Calculate the bimodality coefficient for a numeric vector as specified in
Pfister et al. (2013).

`bimodality_coefficient(x, na.rm = FALSE)`

## Arguments

- x
a numeric vector.

- na.rm
logical specifying whether missing values should be removed.

## Details

The calculation of the bimodality coefficient involves calculating the
skewness and kurtosis of the distribution first. For this, the
skew and kurtosi functions of the `psych`

package are used.
Note that type is set to "2" for these functions in accordance with
Pfister et al. (2013).

## References

Pfister, R., Schwarz, K. A., Janczyk, M., Dale, R., & Freeman, J. B. (2013).
Good things peak in pairs: A note on the bimodality coefficient.
*Frontiers in Psychology, 4*, 700.
doi:10.3389/fpsyg.2013.00700

## See also

skew for calculating skewness and kurtosis.

mt_check_bimodality for assessing bimodality using several methods in
a mousetrap data object.

## Author

Pascal J. Kieslich

Felix Henninger

## Examples

```
pfister_data_a <- rep(1:11, times=c(3,5,5,10,17,20,17,10,5,5,3))
bimodality_coefficient(pfister_data_a) #.34
#> [1] 0.3360581
pfister_data_b <- rep(1:11, times=c(2,26,14,6,2,0,2,6,14,26,2))
bimodality_coefficient(pfister_data_b) #.79
#> [1] 0.7946887
```