InverseGamma: The Inverse Gamma Distribution

InverseGammaR Documentation

The Inverse Gamma Distribution


Density function, distribution function, quantile function, random generation, raw moments, and limited moments for the Inverse Gamma distribution with parameters shape and scale.


dinvgamma(x, shape, rate = 1, scale = 1/rate, log = FALSE)
pinvgamma(q, shape, rate = 1, scale = 1/rate,
          lower.tail = TRUE, log.p = FALSE)
qinvgamma(p, shape, rate = 1, scale = 1/rate,
          lower.tail = TRUE, log.p = FALSE)
rinvgamma(n, shape, rate = 1, scale = 1/rate)
minvgamma(order, shape, rate = 1, scale = 1/rate)
levinvgamma(limit, shape, rate = 1, scale = 1/rate,
            order = 1)
mgfinvgamma(t, shape, rate =1, scale = 1/rate, log =FALSE)


x, q

vector of quantiles.


vector of probabilities.


number of observations. If length(n) > 1, the length is taken to be the number required.

shape, scale

parameters. Must be strictly positive.


an alternative way to specify the scale.

log, log.p

logical; if TRUE, probabilities/densities p are returned as \log(p).


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


order of the moment.


limit of the loss variable.


numeric vector.


The inverse gamma distribution with parameters shape = \alpha and scale = \theta has density:

f(x) = \frac{u^\alpha e^{-u}}{x \Gamma(\alpha)}, % \quad u = \theta/x

for x > 0, \alpha > 0 and \theta > 0. (Here \Gamma(\alpha) is the function implemented by R's gamma() and defined in its help.)

The special case shape == 1 is an Inverse Exponential distribution.

The kth raw moment of the random variable X is E[X^k], k < \alpha, and the kth limited moment at some limit d is E[\min(X, d)^k], all k.

The moment generating function is given by E[e^{tX}].


dinvgamma gives the density, pinvgamma gives the distribution function, qinvgamma gives the quantile function, rinvgamma generates random deviates, minvgamma gives the kth raw moment, levinvgamma gives the kth moment of the limited loss variable, and mgfinvgamma gives the moment generating function in t.

Invalid arguments will result in return value NaN, with a warning.


levinvgamma computes the limited expected value using gammainc from package expint.

Also known as the Vinci distribution. See also Kleiber and Kotz (2003) for alternative names and parametrizations.

The "distributions" package vignette provides the interrelations between the continuous size distributions in actuar and the complete formulas underlying the above functions.


Vincent Goulet and Mathieu Pigeon


Kleiber, C. and Kotz, S. (2003), Statistical Size Distributions in Economics and Actuarial Sciences, Wiley.

Klugman, S. A., Panjer, H. H. and Willmot, G. E. (2012), Loss Models, From Data to Decisions, Fourth Edition, Wiley.


exp(dinvgamma(2, 3, 4, log = TRUE))
p <- (1:10)/10
pinvgamma(qinvgamma(p, 2, 3), 2, 3)
minvgamma(-1, 2, 2) ^ 2
levinvgamma(10, 2, 2, order = 1)
mgfinvgamma(-1, 3, 2)

actuar documentation built on Nov. 8, 2023, 9:06 a.m.