`summ_entropy()`

computes entropy of single distribution while
`summ_entropy2()`

- for a pair of distributions. For "discrete"
pdqr-functions a classic formula `-sum(p * log(p))`

(in nats) is used. In
"continuous" case a differential entropy is computed.

```
summ_entropy(f)
summ_entropy2(f, g, method = "relative", clip = exp(-20))
```

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

g | A pdqr-function. Should be the same type as |

method | Entropy method for pair of distributions. One of "relative" (Kullback–Leibler divergence) or "cross" (for cross-entropy). |

clip | Value to be used instead of 0 during |

A single number representing entropy. If `clip`

is strictly positive,
then it will be finite.

**Note** that due to pdqr approximation error
there can be a rather big error in entropy estimation in case original
density goes to infinity.

Other summary functions:
`summ_center()`

,
`summ_classmetric()`

,
`summ_distance()`

,
`summ_hdr()`

,
`summ_interval()`

,
`summ_moment()`

,
`summ_order()`

,
`summ_prob_true()`

,
`summ_pval()`

,
`summ_quantile()`

,
`summ_roc()`

,
`summ_separation()`

,
`summ_spread()`

```
#> [1] 1.418913summ_entropy2(d_norm, d_norm_2)#> [1] 9.006174summ_entropy2(d_norm, d_norm_2, method = "cross")#> [1] 10.42509
# Increasing `clip` leads to decreasing maximum output value
d_1 <- new_d(1:10, "discrete")
d_2 <- new_d(20:21, "discrete")
# Formally, output isn't clearly defined because functions don't have the
# same support. Direct use of entropy formulas gives infinity output, but
# here maximum value is `-log(clip)`.
summ_entropy2(d_1, d_2, method = "cross")#> [1] 20#> [1] 10summ_entropy2(d_1, d_2, method = "cross", clip = 0)#> [1] Inf
```