Neutrosophic Generalized Exponential {ntsDists}

Neutrosophic Generalized Exponential Distribution

Description

Density, distribution function, quantile function and random generation for the neutrosophic generalized exponential distribution with shape parameter \delta_N and scale parameter \nu_N.

Usage

dnsGenExp(x, nu, delta)

pnsGenExp(q, nu, delta, lower.tail = TRUE)

qnsGenExp(p, nu, delta)

rnsGenExp(n, nu, delta)

Arguments

x

a vector or matrix of observations for which the pdf needs to be computed.

nu

the scale parameter, which must be a positive interval.

delta

the shape parameter, which must be a positive interval.

q

a vector or matrix of quantiles for which the cdf needs to be computed.

lower.tail

logical; if TRUE (default), probabilities are P(X \leq x); otherwise, P(X >x).

p

a vector or matrix of probabilities for which the quantile needs to be computed.

n

number of random values to be generated.

Details

The neutrosophic generalized exponential distribution with parameters \delta_N and \nu_N has density

f_N(x)=\frac{\delta_N}{\nu_N}\left(1-\exp \left\{-\frac{x_N}{\nu_N}\right\}\right)^{\delta_N-1} \exp\left\{-\frac{x_N}{\nu_N}\right\}

for \delta_N \in (\delta_L, \delta_U), the shape parameter which must be a positive interval, and \nu_N \in (\nu_L, \nu_U), the scale parameter which must also be a positive interval, and x \ge 0.

Value

dnsGenExp gives the density function

pnsGenExp gives the distribution function

qnsGenExp gives the quantile function

rnsGenExp generates random variables from the neutrosophic generalized exponential distribution.

References

Rao, G. S., Norouzirad, M., and Mazarei . D. (2023). Neutrosophic Generalized Exponential Distribution with Application. Neutrosophic Sets and Systems, 55, 471-485.

Examples

data(remission)
dnsGenExp(x = remission, nu = c(7.9506, 8.0568), delta = c(1.2390, 1.2397))

pnsGenExp(q = 20, nu = c(7.9506, 8.0568), delta = c(1.2390, 1.2397))


# Calcluate quantiles
qnsGenExp(c(0.25, 0.5, 0.75), nu = c(7.9506, 8.0568), delta = c(1.2390, 1.2397))

# Simulate 10 values
rnsGenExp(n = 10, nu = c(7.9506, 8.0568), delta = c(1.2390, 1.2397))