## Not run:
# ## tpar contains: log(a),log(theta),beta0
# tpar <- c(0.5, -0.5, 1.3)
# ## A scalar containing the sum of the response counts in pre scans
# Y1 <- 0
# ## A scalar containing the summary statistics of the response counts in new scans q(y_new)
# Y2 <- 1
# ## The number of scans in the pre scans.
# sn1 <- 2
# ## The number of scans in the new scans.
# sn2 <- 3
# ## the covariate matrix
# XM <- NULL
# RE <- "G"
# ## the variance covariance matrix:
# V <- matrix(
# c(0.0490673003, -0.0004481864, 0.013279476,
# -0.0004481864, 0.0245814022, 0.001231522,
# 0.0132794760, 0.0012315221, 0.023888065),nrow=3)
#
# ## the estimate of the conditional probability based on the sum summary statistics and its SE
# CP.se(tpar = tpar, Y1=Y1 ,Y2= Y2, sn1 = sn1, sn2 = sn2, XM = XM, RE = RE, V = V, qfun = "sum")
#
# ## the estimate of the conditional probability based on the max summary statistics and its SE
# CP.se(tpar = tpar, Y1=Y1 ,Y2= Y2, sn1 = sn1, sn2 = sn2, XM = XM, RE = RE, V = V, qfun = "max")
#
# ## jCP calls for CP.se to compute the estimate of the conditional probability
# jCP(tpar = tpar, Y1 = Y1, Y2 = Y2, sn1 = sn1, sn2 = sn2,
# XM = XM, RE = RE, LG = FALSE, oth = NULL, qfun = "sum")
# ## End(Not run)
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