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# Tidy Randomly Generated Inverse Pareto Distribution Tibble

Source: `R/random-tidy-pareto-inverse.R`
`tidy_inverse_pareto.Rd`

This function will generate `n` random points from an inverse pareto distribution with a user provided, `.shape`, `.scale`, and number of random simulations to be produced. The function returns a tibble with the simulation number column the x column which corresponds to the n randomly generated points, the `d_`, `p_` and `q_` data points as well.

The data is returned un-grouped.

The columns that are output are:

- `sim_number` The current simulation number.

- `x` The current value of `n` for the current simulation.

- `y` The randomly generated data point.

- `dx` The `x` value from the `stats::density()` function.

- `dy` The `y` value from the `stats::density()` function.

- `p` The values from the resulting p_ function of the distribution family.

- `q` The values from the resulting q_ function of the distribution family.

## Usage

    tidy_inverse_pareto(
      .n = 50,
      .shape = 1,
      .scale = 1,
      .num_sims = 1,
      .return_tibble = TRUE
    )

## Arguments

.n

  The number of randomly generated points you want.

.shape

  Must be positive.

.scale

  Must be positive.

.num_sims

  The number of randomly generated simulations you want.

.return_tibble

  A logical value indicating whether to return the result as a tibble. Default is TRUE.

## Value

A tibble of randomly generated data.

## Details

This function uses the underlying `actuar::rinvpareto()`, and its underlying `p`, `d`, and `q` functions. For more information please see `actuar::rinvpareto()`

## See also

https://openacttexts.github.io/Loss-Data-Analytics/ChapSummaryDistributions.html

Other Continuous Distribution: `tidy_beta()`, `tidy_burr()`, `tidy_cauchy()`, `tidy_chisquare()`, `tidy_exponential()`, `tidy_f()`, `tidy_gamma()`, `tidy_generalized_beta()`, `tidy_generalized_pareto()`, `tidy_geometric()`, `tidy_inverse_burr()`, `tidy_inverse_exponential()`, `tidy_inverse_gamma()`, `tidy_inverse_normal()`, `tidy_inverse_weibull()`, `tidy_logistic()`, `tidy_lognormal()`, `tidy_normal()`, `tidy_paralogistic()`, `tidy_pareto()`, `tidy_pareto1()`, `tidy_t()`, `tidy_triangular()`, `tidy_uniform()`, `tidy_weibull()`, `tidy_zero_truncated_geometric()`

Other Pareto: `tidy_generalized_pareto()`, `tidy_pareto()`, `tidy_pareto1()`, `util_pareto_param_estimate()`, `util_pareto_stats_tbl()`

Other Inverse Distribution: `tidy_inverse_burr()`, `tidy_inverse_exponential()`, `tidy_inverse_gamma()`, `tidy_inverse_normal()`, `tidy_inverse_weibull()`

## Author

Steven P. Sanderson II, MPH

## Examples

    tidy_inverse_pareto()
    #> # A tibble: 50 × 7
    #>    sim_number     x      y     dx      dy      p      q
    #>    <fct>      <int>  <dbl>  <dbl>   <dbl>  <dbl>  <dbl>
    #>  1 1              1 0.119  -1.77  0.00106 0.106  0.119
    #>  2 1              2 2.14   -1.49  0.00415 0.681  2.14
    #>  3 1              3 2.36   -1.22  0.0135  0.702  2.36
    #>  4 1              4 0.677  -0.952 0.0362  0.404  0.677
    #>  5 1              5 0.274  -0.680 0.0804  0.215  0.274
    #>  6 1              6 0.390  -0.409 0.149   0.280  0.390
    #>  7 1              7 1.68   -0.138 0.233   0.626  1.68
    #>  8 1              8 1.24    0.134 0.309   0.554  1.24
    #>  9 1              9 0.0817  0.405 0.352   0.0755 0.0817
    #> 10 1             10 1.27    0.676 0.351   0.560  1.27
    #> # ℹ 40 more rows