#\examples<>

#<ss.cons.norm.knownvar.avg.hpdlimits>
accepted.diff <- 0.01
prior1.known.var <- list(mu0=15, prec0=2.1)
prior2.known.var <- list(mu0=5,  prec0=2.2)
known.prec <- 2
z <- ss.cons.norm.knownvar.avg.hpdlimits(accepted.diff, known.prec, prior1.known.var, prior2.known.var)

#<ss.cons.norm.avg.hpdlimits>
precision.prior <- list(shape=23.85, rate=2197.446) # Gamma prior distribution parameters for precision; sigma = 1/sqrt(precision) has 95% CI: (8, 12)
accepted.diff <- 0.01
prior1 <- list(mu0=15, n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior2 <- list(mu0=5,  n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
z <- ss.cons.norm.avg.hpdlimits(accepted.diff, prior1, prior2)

#<ss.cons.norm.knownvar.avg.hpdlimits.mymarg>
accepted.diff <- 0.01
clinical.prior.known.var <- list(mu0=10, prec0=1.7)
prior1.known.var <- list(mu0=15, prec0=2.1)
prior2.known.var <- list(mu0=5,  n0=25, prec0=2.2)
known.prec <- 2
z <- ss.cons.norm.knownvar.avg.hpdlimits.mymarg(accepted.diff, known.prec, prior1.known.var, prior2.known.var, clinical.prior.known.var)

#<ss.cons.norm.avg.hpdlimits.mymarg>
precision.prior <- list(shape=23.85, rate=2197.446) # Gamma prior distribution parameters for precision; sigma = 1/sqrt(precision) has 95% CI: (8, 12)
accepted.diff <- 0.01
clinical.prior <- list(mu0=10, n0=10, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior1 <- list(mu0=15, n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior2 <- list(mu0=5,  n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
z <- ss.cons.norm.avg.hpdlimits.mymarg(accepted.diff, prior1, prior2, clinical.prior)

#<ss.cons.norm.knownvar.avg.hpdlimits.bothmarg>
accepted.diff <- 0.01
prior1.known.var <- list(mu0=15, prec0=2.1)
prior2.known.var <- list(mu0=5,  prec0=2.2)
known.prec <- 2
z <- ss.cons.norm.knownvar.avg.hpdlimits.bothmarg(accepted.diff, known.prec, prior1.known.var, prior2.known.var)

#<ss.cons.norm.avg.hpdlimits.bothmarg>
precision.prior <- list(shape=23.85, rate=2197.446) # Gamma prior distribution parameters for precision; sigma = 1/sqrt(precision) has 95% CI: (8, 12)
accepted.diff <- 0.01
prior1 <- list(mu0=15, n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior2 <- list(mu0=5,  n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
z <- ss.cons.norm.avg.hpdlimits.bothmarg(accepted.diff, prior1, prior2)

#<ss.cons.norm.knownvar.prob.hpdlimits>
accepted.diff <- 0.01
prior1.known.var <- list(mu0=15, prec0=2.1)
prior2.known.var <- list(mu0=5,  prec0=2.2)
known.prec <- 2
z <- ss.cons.norm.knownvar.prob.hpdlimits(accepted.diff, known.prec, prior1.known.var, prior2.known.var, prob=0.8)

#<ss.cons.norm.prob.hpdlimits>
precision.prior <- list(shape=23.85, rate=2197.446) # Gamma prior distribution parameters for precision; sigma = 1/sqrt(precision) has 95% CI: (8, 12)
accepted.diff <- 0.01
prior1 <- list(mu0=15, n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior2 <- list(mu0=5,  n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
z <- ss.cons.norm.prob.hpdlimits(accepted.diff, prior1, prior2, prob=.8)

#<ss.cons.norm.knownvar.prob.hpdlimits.mymarg>
accepted.diff <- 0.01
clinical.prior.known.var <- list(mu0=10, prec0=1.7)
prior1.known.var <- list(mu0=15, prec0=2.1)
prior2.known.var <- list(mu0=5,  n0=25, prec0=2.2)
known.prec <- 2
z <- ss.cons.norm.knownvar.prob.hpdlimits.mymarg(accepted.diff, known.prec, prior1.known.var, prior2.known.var, clinical.prior.known.var, prob=0.8)

#<ss.cons.norm.prob.hpdlimits.mymarg>
precision.prior <- list(shape=23.85, rate=2197.446) # Gamma prior distribution parameters for precision; sigma = 1/sqrt(precision) has 95% CI: (8, 12)
accepted.diff <- 0.01
clinical.prior <- list(mu0=10, n0=10, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior1 <- list(mu0=15, n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior2 <- list(mu0=5,  n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
z <- ss.cons.norm.prob.hpdlimits.mymarg(accepted.diff, prior1, prior2, clinical.prior, prob=0.8)

