster balance in bal.tab() and love.plot()

  • Increased information and decreased redundancy when assessing balance on interactions

  • Added quick option for bal.tab() to increase speed

  • Added options for print()

  • Bug fixes

  • Speed improvements

  • Edits to the vignette

cobalt 1.2.0

CRAN release: 2016-09-01

  • Added support for continuous treatment variables in bal.tab(), bal.plot(), and love.plot()

  • Added balance assessment within and across clusters

  • Other small performance changes to minimize errors and be more intuitive

  • Major revisions and adjustments to the vignette

cobalt 1.1.0

CRAN release: 2016-07-23

  • Added a vignette.

  • Fixed error in bal.tab.Match() that caused wrong values and and warning messages when used.

  • Added new capabilities to bal.plot(), including the ability to view unadjusted sample distributions, categorical variables as such, and the distance measure. Also updated documentation to reflect these changes and make which.sub more focal.

  • Allowed subclasses to be different from simply 1:S by treating them like factors once input is numerical

  • Changed column names in Balance table output to fit more compactly, and updated documentation to reflect these changes.

  • Other small performance changes to minimize errors and be more intuitive.