Density, distribution function, quantile function and random generation for
the Lindley distribution.
Usage
dLd(x, theta, log = FALSE)
pLd(q, theta, lower.tail = TRUE, log.p = FALSE)
qLd(p, theta, lower.tail = TRUE)
rLd(n, theta)
Value
dLd gives the density, pLd gives the distribution
function, qLd gives the quantile function and rLd generates
random deviates.
Arguments
x, q
vector of quantiles.
theta
a parameter.
log, log.p
logical; if TRUE, probabilities p are given as log(p).
lower.tail
logical; if TRUE (default), probabilities are
\(P\left[ X\leq x\right]\), otherwise, \(P\left[ X>x\right] \).
p
vector of probabilities.
n
number of observations. If length(n) > 1, the length is taken
to be the number required.
Details
The Lindley distribution with a parameter \(\theta\), has density
$$f\left( x\right) =\frac{\theta ^{2}}{1+\theta }\left( 1+x\right)
e^{-\theta~x},$$
where
$$x>0,~\theta >0.$$
References
Akgül, F. G., Acıtaş, Ş. ve Şenoğlu, B., 2018,
Inferences on stress–strength reliability based on ranked set sampling data
in case of Lindley distribution, Journal of statistical computation and
simulation, 88 (15), 3018-3032.