Recently, we published a post presenting a small convenience function, from the bayestestR package. This function, named rnorm_perfect, generated an empirical distribution (i.e., a vector of values) as close as possible to a desired distribution, in this case the normal distribution.

It worked like this:

# Generate a perfect sample
x <- rnorm_perfect(n = 100, mean = 0, sd = 1)

# Visualise it
library(tidyverse)

x %>% 
  density() %>%  # Compute density function
  as.data.frame() %>% 
  ggplot(aes(x=x, y=y)) +
  geom_line()

This function’s name was initially chosen because of its proximity (argument-wise) with its random counterpart rnorm (it has the same arguments, the same order and the same output type). However, we quickly realised this this name was not perfect (no pun intended), as indeed the obtained distribution was not random (and the r in rnorm stands for random).

Thus, after discussion, we decided to change it to distribution_normal. We also used that opportunity to add other types of “perfect” distributions:

# Generate a perfect sample
x <- distribution_beta(n = 100, 6, 2)

x %>% 
  density() %>% 
  as.data.frame() %>% 
  ggplot(aes(x=x, y=y)) +
  geom_line()

Interestingly, a few days before this update, we received a small email asking:

Hi, I saw your blog post and wonder how you define a perfectly normal distribution.

We responded:

that’s actually a good question. I would say an empirical sample having characteristics as close as possible to a canonic Gaussian distribution.

After an (ironic?) “Thanks, most helpful!”, this blog post got published, which emphasize on the irrelevance of the r prefix in the function name. Thanks to some of the good points raised in this post, we’ve felt the need to update users on this function.

Thus, please note that the rnorm_perfect name will be deprecated in the next version, and ultimately removed to avoid further confusion 🙂