Modify a distribution with a transformation

Source: R/transformed.R

Experimental lifecycle

dist_transformed(dist, transform, inverse)

Arguments

dist

A univariate distribution vector.
transform

A function used to transform the distribution. This transformation should be monotonic over appropriate domain.
inverse

The inverse of the transform function.

Details

The density(), mean(), and variance() methods are approximate as they are based on numerical derivatives.

Examples

# Create a log normal distribution
dist <- dist_transformed(dist_normal(0, 0.5), exp, log)
density(dist, 1) # dlnorm(1, 0, 0.5)

#> [1] 0.7978846
cdf(dist, 4) # plnorm(4, 0, 0.5)

#> [1] 0.9972194
quantile(dist, 0.1) # qlnorm(0.1, 0, 0.5)

#> [1] 0.5268835
generate(dist, 10) # rlnorm(10, 0, 0.5)

#> [[1]]
#>  [1] 1.3928036 1.3077456 0.9644412 1.3642390 2.3566417 0.7444349 0.7024642
#>  [8] 0.7828532 0.6723029 1.2295892
#>