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# random, percentile, density and quantile function of the logit-normal distribution
# and estimation of parameters from percentiles by Sum of Squares Newton optimization
#
logit <- function(
### Transforming (0,1) to normal scale (-Inf Inf)
p,...
){
##details<<
## function \eqn{ logit(p)= log \left( \frac{p}{1-p} \right) = log(p) - log(1-p) }
##seealso<< \code{\link{invlogit}}
qlogis(p,...)
}
invlogit <- function(
### Transforming (-Inf,Inf) to original scale (0,1)
q,...
){
##details<<
## function \eqn{f(z) = \frac{e^{z}}{e^{z} + 1} \! = \frac{1}{1 + e^{-z}} \!}
##seealso<< \code{\link{logit}}
plogis(q,...)
}
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