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Also included are\nmethods for assessing balance in clustered or multiply imputed\ndata sets or data sets with multi-category, continuous, or\nlongitudinal treatments.", "License": "GPL (>=2)", "Language": "en-US", "Encoding": "UTF-8", "LazyData": "true", "VignetteBuilder": "knitr", "URL": "https://ngreifer.github.io/cobalt/,\nhttps://github.com/ngreifer/cobalt", "BugReports": "https://github.com/ngreifer/cobalt/issues", "Config/testthat/edition": "3", "Roxygen": "list(markdown = TRUE)", "Config/roxygen2/version": "8.1.0", "Repository": "https://ngreifer.r-universe.dev", "Date/Publication": "2026-09-29 20:13:23 UTC", "RemoteUrl": "https://github.com/ngreifer/cobalt", "RemoteRef": "HEAD", "RemoteSha": "78b8336c19dfead7cd5ef7a8334d3d5ac89fd22a", "NeedsCompilation": "no", "Packaged": { "Date": "2026-09-29 21:09:06 UTC", "User": "root" }, "Author": "Noah Greifer [aut, cre] (ORCID:\n)", "Maintainer": "Noah Greifer ", "_user": "ngreifer", "_type": "src", "_file": "cobalt_5.0.0.9000.tar.gz", "_fileid": "https://r2.ropensci.org/fdab765705d44283e8e81433f76f663c7aeda3f9694ddd4fc677d9e780f305c2", "_filesize": 4497505, "_sha256": "fdab765705d44283e8e81433f76f663c7aeda3f9694ddd4fc677d9e780f305c2", "_expires": "2027-01-07T21:16:37.000Z", "_created": "2026-09-29T21:09:06.000Z", "_published": "2026-09-29T21:16:40.153Z", "_jobs": [ { "job": 109621357093, "time": 267, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11062379721" }, { "job": 109621357000, "time": 288, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11063090068" }, { "job": 109621357029, "time": 202, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "11061734652" }, { "job": 109621357016, "time": 265, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "11063050349" }, { "job": 109619039048, "time": 359, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "11061949346" }, { "job": 109621356989, "time": 160, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "11062239485" }, { "job": 109621357091, "time": 240, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11062394558" }, { "job": 109621357179, "time": 234, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "11062623791" }, { "job": 109621357243, "time": 228, "config": "windows-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11062718431" } ], "_host": "GitHub-Actions", "_buildurl": "https://github.com/r-universe/ngreifer/actions/runs/36630702322", "_status": "success", "_upstream": "https://github.com/ngreifer/cobalt", "_commit": { "id": "78b8336c19dfead7cd5ef7a8334d3d5ac89fd22a", "author": "Noah Greifer ", "committer": "Noah Greifer ", "message": "Compute every balance statistic on the units where its covariate is observed\n\nWith `na.rm = TRUE`, a covariate's statistic is now the one computed after\ndropping the units missing it, for every `col_w_*()` function and so for\n`bal.tab()` and `bal.compute()`.\n\n`col_w_ks()` dropped the missing values from the covariate but not from the\nweights, pairing each value with another unit's weight (#97). Correlations\ntook the treatment's standard deviation, and Spearman correlations its ranks,\nover all units; `.spearman_ranks()` now ranks the treatment among the units\nobserved for each covariate, and `.compute_s.d.denom()` accepts one treatment\ncolumn per covariate. `col_w_dcov()` ignored `na.rm`. The Hedges correction\ncounted units missing the covariate, and `col_w_ovl()` chose its bandwidth\ngroup before dropping them.\n\nBinary columns with missing values are now detected as binary, and a\ncovariate with no observed values in one treatment group gets `NA` rather than\n0 or 1. 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Multiply Imputed Data", "topics": [ "class-bal.tab.imp" ] }, { "page": "class-bal.tab.msm", "title": "Using 'bal.tab()' with Longitudinal Treatments", "topics": [ "class-bal.tab.msm" ] }, { "page": "class-bal.tab.multi", "title": "Using 'bal.tab()' with Multi-Category Treatments", "topics": [ "class-bal.tab.multi" ] }, { "page": "class-bal.tab.subclass", "title": "Using 'bal.tab()' with Subclassified Data", "topics": [ "class-bal.tab.subclass" ] }, { "page": "display-options", "title": "Options for Displaying 'bal.tab()' Output", "topics": [ "display-options" ] }, { "page": "extract.bal.tab", "title": "Extract a Balance Table for Further Use", "topics": [ "as.data.frame.bal.tab", "extract.bal.tab", "format.bal.tab" ] }, { "page": "f.build", "title": "Convenient Formula Generation", "topics": [ "f.build" ] }, { "page": "get.w", "title": "Extract Weights from Preprocessing Objects", "topics": [ "get.w", "get.w.CBMSM", "get.w.CBPS", "get.w.cem.match", "get.w.designmatch", 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