Calculate posterior expected loss manually from bayesTest function of bayesAB package

When I use the function on some simulated data

library(bayesAB)set.seed(255)# simulate dataA <- rbinom(250, 1, .25)B <- rbinom(250, 1, .2)# apply the function for AB group comparisonAB_test <- bayesTest(A,                      B,                      priors = c('alpha' = 65, 'beta' = 200),                      n_samples = 1e5,                      distribution = 'bernoulli')# obtain the outputsummary(AB_test)

I get the following output

# Quantiles of posteriors for A and B:#   # $Probability# $Probability$A# 0%       25%       50%       75%      100% # 0.1775006 0.2469845 0.2598399 0.2730324 0.3506919 # # $Probability$B# 0%       25%       50%       75%      100% # 0.1510354 0.2146442 0.2268472 0.2394675 0.3182802 # # # --------------------------------------------#   # P(A > B) by (0)%: #   # $Probability# [1] 0.89305# # --------------------------------------------#   # Credible Interval on (A - B) / B for interval length(s) (0.9) : #   # $Probability# 5%         95% # -0.04278263  0.37454069 # # --------------------------------------------#   # Posterior Expected Loss for choosing A over B:#   # $Probability# [1] 0.00587424

I know how to manually obtain the first 3 sections, using the posterior data

quantile(AB_test$posteriors$Probability$A, c(0, 0.25, 0.50, 0.75, 1))# 0%       25%       50%       75%      100% # 0.1775006 0.2469845 0.2598399 0.2730324 0.3506919quantile(AB_test$posteriors$Probability$B, c(0, 0.25, 0.50, 0.75, 1))# 0%       25%       50%       75%      100% # 0.1510354 0.2146442 0.2268472 0.2394675 0.3182802mean(AB_test$posteriors$Probability$A > AB_test$posteriors$Probability$B)# [1] 0.89305quantile(AB_test$posteriors$Probability$A / AB_test$posteriors$Probability$B - 1, c(0.05, 0.95))# 5%         95% # -0.04278263  0.37454069 

But I'm not sure how to calculate the posterior expected loss, shown in the last section of the output.Is that possible?