#<ss.cons.norm.knownvar.prob.hpdlimits.bothmarg>
accepted.diff <- 0.01
prior1.known.var <- list(mu0=15, prec0=2.1)
prior2.known.var <- list(mu0=5,  prec0=2.2)
known.prec <- 2
z <- ss.cons.norm.knownvar.prob.hpdlimits.bothmarg(accepted.diff, known.prec, prior1.known.var, prior2.known.var, prob=0.8)

#<ss.cons.norm.prob.hpdlimits.bothmarg>
precision.prior <- list(shape=23.85, rate=2197.446) # Gamma prior distribution parameters for precision; sigma = 1/sqrt(precision) has 95% CI: (8, 12)
accepted.diff <- 0.01
prior1 <- list(mu0=15, n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior2 <- list(mu0=5,  n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
z <- ss.cons.norm.prob.hpdlimits.bothmarg(accepted.diff, prior1, prior2, prob=0.8)

#<ss.cons.norm.knownvar.worst.cdf>
accepted.cdf.diff <- 0.025
cdf.points <- c(5, 10)
prior1.known.var <- list(mu0=15, prec0=2.1)
prior2.known.var <- list(mu0=5,  prec0=2.2)
known.prec <- 2
z <- ss.cons.norm.knownvar.worst.cdf(cdf.points, accepted.cdf.diff, known.prec, prior1.known.var, prior2.known.var)

#<ss.cons.norm.knownvar.avg.cdf>
accepted.cdf.diff <- 0.025
cdf.points <- c(5, 10)
prior1.known.var <- list(mu0=15, prec0=2.1)
prior2.known.var <- list(mu0=5,  prec0=2.2)
known.prec <- 2
z <- ss.cons.norm.knownvar.avg.cdf(cdf.points, accepted.cdf.diff, known.prec, prior1.known.var, prior2.known.var)

#<ss.cons.norm.knownvar.avg.cdf.mymarg>
accepted.cdf.diff <- 0.025
cdf.points <- c(5, 10)
clinical.prior.known.var <- list(mu0=10, prec0=1.7)
prior1.known.var <- list(mu0=15, prec0=2.1)
prior2.known.var <- list(mu0=5,  prec0=2.2)
known.prec <- 2
z <- ss.cons.norm.knownvar.avg.cdf.mymarg(cdf.points, accepted.cdf.diff, known.prec, prior1.known.var, prior2.known.var, clinical.prior.known.var)

#<ss.cons.norm.knownvar.avg.cdf.bothmarg>
accepted.cdf.diff <- 0.025
cdf.points <- c(5, 10)
prior1.known.var <- list(mu0=15, prec0=2.1)
prior2.known.var <- list(mu0=5,  prec0=2.2)
known.prec <- 2
z <- ss.cons.norm.knownvar.avg.cdf.bothmarg(cdf.points, accepted.cdf.diff, known.prec, prior1.known.var, prior2.known.var)

#<ss.cons.norm.knownvar.prob.cdf>
accepted.cdf.diff <- 0.025
cdf.points <- c(5, 10)
prior1.known.var <- list(mu0=15, prec0=2.1)
prior2.known.var <- list(mu0=5,  prec0=2.2)
known.prec <- 2
z <- ss.cons.norm.knownvar.prob.cdf(cdf.points, accepted.cdf.diff, known.prec, prior1.known.var, prior2.known.var, prob=0.9)

#<ss.cons.norm.knownvar.prob.cdf.mymarg>
accepted.cdf.diff <- 0.025
cdf.points <- c(5, 10)
clinical.prior.known.var <- list(mu0=10, prec0=1.7)
prior1.known.var <- list(mu0=15, prec0=2.1)
prior2.known.var <- list(mu0=5,  n0=25, prec0=2.2)
known.prec <- 2
z <- ss.cons.norm.knownvar.prob.cdf.mymarg(cdf.points, accepted.cdf.diff, known.prec, prior1.known.var, prior2.known.var, clinical.prior.known.var, prob=0.9)

#<ss.cons.norm.knownvar.prob.cdf.bothmarg>
accepted.cdf.diff <- 0.025
cdf.points <- c(5, 10)
prior1.known.var <- list(mu0=15, prec0=2.1)
prior2.known.var <- list(mu0=5,  prec0=2.2)
known.prec <- 2
z <- ss.cons.norm.knownvar.prob.cdf.bothmarg(cdf.points, accepted.cdf.diff, known.prec, prior1.known.var, prior2.known.var, prob=0.9)

