version 3.0.0

DESCRIPTION

@@ -1,24 +1,19 @@
 Package: cobalt
 Title: Covariate Balance Tables and Plots
-Version: 2.2.0
+Version: 3.0.0
 Author: Noah Greifer [aut, cre]
 Maintainer: Noah Greifer <noah@unc.edu>
-Description: Generate balance tables and plots for covariates of groups
-    preprocessed through matching, weighting or subclassification, for example,
-    using propensity scores. Includes integration with 'MatchIt', 'twang', 'Matching', 'optmatch', 
-    'CBPS', and 'ebal' for assessing balance on the output of their preprocessing functions. Users
-    can also specify data for balance assessment not generated through the above packages. Also 
-    included are methods for assessing balance in clustered or multiply imputed data sets.
+Description: Generate balance tables and plots for covariates of groups preprocessed through matching, weighting or subclassification, for example, using propensity scores. Includes integration with 'MatchIt', 'twang', 'Matching', 'optmatch', 'CBPS', 'ebal', and 'WeightIt' for assessing balance on the output of their preprocessing functions. Users can also specify data for balance assessment not generated through the above packages. Also included are methods for assessing balance in clustered or multiply imputed data sets.
 Depends: R (>= 3.1)
 Imports: ggplot2 (>= 2.2.0), ggstance
 Suggests: MatchIt, twang, Matching, optmatch, CBPS, ebal, mice, knitr,
         rmarkdown
 License: GPL (>= 2)
 Encoding: UTF-8
 LazyData: true
-Date: 2017-09-05
+Date: 2017-10-15
 VignetteBuilder: knitr
 NeedsCompilation: no
-Packaged: 2017-09-05 04:53:50 UTC; NoahGreifer
+Packaged: 2017-10-16 03:31:38 UTC; NoahGreifer
 Repository: CRAN
-Date/Publication: 2017-09-05 14:28:31 UTC
+Date/Publication: 2017-10-16 03:45:40 UTC

