Compute the kurtosis (Fisher or Pearson)

Description

Kurtosis is the fourth central moment divided by the square of the variance. If Fisher’s definition is used, then 3.0 is subtracted from the result to give 0.0 for a normal distribution. If bias is FALSE then the kurtosis is calculated using k statistics to eliminate bias coming from biased moment estimators.

Usage

<Expr>$kurtosis(..., fisher = TRUE, bias = TRUE)

Arguments

…	These dots are for future extensions and must be empty.
fisher	If TRUE (default), Fisher’s definition is used (normal ==\> 0.0). If FALSE, Pearson’s definition is used (normal ==\> 3.0).
bias	If FALSE, the calculations are corrected for statistical bias.

Value

A polars expression

Examples

library("polars")

df <- pl$DataFrame(x = c(1, 2, 3, 2, 1))
df$select(pl$col("x")$kurtosis())

#> shape: (1, 1)
#> ┌───────────┐
#> │ x         │
#> │ ---       │
#> │ f64       │
#> ╞═══════════╡
#> │ -1.153061 │
#> └───────────┘