#<ss.cons.norm.avg.cdf>
precision.prior <- list(shape=23.85, rate=2197.446) # Gamma prior distribution parameters for precision; sigma = 1/sqrt(precision) has 95% CI: (8, 12)
accepted.cdf.diff <- 0.025
cdf.points <- c(5, 10)
prior1 <- list(mu0=15, n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior2 <- list(mu0=5,  n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
z <- ss.cons.norm.avg.cdf(cdf.points, accepted.cdf.diff, prior1, prior2)

#<ss.cons.norm.avg.cdf.mymarg>
precision.prior <- list(shape=23.85, rate=2197.446) # Gamma prior distribution parameters for precision; sigma = 1/sqrt(precision) has 95% CI: (8, 12)
accepted.cdf.diff <- 0.025
cdf.points <- c(5, 10)
clinical.prior <- list(mu0=10, n0=10, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior1 <- list(mu0=15, n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior2 <- list(mu0=5,  n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
z <- ss.cons.norm.avg.cdf.mymarg(cdf.points, accepted.cdf.diff, prior1, prior2, clinical.prior)

#<ss.cons.norm.avg.cdf.bothmarg>
precision.prior <- list(shape=23.85, rate=2197.446) # Gamma prior distribution parameters for precision; sigma = 1/sqrt(precision) has 95% CI: (8, 12)
accepted.cdf.diff <- 0.025
cdf.points <- c(5, 10)
prior1 <- list(mu0=15, n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior2 <- list(mu0=5,  n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
z <- ss.cons.norm.avg.cdf.bothmarg(cdf.points, accepted.cdf.diff, prior1, prior2)

#<ss.cons.norm.prob.cdf>
precision.prior <- list(shape=23.85, rate=2197.446) # Gamma prior distribution parameters for precision; sigma = 1/sqrt(precision) has 95% CI: (8, 12)
accepted.cdf.diff <- 0.025
cdf.points <- c(5, 10)
prior1 <- list(mu0=15, n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior2 <- list(mu0=5,  n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
z <- ss.cons.norm.prob.cdf(cdf.points, accepted.cdf.diff, prior1, prior2, prob=0.9)

#<ss.cons.norm.prob.cdf.mymarg>
precision.prior <- list(shape=23.85, rate=2197.446) # Gamma prior distribution parameters for precision; sigma = 1/sqrt(precision) has 95% CI: (8, 12)
accepted.cdf.diff <- 0.025
cdf.points <- c(5, 10)
clinical.prior <- list(mu0=10, n0=10, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior1 <- list(mu0=15, n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior2 <- list(mu0=5,  n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
z <- ss.cons.norm.prob.cdf.mymarg(cdf.points, accepted.cdf.diff, prior1, prior2, clinical.prior, prob=0.9)

#<ss.cons.norm.prob.cdf.bothmarg>
precision.prior <- list(shape=23.85, rate=2197.446) # Gamma prior distribution parameters for precision; sigma = 1/sqrt(precision) has 95% CI: (8, 12)
accepted.cdf.diff <- 0.025
cdf.points <- c(5, 10)
prior1 <- list(mu0=15, n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior2 <- list(mu0=5,  n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
z <- ss.cons.norm.prob.cdf.bothmarg(cdf.points, accepted.cdf.diff, prior1, prior2, prob=0.9)

#<ss.cons.norm.knownvar.avg.q>
accepted.diff <- 0.01
prior1.known.var <- list(mu0=15, prec0=2.1)
prior2.known.var <- list(mu0=5,  prec0=2.2)
known.prec <- 2
quantiles <- c(0.2, 0.75)
z <- ss.cons.norm.knownvar.avg.q(quantiles, accepted.diff, known.prec, prior1.known.var, prior2.known.var)

#<ss.cons.norm.knownvar.avg.q.mymarg>
accepted.diff <- 0.01
clinical.prior.known.var <- list(mu0=10, prec0=1.7)
prior1.known.var <- list(mu0=15, prec0=2.1)
prior2.known.var <- list(mu0=5,  n0=25, prec0=2.2)
known.prec <- 2
quantiles <- c(0.2, 0.75)
z <- ss.cons.norm.knownvar.avg.q.mymarg(quantiles, accepted.diff, known.prec, prior1.known.var, prior2.known.var, clinical.prior.known.var)

#<ss.cons.norm.knownvar.avg.q.bothmarg>
accepted.diff <- 0.01
prior1.known.var <- list(mu0=15, prec0=2.1)
prior2.known.var <- list(mu0=5,  prec0=2.2)
known.prec <- 2
quantiles <- c(0.2, 0.75)
z <- ss.cons.norm.knownvar.avg.q.bothmarg(quantiles, accepted.diff, known.prec, prior1.known.var, prior2.known.var)

