ps objects from twang.
"bal.tab"(x, full.stop.method, int = FALSE, addl = NULL,  continuous = c("std", "raw"), binary = c("raw", "std"),  s.d.denom, m.threshold = NULL, v.threshold = NULL,  un = FALSE, disp.means = FALSE, disp.v.ratio = FALSE,  cluster = NULL, which.cluster = NULL, cluster.summary = TRUE,  quick = FALSE, ...)ps object; the output of a call to ps() from the twang package.
ps(). Examples include "es.max.att" or "ks.mean.ate". bal.tab() will assess balance for the weights created by that stopping method. If no stopping method is provided, bal.tab() will default to displaying balance for the first available stopping method. 
  logical; whether or not to include 2-way interactions of covariates included in the call to ps() and in addl.
ps(). See note in Details.
bal.tab() will use "treated" if the estimand of the call to ps() is the ATT and "pooled" if the estimand is the ATE.
logical; whether to print statistics for the unadjusted sample as well as for the adjusted sample.
logical; whether to print the group means in balance output.
logical; whether to display variance ratios in balance output.
NULL, all clusters in cluster will be displayed. If NA, no clusters will be displayed. Otherwise, can be a vector of cluster names or numerical indices for which to display balance. Indices correspond to the alphabetical order of cluster names. 
logical; whether to display the cluster summary table if cluster is specified. If which.cluster is NA, cluster.summary will be set to TRUE.
logical; if TRUE, will not compute any values that will not be displayed. Leave FALSE if computed values not displayed will be used later.
"bal.tab" containing balance summaries for the ps object. The following are the elements of bal.tab:If clusters are specified, an object of class "bal.tab.cluster" containing balance summaries within each cluster and a summary of balance across clusters. Each balance summary is a balance table as described in Balance above. The summary of balance across clusters displays the mean, median, and maximum mean difference and variance ratio after adjustment for each covariate across clusters. Minimum statistics are calculated as well, but not displayed. To see these, use the options in print.bal.tab.cluster.
bal.tab.ps() generates a list of balance summaries for the ps object given, and functions similarly to bal.table() in twang.The last four arguments of bal.tab() affect display only; they are passed directly to print.bal.tab, and do not affect any calculations or the contents of the bal.tab object. All balance statistics are calculated whether they are displayed by print or not. The threshold values (m.threshold and v.threshold) control whether extra columns should be inserted into the Balance table describing whether the balance statistics in question exceeded or were within the threshold. Including these thresholds also creates summary tables tallying the number of variables that exceeded and were within the threshold and displaying the variables with the greatest imbalance on that balance measure.
The input to addl must be a data frame; if more than one variable is included, this is straightforward (i.e., because data[,c("v1", "v2")] is already a data frame), but if only one variable is used, R will coerce it to a vector, thus making it unfit for input here. To avoid this, simply wrap the input to addl in data.frame() or use subset() if only one variable is to be added. Again, when more than one variable is included, the input is general already a data frame and nothing needs to be done.
bal.tab for details of calculations.
library(twang); data("lalonde", package = "cobalt")
## Not run: 
# ## Using ps() for generalized boosted modeling
# ps.out <- ps(treat ~ age + educ + black + hispan + married + 
#              nodegree + re74 + re75, data = lalonde, 
#              stop.method = c("es.mean", "es.max"), 
#              estimand = "ATT", verbose = FALSE)
# bal.tab(ps.out, full.stop.method = "es.max.att", un = TRUE, 
#         m.threshold = .1, v.threshold = 2)
# ## End(Not run)
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