`summ_moment()`

computes a moment of distribution. It can be one of eight
kinds determined by the combination of `central`

, `standard`

, and `absolute`

boolean features. `summ_skewness()`

and `summ_kurtosis()`

are wrappers for
commonly used kinds of moments: third and forth order central standard ones.
**Note** that `summ_kurtosis()`

by default computes excess kurtosis, i.e.
subtracts 3 from computed forth order moment.

```
summ_moment(f, order, central = FALSE, standard = FALSE, absolute = FALSE)
summ_skewness(f)
summ_kurtosis(f, excess = TRUE)
```

f | A pdqr-function representing distribution. |
---|---|

order | A single number representing order of a moment. Should be non-negative number (even fractional). |

central | Whether to compute central moment (subtract mean of distribution). |

standard | Whether to compute standard moment (divide by standard deviation of distribution). |

absolute | Whether to compute absolute moment (take absolute value of
random variable created after possible effect of |

excess | Whether to compute excess kurtosis (subtract 3 from third order
central standard moment). Default is |

A single number representing moment. If `summ_sd(f)`

is zero and
`standard`

is `TRUE`

, then it is `Inf`

; otherwise - finite number.

`summ_center()`

for computing distribution's center, `summ_spread()`

for spread.

Other summary functions:
`summ_center()`

,
`summ_classmetric()`

,
`summ_distance()`

,
`summ_entropy()`

,
`summ_hdr()`

,
`summ_interval()`

,
`summ_order()`

,
`summ_prob_true()`

,
`summ_pval()`

,
`summ_quantile()`

,
`summ_roc()`

,
`summ_separation()`

,
`summ_spread()`

```
d_beta <- as_d(dbeta, shape1 = 2, shape2 = 1)
# The same as `summ_mean(d_beta)`
summ_moment(d_beta, order = 1)
#> [1] 0.6666667
# The same as `summ_var(d_beta)`
summ_moment(d_beta, order = 2, central = TRUE)
#> [1] 0.05555556
# Return the same number
summ_moment(d_beta, order = 3, central = TRUE, standard = TRUE)
#> [1] -0.5656854summ_skewness(d_beta)
#> [1] -0.5656854
# Return the same number representing non-excess kurtosis
summ_moment(d_beta, order = 4, central = TRUE, standard = TRUE)
#> [1] 2.4summ_kurtosis(d_beta, excess = FALSE)
#> [1] 2.4
```