MD5

@@ -1,45 +1,45 @@
-0f71c1c0c47cf9072c41b2b4fa890094 *DESCRIPTION
-64a792efd9244738e27f126e30a31aa4 *NAMESPACE
-946e6a1c07fab01a23c98d2ea5ce7342 *NEWS.md
-24890b1551219a8361b64627ea5f81df *R/bal.plot.R
-a7a0267fdb1dc84a02341ad7d328d29f *R/bal.tab.R
-1c9d5f5d972887b7776b85477ee7f4d4 *R/do_not_include/Under_construction/d.out.R
-f3c30985fb7d42211d9649bd92c5d710 *R/do_not_include/Under_construction/int.poly.R
-7a88a49e108b455e3346d09208d2ef2f *R/do_not_include/Under_construction/under_construction.R
-02f82204b617b13829f5cc81b6e85d70 *R/do_not_include/tests.R
-272ef91ca2545c26cef7439b5f9c7ab5 *R/functions_for_processing.R
-98f88d77c54c0adee74bd16e851efc6c *R/love.plot.R
-a3ccb15b15525ac3d1f9a76e36a6600c *R/print.bal.tab.R
-ebd0402b50a048f528e337cd9e06ab8c *R/utilities.R
-482cf5c3b684ed8db17970f2091a2893 *R/x2base.R
+ca2d343b11b537cfaab2b1f89501b711 *DESCRIPTION
+ea42585b65311b9a8931c0052277f937 *NAMESPACE
+26d5c54cd52fb32900b42c3455c823c3 *NEWS.md
+58db4c6619cfbe2967c496f19d24dc6a *R/bal.plot.R
+8aa83a8e229c06d38dcc4cae5c0a0633 *R/bal.tab.R
+5bf42da2cad76685f4ed2f447ca7c952 *R/functions_for_processing.R
+10a1b042992e2b6446fa3f05e1ae5b9e *R/love.plot.R
+3e183692e8f52950b2174210d4511324 *R/print.bal.tab.R
+7a28a548ebc2127d39d8e2d73bccddbb *R/utilities.R
+18b07d6b4e9ac89d20bd59f97fc28276 *R/x2base.R
 b7832b812849537f814324193462610b *README.md
-6e1d3a8b89d55d5d3c7823133a1dc3f2 *build/vignette.rds
+cf62b3177b577aaff9a8dcff09beeb0b *build/vignette.rds
 949b492287de79b9a90d520db98768c1 *data/lalonde.RData
 d22e5afa667cdc190e348ae984c830a2 *data/lalonde_mis.RData
-8a95d708d4b879551be1a7b4c6cfbfd7 *inst/doc/cobalt_basic_use.R
-9798f99ade90d1011cb6b624ba26f65f *inst/doc/cobalt_basic_use.Rmd
-e660abfdb0f23281e253048b2bc8f207 *inst/doc/cobalt_basic_use.html
+15be95d892231d34903252ba29dbca6a *inst/doc/cobalt_basic_use.R
+d2e161fb211577f54198cc19ae535270 *inst/doc/cobalt_basic_use.Rmd
+949e2c207b188c41c3d60c919b014aa7 *inst/doc/cobalt_basic_use.html
 0afa9b5d4c5fc247bc3aea495e62f3dd *inst/doc/cobalt_cluster_imp.R
-aae0b33ee8cf294a55dfab63815cc279 *inst/doc/cobalt_cluster_imp.Rmd
-879215eee3d70050bd30008d1674fe62 *inst/doc/cobalt_cluster_imp.html
+8a6479e87453790a1e9616a03c64e3fa *inst/doc/cobalt_cluster_imp.Rmd
+3d27c00958636ff7ac94290b194ef38f *inst/doc/cobalt_cluster_imp.html
 18e63995f37fcb1b800639423a057aee *inst/figures/README-unnamed-chunk-3-1.png
 ee5f3f538bc5e9fc8e3851274334d79b *inst/figures/README-unnamed-chunk-3-2.png
 1ceccc3c579652a5c6c694f909d75b85 *inst/figures/README-unnamed-chunk-4-1.png
-72555ab97cf404d532f9f03b436fdc73 *man/bal.plot.Rd
-c87ff7ab5ee3bf16337e4c31e2830975 *man/bal.tab.CBPS.Rd
-3855125ac9d872496a2ddcd7361f24f8 *man/bal.tab.Match.Rd
-123152ab4d8dd4bd9c0d2a62bed06a88 *man/bal.tab.Rd
-6784b882655fcea7304b1df8d6d135f9 *man/bal.tab.df.formula.Rd
-769a47ac5c474d23d941a739dd7b9320 *man/bal.tab.ebalance.Rd
-97f9a661548cf714f02e9ec10337887e *man/bal.tab.matchit.Rd
-3aa5761deee4eb1608d80f6f8e1dee5c *man/bal.tab.mnps.Rd
-f7ba05956821866f4490d8985a600457 *man/bal.tab.optmatch.Rd
-63e1d8f08d911960f1fa20fb3a7037c4 *man/bal.tab.ps.Rd
+4dccdfa8d002a4f1f55dce51e535bf57 *man/bal.plot.Rd
+0aa32d5b1cfb2ac6bab34ceb21ae78ef *man/bal.tab.CBPS.Rd
+388f96d09b43041c44907162679a9eec *man/bal.tab.Match.Rd
+82b929d3ecd6857d6eb3d00758de0afc *man/bal.tab.Rd
+fa8e4803bdb103f94cafaf3b4ede2b06 *man/bal.tab.df.formula.Rd
+148619a53638fc73201874d1b1db6f88 *man/bal.tab.ebalance.Rd
+e71d489d606394d1babf734973b7e5dd *man/bal.tab.matchit.Rd
+ccca9464d0f320811c93117f50786a29 *man/bal.tab.optmatch.Rd
+981dd2648eefca6a0f37cb6de1cd5b6c *man/bal.tab.ps.Rd
+cb5a1c401b63744706aa2f1ff0deafac *man/bal.tab.weightit.Rd
+5393b869fd5241a0b67a953d0bfd50eb *man/class-bal.tab.cluster.Rd
+69424d5e9261defe66fbb22f2bd9a988 *man/class-bal.tab.imp.Rd
+0a3f822364669424af887fd16615ca6d *man/class-bal.tab.multi.Rd
+872387cdbd9055ab1f1fb6ee98eb2018 *man/class-bal.tab.subclass.Rd
 a08ee89605f037da64ad5c71d24badec *man/f.build.Rd
-ca310aec24f9c4d676405064e9f4cbfa *man/get.w.Rd
+dbb727841b8761742ca6be1d6863db83 *man/get.w.Rd
 e43b36b1df7ae5954e88f78eeb9d7f87 *man/lalonde.Rd
-e6ec522d43cb08fb133cfacaa4b9bfb0 *man/love.plot.Rd
-66ba4630da735356b5a25f11f8f36ff0 *man/print.bal.tab.Rd
+2582bb816ba54acbc3f38a2cd29a4fc7 *man/love.plot.Rd
+4d56dae63a52319d5686c0286163ee39 *man/print.bal.tab.Rd
 969532fe9e9ee03bfb532d4255a2f49b *man/splitfactor.Rd
-9798f99ade90d1011cb6b624ba26f65f *vignettes/cobalt_basic_use.Rmd
-aae0b33ee8cf294a55dfab63815cc279 *vignettes/cobalt_cluster_imp.Rmd
+d2e161fb211577f54198cc19ae535270 *vignettes/cobalt_basic_use.Rmd
+8a6479e87453790a1e9616a03c64e3fa *vignettes/cobalt_cluster_imp.Rmd

