# invexp: The Inverse Exponential Distribution In dkahle/invgamma: The Inverse Gamma Distribution

## Description

Density, distribution function, quantile function and random generation for the inverse exponential distribution.

## Usage

 ```1 2 3 4 5 6 7``` ```dinvexp(x, rate = 1, log = FALSE) pinvexp(q, rate = 1, lower.tail = TRUE, log.p = FALSE) qinvexp(p, rate = 1, lower.tail = TRUE, log.p = FALSE) rinvexp(n, rate = 1) ```

## Arguments

 `x, q` vector of quantiles. `rate` degrees of freedom (non-negative, but can be non-integer). `log, log.p` logical; if TRUE, probabilities p are given as log(p). `lower.tail` logical; if TRUE (default), probabilities are P[X <= x] otherwise, P[X > x]. `p` vector of probabilities. `n` number of observations. If length(n) > 1, the length is taken to be the number required.

## Details

The functions (d/p/q/r)invexp simply wrap those of the standard (d/p/q/r)exp R implementation, so look at, say, `dexp` for details.

`dexp`; these functions just wrap the (d/p/q/r)exp functions.
 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```s <- seq(0, 10, .01) plot(s, dinvexp(s, 2), type = 'l') f <- function(x) dinvexp(x, 2) q <- 3 integrate(f, 0, q) (p <- pinvexp(q, 2)) qinvexp(p, 2) # = q mean(rinvexp(1e5, 2) <= q) pinvgamma(q, 1, 2) ```