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## error.distributions.R: additional error distributions for use in simulations
## require(skewt) ## loaded in vignette if required
## centered skewed t distribution
Eskt <- function(nu, gam) {
M <- if (is.infinite(nu)) sqrt(2/pi) else
gamma((nu+1)/2)/sqrt(nu*pi)/gamma(nu/2)*2*nu/(nu-1)
M*(gam^2-1/gam^2)/(gam + 1/gam)
}
dcskt <- function(x, df, gamma=2) {
ncp <- Eskt(df, gamma)
dskt(x + ncp, df, gamma)
}
pcskt <- function(q, df, gamma=2) {
ncp <- Eskt(df, gamma)
pskt(q + ncp, df, gamma)
}
qcskt <- function(p, df, gamma=2) {
ncp <- Eskt(df, gamma)
qskt(p, df, gamma) - ncp
}
rcskt <- function(n, df, gamma=2) {
ncp <- Eskt(df, gamma)
rskt(n, df, gamma) - ncp
}
####################################################################################
## contaminated normal
####################################################################################
rcnorm <- function (n,mean=0,sd=1,epsilon=0.1,meanc=mean,sdc=sqrt(10)*sd) {
e <- rnorm(n,mean,sd)
nc <- floor(epsilon*n)
idx <- sample(1:n,nc)
e[idx] <- rnorm(nc,meanc,sdc)
e
}
## ignore other arguments for the moment
pcnorm <- function(q,mean=0,sd=1,lower.tail=TRUE,log.p=FALSE,...)
pnorm(q,mean,sd,lower.tail,log.p)
## ignore other arguments for the moment
qcnorm <- function(p,mean=0,sd=1,lower.tail=TRUE,log.p=FALSE,...)
qnorm(p,mean,sd,lower.tail,log.p)
## ignore other arguments for the moment
dcnorm <- function(x,mean=0,sd=1,log=FALSE,...)
dnorm(x,mean,sd,log)
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