Neutrosophic Laplace {ntsDists} | R Documentation |
Density, distribution function, quantile function, and random
generation for the neutrosophic Laplace (Double Exponential)
distribution with parameters location
= \theta_N
and
scale
= \beta_N
.
dnsLaplace(x, location, scale)
pnsLaplace(q, location, scale, lower.tail = TRUE)
qnsLaplace(p, location, scale)
rnsLaplace(n, location, scale)
x |
a vector or matrix of observations for which the pdf needs to be computed. |
location |
the location parameter, which is the mean. |
scale |
the scale parameter, Must consist of positive values. |
q |
a vector or matrix of quantiles for which the cdf needs to be computed. |
lower.tail |
logical; if TRUE (default), probabilities are
|
p |
a vector or matrix of probabilities for which the quantile needs to be computed. |
n |
number of random values to be generated. |
The neutrosophic Laplace distribution with parameters \theta_N
and \beta_N
has density
f_N(x) = \frac{1}{2\beta_N} \exp\left\{-\frac{|x-\theta_N|}{\beta_N}\right\}
for -\infty < x < \infty
, \theta_N \in (\theta_L, \theta_U)
, the location parameter,
\beta_N \in (\beta_L, \beta_U)
, the scale parameter which be a positive interval.
dnsLaplace
gives the density function
pnsLaplace
gives the distribution function
qnsLaplace
gives the quantile function
rnsLaplace
generates random values from the neutrosophic Laplace distribution.
Rahul, T., Malik, S. C., Raj, M. (2023). Neutrosophic Laplace Distribution with Application in Financial Data Analysis, Neutrosophic Sets and Systems, 57(1), 224-233.
dnsLaplace(x = c(4, 4.1), location = c(0.23, 0.24), scale = c(1, 2))
dnsLaplace(4, location = c(0.23, 0.24), scale = c(1, 2))
# The cumulative distribution function for the neutrosophic observation (4,4.1)
pnsLaplace(q = c(4, 4.1), location = c(0.23, 0.24), scale = c(1, 2))
# The first percentile
qnsLaplace(p = 0.1, location = 0.24, scale = 2)
# The quantiles
qnsLaplace(p = c(0.25, 0.5, 0.75), location = c(0.23, 0.24), scale = c(1, 2))
# Simulate 10 numbers
rnsLaplace(n = 10, location = c(0.23, 0.24), scale = c(1, 2))