## -----------------------------------------------------------------------------
R <- matrix(c(1,-1,0,0,1,-1), nrow = 2, byrow = TRUE)
mu <- c(.4, .2, 0)

R
mu
R %*% mu
(R %*% mu) > 0

## ---- eval = FALSE------------------------------------------------------------
#  h1 <- matrix(c(1,-1,0,0,1,-1), nrow= 2, byrow= TRUE)
#  h2 <- 'c'
#  m1 <- c(.4,.2,0)
#  m2 <- c(.2,0,.1)
#  bayes_power(40, m1, m2, h1, h2)

## ---- eval = FALSE------------------------------------------------------------
#  h1 <- matrix(c(1,-1,0,0,0,1,-1,0,0,0,1,-1), nrow= 3, byrow= TRUE)
#  h2 <- matrix(c(0,-1,1,0,0,1,0,-1,-1,0,0,1), nrow = 3, byrow= TRUE)
#  m1 <- c(.7,.3,.1,0)
#  m2 <- c(0,.4,.5,.1)
#  bayes_power(34, h1, h2, m1, m2, bound1 = 3, bound2 = 1/3)

## ---- eval = FALSE------------------------------------------------------------
#  h1 <- matrix(c(1, -1, 0,
#                 0, 1, -1),
#               nrow= 2, byrow= TRUE)
#  h2 <- 'c'
#  m1 <- c(.4, .2, 0)
#  m2 <- c(.2, 0, .1)
#  bayes_sampsize(h1, h2, m1, m2, type = "de", cutoff = .125)

## ---- eval = FALSE------------------------------------------------------------
#  h1 <- matrix(c(1, -1, 0, 0,
#                 0, 1, -1, 0,
#                 0, 0, 1, -1),
#               nrow= 3, byrow= TRUE)
#  h2 <- matrix(c(0, -1, 1, 0,
#                 0, 1, 0, -1,
#                 -1, 0, 0, 1),
#               nrow = 3, byrow= TRUE)
#  m1 <- c(.7, .3, .1, 0)
#  m2 <- c(0, .4, .5, .1)
#  bayes_sampsize(h1, h2, m1, m2, type = "aoi", cutoff = .2, minss = 2, maxss = 500)

## ---- eval = FALSE------------------------------------------------------------
#  h1 <- matrix(c(1, -1, 0, 0,
#                 0, 1, -1, 0,
#                 0, 0, 1, -1),
#               nrow= 3, byrow= TRUE)
#  h2 <- 'u'
#  m1 <- c(.3, .2, 0)
#  m2 <- c(0, 0, 0)
#  bayes_sampsize(h1, h2, m1, m2, type = "med.1", cutoff = 3, minss = 2, maxss = 500)