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>.