distionary:

- makes standard probability distributions available, like Normal, Poisson, and empirical distributions – and even your own distribution, and
- provides a framework for evaluating probability distributions.

The distionary package is more useful when augmented with the distplyr package, which provides a grammar for distribution manipulation.

distionary is not on CRAN yet. You can download the development version from GitHub with:

```
# install.packages("devtools")
devtools::install_github("vincenzocoia/distionary")
```

Or, install distplyr, and distionary will come along with it.

`library(distionary)`

We can make distributions from standard families, like beta and Poisson:

```
(d_beta <- dst_beta(2, 4))
#> beta parametric dst
#>
#> name :
#> [1] "beta"
(d_pois <- dst_pois(1.2))
#> pois parametric dst
#>
#> name :
#> [1] "pois"
```

We can also make empirical distributions from data:

```
x <- c(4.1, 2.3, 3.4, 5.5, 1.0, 6.8)
(d_emp <- dst_empirical(x))
#> finite dst
#>
#> probabilities :
#> # A tibble: 6 × 2
#> location size
#> <dbl> <dbl>
#> 1 1 0.167
#> 2 2.3 0.167
#> 3 3.4 0.167
#> 4 4.1 0.167
#> 5 5.5 0.167
#> 6 6.8 0.167
```

We can evaluate different distributional forms, such as the density or pmf:

```
eval_density(d_beta, at = c(0.1, 0.2))
#> [1] 1.458 2.048
eval_pmf(d_pois, at = c(1, 1.5, 3))
#> [1] 0.36143305 0.00000000 0.08674393
```

Or, we can enframe the results in a tibble:

```
enframe_cdf(d_beta, d_pois, d_emp, at = c(0.1, 0.6, 1.5, 3))
#> # A tibble: 4 × 4
#> .arg cdf_d_beta cdf_d_pois cdf_d_emp
#> <dbl> <dbl> <dbl> <dbl>
#> 1 0.1 0.0815 0.301 0
#> 2 0.6 0.913 0.301 0
#> 3 1.5 1 0.663 0.167
#> 4 3 1 0.966 0.333
```

Evaluate properties of the distributions:

You can make your own distributions, too. Want to make a distribution whose density decays linearly from 0 to `a`

? Just ensure the `p`

/`d`

/`q`

functions are available:

```
# dlinear <- function(x, a) (a - x) / (a^2 / 2)
# plinear <- function(x, a) x * (a - x / 2) / (a^2 / 2)
# qlinear <- function(p, a) a * (1 - sqrt(1 - p))
# (my_dst <- dst_parametric("linear", a = 3, .variable = "continuous"))
```

Hazard function:

`# plot(my_dst, "hazard", from = 0, to = 3)`

Mean:

`# mean(my_dst)`

Please note that the distionary project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.