InverseBurr {actuar}R Documentation

The Inverse Burr Distribution


Density function, distribution function, quantile function, random generation, raw moments and limited moments for the Inverse Burr distribution with parameters shape1, shape2 and scale.


dinvburr(x, shape1, shape2, rate = 1, scale = 1/rate,
         log = FALSE)
pinvburr(q, shape1, shape2, rate = 1, scale = 1/rate,
         lower.tail = TRUE, log.p = FALSE)
qinvburr(p, shape1, shape2, rate = 1, scale = 1/rate,
         lower.tail = TRUE, log.p = FALSE)
rinvburr(n, shape1, shape2, rate = 1, scale = 1/rate)
minvburr(order, shape1, shape2, rate = 1, scale = 1/rate)
levinvburr(limit, shape1, shape2, rate = 1, scale = 1/rate,
           order = 1)


x, q

vector of quantiles.


vector of probabilities.


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

shape1, shape2, 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 <= x], otherwise, P[X > x].


order of the moment.


limit of the loss variable.


The inverse Burr distribution with parameters shape1 = a, shape2 = b and scale = s, has density:

f(x) = a b (x/s)^(ba)/(x [1 + (x/s)^b]^(a + 1))

for x > 0, a > 0, b > 0 and s > 0.

The inverse Burr is the distribution of the random variable

s (X/(1 - X))^(1/b),

where X has a beta distribution with parameters a and 1.

The inverse Burr distribution has the following special cases:

The kth raw moment of the random variable X is E[X^k], -shape1 * shape2 < k < shape2.

The kth limited moment at some limit d is E[min(X, d)^k], k > -shape1 * shape2 and 1 - k/shape2 not a negative integer.


dinvburr gives the density, invburr gives the distribution function, qinvburr gives the quantile function, rinvburr generates random deviates, minvburr gives the kth raw moment, and levinvburr gives the kth moment of the limited loss variable.

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


levinvburr computes the limited expected value using betaint.

Also known as the Dagum 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(dinvburr(2, 2, 3, 1, log = TRUE))
p <- (1:10)/10
pinvburr(qinvburr(p, 2, 3, 1), 2, 3, 1)

## variance
minvburr(2, 2, 3, 1) - minvburr(1, 2, 3, 1) ^ 2

## case with 1 - order/shape2 > 0
levinvburr(10, 2, 3, 1, order = 2)

## case with 1 - order/shape2 < 0
levinvburr(10, 2, 1.5, 1, order = 2)

[Package actuar version 3.1-4 Index]