#<ss.cons.norm.avg.q>
precision.prior <- list(shape=23.85, rate=2197.446) # Gamma prior distribution parameters for precision; sigma = 1/sqrt(precision) has 95% CI: (8, 12)
accepted.diff <- 0.01
prior1 <- list(mu0=15, n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior2 <- list(mu0=5,  n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
quantiles <- c(0.2, 0.75)
z <- ss.cons.norm.avg.q(quantiles, accepted.diff, prior1, prior2)

#<ss.cons.norm.avg.q.mymarg>
precision.prior <- list(shape=23.85, rate=2197.446) # Gamma prior distribution parameters for precision; sigma = 1/sqrt(precision) has 95% CI: (8, 12)
accepted.diff <- 0.01
clinical.prior <- list(mu0=10, n0=10, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior1 <- list(mu0=15, n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior2 <- list(mu0=5,  n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
quantiles <- c(0.2, 0.75)
z <- ss.cons.norm.avg.q.mymarg(quantiles, accepted.diff, prior1, prior2, clinical.prior)

#<ss.cons.norm.avg.q.bothmarg>
precision.prior <- list(shape=23.85, rate=2197.446) # Gamma prior distribution parameters for precision; sigma = 1/sqrt(precision) has 95% CI: (8, 12)
accepted.diff <- 0.01
prior1 <- list(mu0=15, n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior2 <- list(mu0=5,  n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
quantiles <- c(0.2, 0.75)
z <- ss.cons.norm.avg.q.bothmarg(quantiles, accepted.diff, prior1, prior2)

#<ss.cons.norm.knownvar.prob.q>
accepted.diff <- 0.01
prior1.known.var <- list(mu0=15, prec0=2.1)
prior2.known.var <- list(mu0=5,  prec0=2.2)
known.prec <- 2
quantiles <- c(0.2, 0.75)
z <- ss.cons.norm.knownvar.prob.q(quantiles, accepted.diff, known.prec, prior1.known.var, prior2.known.var, prob=0.8)

#<ss.cons.norm.prob.q>
precision.prior <- list(shape=23.85, rate=2197.446) # Gamma prior distribution parameters for precision; sigma = 1/sqrt(precision) has 95% CI: (8, 12)
accepted.diff <- 0.01
prior1 <- list(mu0=15, n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior2 <- list(mu0=5,  n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
quantiles <- c(0.2, 0.75)
z <- ss.cons.norm.prob.q(quantiles, accepted.diff, prior1, prior2, prob=0.8)

#<ss.cons.norm.knownvar.prob.q.mymarg>
accepted.diff <- 0.01
clinical.prior.known.var <- list(mu0=10, prec0=1.7)
prior1.known.var <- list(mu0=15, prec0=2.1)
prior2.known.var <- list(mu0=5,  n0=25, prec0=2.2)
known.prec <- 2
quantiles <- c(0.2, 0.75)
z <- ss.cons.norm.knownvar.prob.q.mymarg(quantiles, accepted.diff, known.prec, prior1.known.var, prior2.known.var, clinical.prior.known.var, prob=0.8)

#<ss.cons.norm.prob.q.mymarg>
precision.prior <- list(shape=23.85, rate=2197.446) # Gamma prior distribution parameters for precision; sigma = 1/sqrt(precision) has 95% CI: (8, 12)
accepted.diff <- 0.01
clinical.prior <- list(mu0=10, n0=10, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior1 <- list(mu0=15, n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior2 <- list(mu0=5,  n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
quantiles <- c(0.2, 0.75)
z <- ss.cons.norm.prob.q.mymarg(quantiles, accepted.diff, prior1, prior2, clinical.prior, prob=0.8)

#<ss.cons.norm.knownvar.prob.q.bothmarg>
accepted.diff <- 0.01
prior1.known.var <- list(mu0=15, prec0=2.1)
prior2.known.var <- list(mu0=5,  prec0=2.2)
known.prec <- 2
quantiles <- c(0.2, 0.75)
z <- ss.cons.norm.knownvar.prob.q.bothmarg(quantiles, accepted.diff, known.prec, prior1.known.var, prior2.known.var, prob=0.8)

#<ss.cons.norm.prob.q.bothmarg>
precision.prior <- list(shape=23.85, rate=2197.446) # Gamma prior distribution parameters for precision; sigma = 1/sqrt(precision) has 95% CI: (8, 12)
accepted.diff <- 0.01
prior1 <- list(mu0=15, n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
prior2 <- list(mu0=5,  n0=25, prec.shape=precision.prior$shape, prec.rate=precision.prior$rate)
quantiles <- c(0.2, 0.75)
z <- ss.cons.norm.prob.q.bothmarg(quantiles, accepted.diff, prior1, prior2, prob=0.8)