Wrapper around the `logs_sample()`

function from the
scoringRules package. Used to score continuous predictions.
While the Log Score is in theory also applicable
to integer forecasts, the problem lies in the implementation: The Log Score
needs a kernel density estimation, which is not well defined with
integer-valued Monte Carlo Samples. The Log Score can be used for specific
integer valued probability distributions. See the scoringRules package for
more details.

## Arguments

- true_values
A vector with the true observed values of size n

- predictions
nxN matrix of predictive samples, n (number of rows) being the number of data points and N (number of columns) the number of Monte Carlo samples. Alternatively, predictions can just be a vector of size n.

## References

Alexander Jordan, Fabian Krüger, Sebastian Lerch, Evaluating Probabilistic Forecasts with scoringRules, https://www.jstatsoft.org/article/view/v090i12

## Examples

```
true_values <- rpois(30, lambda = 1:30)
predictions <- replicate(200, rpois(n = 30, lambda = 1:30))
logs_sample(true_values, predictions)
#> [1] 0.7494908 1.0919477 3.3271950 1.9234195 2.4318399 2.4545439 2.1356909
#> [8] 1.9239871 2.9062721 2.2720969 3.8116822 2.2907329 2.3367677 2.2344713
#> [15] 3.2636670 2.3164318 2.4699314 2.4214885 2.9668527 2.5352744 5.3762493
#> [22] 3.7832992 2.4065689 2.6047600 2.5900973 3.9673890 6.2267761 3.1688208
#> [29] 2.8515763 2.7514476
```