NAMESPACE

@@ -12,6 +12,7 @@ S3method(print, bal.tab.subclass)
 S3method(print, bal.tab.cluster)
 S3method(print, bal.tab.imp)
 S3method(print, bal.tab.imp.cluster)
+S3method(print, bal.tab.multi)
 S3method(plot, bal.tab)
 S3method(bal.tab, matchit)
 S3method(bal.tab, ps)
@@ -21,11 +22,16 @@ S3method(bal.tab, CBPS)
 S3method(bal.tab, formula)
 S3method(bal.tab, data.frame)
 S3method(bal.tab, ebalance)
+S3method(bal.tab, ebalance.trim)
 S3method(bal.tab, optmatch)
+S3method(bal.tab, weightit)
 S3method(get.w, matchit)
 S3method(get.w, ps)
 S3method(get.w, mnps)
 S3method(get.w, Match)
 S3method(get.w, CBPS)
+S3method(get.w, npCBPS)
 S3method(get.w, ebalance)
-S3method(get.w, optmatch)
+S3method(get.w, ebalance.trim)
+S3method(get.w, optmatch)
+S3method(get.w, weightit)

NEWS.md

@@ -1,5 +1,20 @@
 cobalt News and Updates
 ======
+
+Version 3.0.0
+
+* 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()`.
+
 Version 2.2.0
 
 * Added `disp.ks` and `ks.threshold` options to `bal.tab()` to display Kolmogorov-Smirnov statistics before and after preprocessing.

R/bal.plot.R

@@ -1,4 +1,4 @@
-bal.plot <- function(obj, var.name, ..., which, which.sub = NULL, cluster = NULL, which.cluster = NULL, imp = NULL, which.imp = NULL, size.weight = FALSE) {
+bal.plot <- function(obj, var.name, ..., which, which.sub = NULL, cluster = NULL, which.cluster = NULL, imp = NULL, which.imp = NULL, which.treat = NULL, size.weight = FALSE) {
 
     args <- list(...)
 
@@ -191,8 +191,7 @@ bal.plot <- function(obj, var.name, ..., which, which.sub = NULL, cluster = NULL
         }
     }
 
-    if (length(unique(D$treat)) > 2 && is.numeric(D$treat)) { #Continuous treatments
-
+    if (nunique(D$treat) > 2 && is.numeric(D$treat)) { #Continuous treatments
         if ("subclass" %in% facet) {
             if (is.categorical.var) {
                 weights <- with(D, mapply(function(w, s, v) w / sum(weights[subclass==s & var==v]), w = weights, s = subclass, v = var))
@@ -232,6 +231,33 @@ bal.plot <- function(obj, var.name, ..., which, which.sub = NULL, cluster = NULL
     else { #Categorical treatments (multinomial supported)
         D$treat <- factor(D$treat)
 
+        if (length(which.treat) == 0) 
+            which.treat <- character(0)
+        else if (is.numeric(which.treat)) {
+            which.treat <- levels(D$treat)[seq_along(levels(D$treat)) %in% which.treat]
+            if (length(which.treat) == 0) {
+                warning("No numbers in which.treat correspond to treatment values. All treatment groups will be displayed.", call. = FALSE)
+                which.treat <- character(0)
+            }
+        }
+        else if (is.character(which.treat)) {
+            which.treat <- levels(D$treat)[levels(D$treat) %in% which.treat]
+            if (length(which.treat) == 0) {
+                warning("No names in which.treat correspond to treatment values. All treatment groups will be displayed.", call. = FALSE)
+                which.treat <- character(0)
+            }
+        }
+        else if (is.na(which.treat)) {
+            which.treat <- character(0)
+        }
+        else {
+            warning("The argument to which.treat must be NA, NULL, or a vector of treatment names or indices. All treatment groups will be displayed.", call. = FALSE)
+            which.treat <- character(0)
+        }
+        if (length(which.treat) > 0 && all(!is.na(which.treat))) D <- D[D$treat %in% which.treat,]
+
+        for (i in names(D)[sapply(D, is.factor)]) D[, i] <- factor(D[, i])
+
         if (length(facet) > 0) {
             if ("subclass" %in% facet) {
                 weights <- with(D, mapply(function(w, t, s) w / sum(weights[treat==t & subclass==s]), w = weights, t = treat, s = subclass))
@@ -240,9 +266,7 @@ bal.plot <- function(obj, var.name, ..., which, which.sub = NULL, cluster = NULL
             else {
                 weights <- with(D, mapply(function(w, t, c, i, f.which) w / sum(weights[treat==t & cluster==c & imp==i & facet.which==f.which]), w = weights, t = treat, c= cluster, i = imp, f.which = facet.which))
                 d <- data.frame(weights = weights, treat = D$treat, var = D$var, cluster = D$cluster, imp = D$imp, facet.which = D$facet.which)
-
             }
-
         }
         else {
             weights <- with(D, mapply(function(w, t) w / sum(weights[treat==t]), w = weights, t = treat))