README.md

In reconhub/distcrete: Discrete Distribution Approximations

distcrete

Discrete Distribution Approximations

distcrete takes a distribution and a set of parameters and returns a list object with functions as elements. Each one is the equivalent to the function calls we typically expect to be able to do in R for a given distribution:

• Density e.g.dnorm
• Distribution function e.g. pnorm
• Quantile function e.g. qnorm
• Random generation e.g. rnorm

Each function created by distcrete corresponds to the first letter of the non-discrete equivalent.

set.seed(415)
d0 <- distcrete::distcrete("gamma", 1, shape = 3, w = 0)
d0$d(1:10)
#>  [1] 0.243022187 0.253486335 0.185086776 0.113451286 0.062683215
#>  [6] 0.032332641 0.015882196 0.007521773 0.003462799 0.001558522
d0$p(c(.1,.5))
#> [1] 0.09958372 0.19115317
d0$q(c(.1,.5))
#> [1] 0 1
d0$r(10)
#>  [1]  2  3  1  2 12  2  2  4  5  3

Allowed distributions

You can use any distribution that conforms to the following expectations:

• It has a distribution function like p[dist name] available
• It has a quantile function like q[dist name] available

These can be loaded from a package or created on the fly, but must exist when the distcrete() function is called.

Installation

You can install it from CRAN with:

install.packages("distcrete")

You can install distcrete the most up to date version from github with:

# install.packages("devtools")
devtools::install_github("reconhub/distcrete")

Tests

devtools::test()
#> Loading distcrete
#> Loading required package: testthat
#> Testing distcrete
#> distcrete: .....................................................................................................................................
#> utils: ...............
#> 
#> DONE ======================================================================

reconhub/distcrete documentation built on May 27, 2019, 4:02 a.m.