# NOT RUN {
# Example 1: Let F be a standard normal cumulative distribution function then
f_N<-function(s){dnorm(s, mean = 0,sd = 1)} # density function of N(0,1)
F_N<-function(s){pnorm(s, mean = 0,sd = 1)} # distribution function of N(0,1)
QF_N<-function(beta){qnorm(beta, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)}
rnum<-rnorm(100)
beta=c(0.25,0.50,0.75)
# Density
dQBAD(y=rnum,mu=0,phi=1,alpha=.5,f=f_N)
# Distribution function
pQBAD(q=rnum,mu=0,phi=1,alpha=.5,F=F_N)
# Quantile function
qQBAD(beta=beta,mu=0,phi=1,alpha=.5,F=F_N,QF=QF_N)
qQBAD(beta=beta,mu=0,phi=1,alpha=.5,F=F_N)
# random sample generation
rQBAD(n=100,mu=0,phi=1,alpha=.5,QF=QF_N)
rQBAD(n=100,mu=0,phi=1,alpha=.5,F=F_N)
# Example 2: Let F be a standard Laplace cumulative distribution function then
f_La<-function(s){0.5*exp(-abs(s))} # density function of Laplace(0,1)
F_La<-function(s){0.5+0.5*sign(s)*(1-exp(-abs(s)))} # distribution function of Laplace(0,1)
QF_La<-function(beta){-sign(beta-0.5)*log(1-2*abs(beta-0.5))}
rnum<-rnorm(100)
beta=c(0.25,0.50,0.75)
# Density
dQBAD(y=rnum,mu=0,phi=1,alpha=.5,f=f_La)
# Distribution function
pQBAD(q=rnum,mu=0,phi=1,alpha=.5,F=F_La)
# Quantile function
qQBAD(beta=c(0.25,0.50,0.75),mu=0,phi=1,alpha=.5,F=F_La,QF=QF_La)
qQBAD(beta=c(0.25,0.50,0.75),mu=0,phi=1,alpha=.5,F=F_La)
# random sample generation
rQBAD(n=100,mu=0,phi=1,alpha=.5,QF=QF_La)
rQBAD(n=100,mu=0,phi=1,alpha=.5,F=F_La)
# }
Run the code above in your browser using DataLab