Creates an empirical distribution object from a sample.
Assumes iid. samples. `with_params`

should **not** be used with this
distribution because estimation of the relevant indicators happens during
construction.

## Arguments

- sample
Sample to build the empirical distribution from

- positive
Is the underlying distribution known to be positive? This will effect the density estimation procedure.

`positive = FALSE`

uses a kernel density estimate produced by`density()`

,`positive = TRUE`

uses a log-kernel density estimate produced by`logKDE::logdensity_fft()`

. The latter can improve density estimation near zero.- bw
Bandwidth parameter for density estimation. Passed to the density estimation function selected by

`positive`

.

## Details

`sample()`

samples iid. from`sample`

. This approach is similar to bootstrapping.`density()`

evaluates a kernel density estimate, approximating with zero outside of the known support. This estimate is either obtained using stats::density or logKDE::logdensity_fft, depending on`positive`

.`probability()`

evaluates the empirical cumulative density function obtained by stats::ecdf.`quantile()`

evaluates the empirical quantiles using stats::quantile`hazard()`

estimates the hazard rate using the density estimate and the empirical cumulative density function:`h(t) = df(t) / (1 - cdf(t))`

.

## See also

Other Distributions:
`Distribution`

,
`dist_bdegp()`

,
`dist_beta()`

,
`dist_binomial()`

,
`dist_blended()`

,
`dist_dirac()`

,
`dist_discrete()`

,
`dist_erlangmix()`

,
`dist_exponential()`

,
`dist_gamma()`

,
`dist_genpareto()`

,
`dist_lognormal()`

,
`dist_mixture()`

,
`dist_negbinomial()`

,
`dist_normal()`

,
`dist_pareto()`

,
`dist_poisson()`

,
`dist_translate()`

,
`dist_trunc()`

,
`dist_uniform()`

,
`dist_weibull()`

## Examples

```
x <- rexp(20, rate = 1)
dx <- dist_empirical(sample = x, positive = TRUE)
y <- rnorm(20)
dy <- dist_empirical(sample = y)
plot_distributions(
exponential = dx,
normal = dy,
.x = seq(-3, 3, length.out = 100)
)
```