Density function, distribution function, quantile function and random number generation for various extended generalized Pareto distributions
Usage
pegp(
q,
scale,
shape,
kappa,
model = c("pt-beta", "pt-gamma", "pt-power", "gj-tnorm", "gj-beta", "exptilt",
"logist"),
lower.tail = TRUE,
log.p = FALSE
)
degp(
x,
scale,
shape,
kappa,
model = c("pt-beta", "pt-gamma", "pt-power", "gj-tnorm", "gj-beta", "exptilt",
"logist"),
log = FALSE
)
qegp(
p,
scale,
shape,
kappa,
model = c("pt-beta", "pt-gamma", "pt-power", "gj-tnorm", "gj-beta", "exptilt",
"logist"),
lower.tail = TRUE
)
regp(
n,
scale,
shape,
kappa,
model = c("pt-beta", "pt-gamma", "pt-power", "gj-tnorm", "gj-beta", "exptilt",
"logist")
)
Arguments
- scale
scale parameter, strictly positive.
- shape
shape parameter.
- kappa
shape parameter for the tilting distribution.
- model
string giving the distribution of the model
- lower.tail
logical; if
TRUE
(default), the lower tail probability \(\Pr(X \leq x)\) is returned.- log.p, log
logical; if
FALSE
(default), values are returned on the probability scale.- x, q
vector of quantiles
- p
vector of probabilities
- n
scalar number of observations
References
Papastathopoulos, I. and J. Tawn (2013). Extended generalised Pareto models for tail estimation, Journal of Statistical Planning and Inference 143(3), 131–143, <doi:10.1016/j.jspi.2012.07.001>.
Gamet, P. and Jalbert, J. (2022). A flexible extended generalized Pareto distribution for tail estimation. Environmetrics, 33(6), <doi:10.1002/env.2744>.