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# Given a contingency table (2-by-2) (as a data frame or matrix)
# it will untabulate it by repeating each row by the number of times
# it was repeated.
#
# @param matrix or data.frame to untable
# @param vector of counts (of same length as \code{df})
# @keywords internal
callback_df <- function(df, num) {
df[rep(1:nrow(df), num), ]
}
# Logit-log distribution function
#
# @param sesp Distribution parameters as list
# @keywords internal
logitlog.dstr <- function(sesp) {
u <- runif(sesp[[1]])
w <- sesp[[2]] + sesp[[3]] * (log(u / (1 - u)))
p <- sesp[[4]] + (sesp[[5]] - sesp[[4]]) * exp(w) / (1 + exp(w))
return(p)
}
# Logit-normal distribution function
#
# @param sesp Distribution parameters as list
# @keywords internal
logitnorm.dstr <- function(sesp) {
u <- runif(sesp[[1]])
w <- sesp[[2]] + sesp[[3]] * qnorm(u)
p <- sesp[[4]] + (sesp[[5]] - sesp[[4]]) * exp(w) / (1 + exp(w))
return(p)
}
# Find closest value
#
# @param x First value
# @param y Reference
# @keywords internal
closest <- function(x, y) {
x[which(abs(x - y) == min(abs(x - y)))]
}
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