Nothing
expertprobs <-
function(fit, x, d = "best", ex = 1){
if(d == "best"){
d <- fit$best.fitting[ex, 1]
}
if(d == "normal"){
px <- pnorm(x, fit$Normal[ex,1], fit$Normal[ex,2])
}
if(d == "skewnormal"){
px <- sn::psn(x, xi = fit$Skewnormal[ex,1],
omega = fit$Skewnormal[ex,2],
alpha = fit$Skewnormal[ex,3])
}
if(d == "t"){
px <- pt((x - fit$Student.t[ex,1])/fit$Student.t[ex,2], fit$Student.t[ex,3])
}
if(d == "gamma"){
xl <- fit$limits[ex,1]
if(xl == -Inf){xl <- 0}
px <- pgamma(x - xl, fit$Gamma[ex,1], fit$Gamma[ex,2])
}
if(d == "mirrorgamma"){
xu <- fit$limits[ex, 2]
px <- 1 - pgamma(xu - x, fit$mirrorgamma[ex,1], fit$mirrorgamma[ex,2])
}
if(d == "lognormal"){
xl <- fit$limits[ex,1]
if(xl == -Inf){xl <- 0}
px <- plnorm(x - xl, fit$Log.normal[ex,1], fit$Log.normal[ex,2])
}
if(d == "mirrorlognormal"){
xu <- fit$limits[ex, 2]
px <- 1 - plnorm(xu - x, fit$mirrorlognormal[ex,1], fit$mirrorlognormal[ex,2])
}
if(d == "logt"){
xl <- fit$limits[ex,1]
if(xl == -Inf){xl <- 0}
# Avoid NaN
px <- pt( (log(abs(x - xl)) - fit$Log.Student.t[ex,1])
/ fit$Log.Student.t[ex,2], fit$Log.Student.t[ex,3])
px[x <= xl] <- 0 # Set to 0 for x < lower limit
}
if(d == "mirrorlogt"){
xu <- fit$limits[ex, 2]
# Avoid NaN
px <- 1 - pt( (log(abs(xu - x)) - fit$mirrorlogt[ex,1])
/ fit$mirrorlogt[ex,2], fit$mirrorlogt[ex,3])
px[x >= xu] <- 1 # Set to 1 for x > upper limit
}
if(d == "beta"){
xl <- fit$limits[ex,1]
xu <- fit$limits[ex,2]
if(xl == -Inf){xl <- 0}
if(xu == Inf){xu <- 1}
px <- pbeta( (x - xl) / (xu - xl), fit$Beta[ex,1], fit$Beta[ex,2])
}
if(d == "hist"){
px <- approx(c(fit$limits[ex, 1],
fit$vals[ex, ],
fit$limits[ex, 2]),
c(0, fit$probs[ex, ], 1),
xout = x,
yleft = 0,
yright = 1)$y
}
px
}
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