## coefficients
mcmc_coef(mcmcobj, exclude="deviance")

## covariance matrix
mcmc_vcov(mcmcobj, exclude="deviance")

## confidence interval
mcmc_confint( mcmcobj, parm, level=.95, exclude="deviance" )

## summary function
mcmc_summary( mcmcobj, quantiles=c(.025,.05,.50,.95,.975) )

## plot function
mcmc_plot(mcmcobj, ...)

## inclusion of derived parameters in mcmc object
mcmc_derivedPars( mcmcobj, derivedPars )

## Wald test for parameters
mcmc_WaldTest( mcmcobj, hypotheses )

## S3 method for class 'mcmc_WaldTest'
summary(object, digits=3, ...)

## Not run: 
#############################################################################
# EXAMPLE 1: Logistic regression in rcppbugs package
#############################################################################


#***************************************
# (1) simulate data
set.seed(8765)
N <- 500
x1 <- stats::rnorm(N)
x2 <- stats::rnorm(N)
y <- 1*( stats::plogis( -.6 + .7*x1 + 1.1 *x2 ) > stats::runif(N) )

#***************************************
# (2) estimate logistic regression with glm
mod <- stats::glm( y ~ x1 + x2, family="binomial" )
summary(mod)

#***************************************
# (3) estimate model with rcppbugs package
library(rcppbugs)
b <- rcppbugs::mcmc.normal( stats::rnorm(3),mu=0,tau=0.0001)
y.hat <- rcppbugs::deterministic( function(x1,x2,b){
                stats::plogis( b[1] + b[2]*x1 + b[3]*x2 ) },
                  x1, x2, b)
y.lik <- rcppbugs::mcmc.bernoulli( y, p=y.hat, observed=TRUE)
model <- rcppbugs::create.model(b, y.hat, y.lik)

#*** estimate model in rcppbugs; 5000 iterations, 1000 burnin iterations
n.burnin <- 500 ; n.iter <- 2000 ; thin <- 2
ans <- rcppbugs::run.model(model, iterations=n.iter, burn=n.burnin, adapt=200, thin=thin)
print(rcppbugs::get.ar(ans)) # get acceptance rate
print(apply(ans[["b"]],2,mean)) # get means of posterior

#*** convert rcppbugs into mcmclist object
mcmcobj <- data.frame( ans$b )
colnames(mcmcobj) <- paste0("b",1:3)
mcmcobj <- as.matrix(mcmcobj)
class(mcmcobj) <- "mcmc"
attr(mcmcobj, "mcpar") <- c( n.burnin+1, n.iter, thin )
mcmcobj <- coda::mcmc( mcmcobj )

# coefficients, variance covariance matrix and confidence interval
mcmc_coef(mcmcobj)
mcmc_vcov(mcmcobj)
mcmc_confint( mcmcobj, level=.90 )

# summary and plot
mcmc_summary(mcmcobj)
mcmc_plot(mcmcobj, ask=TRUE)

# include derived parameters in mcmc object
derivedPars <- list( "diff12"=~ I(b2-b1), "diff13"=~ I(b3-b1) )
mcmcobj2 <- sirt::mcmc_derivedPars(mcmcobj, derivedPars=derivedPars )
mcmc_summary(mcmcobj2)

#*** Wald test for parameters
 # hyp1: b2 - 0.5=0
 # hyp2: b2 * b3=0
hypotheses <- list( "hyp1"=~ I( b2 - .5 ), "hyp2"=~ I( b2*b3 ) )
test1 <- sirt::mcmc_WaldTest( mcmcobj, hypotheses=hypotheses )
summary(test1)

## End(Not run)