This function calculates the Bimodality Ratio which is a measure of the proportion of bimodality. The proportion of bimodality here is referring to the mixture proportions of two, say, Gaussian (normal) components that can have different frequencies. For instance, a 50 separation will be different from a 25 results of "Example 2", "Example 3", and "Example 4" to get a better understanding.

1 | ```
bimodality_ratio(x, list = FALSE, ...)
``` |

`x` |
Data vector. |

`list` |
Calculate the Bimodality Ratio for a list of data vectors. This technique is faster than parallelizing for typical big datasets (i.e. when the length of a data vector <=1E9), though benchmarks weren't assessed beyond dimensions of 1E10 x 1E10. When selected, this outputs a list of Bimodality Ratios. Defaults to FALSE. |

`...` |
Pass through arguments. |

Zhang, C., Mapes, B., & Soden, B. (2003). Bimodality in tropical water vapour. Quarterly Journal of the Royal Meteorological Society, 129(594), 2847-2866.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | ```
#Example 1
data<-c(rnorm(15,0,1),rnorm(21,5,1))
bimodality_ratio(data,FALSE)
values<-as.list(rep(list(rnorm(15,-4,2),rnorm(21,7,2),data),2))
bimodality_ratio(values,TRUE)
#Example 2
dist1<-rnorm(21,5,2)
dist2<-dist1+11
data<-c(dist1,dist2)
hist(data)
bimodality_ratio(data,FALSE)
#Example 3
dist1<-rnorm(21,-15,1)
dist2<-rep(dist1,3)+30
data<-c(dist1,dist2)
hist(data)
bimodality_ratio(data,FALSE)
#Example 4
dist1<-rep(7,70)
dist2<-rep(-7,70)
data<-c(dist1,dist2)
hist(data)
bimodality_ratio(data,FALSE)
``` |

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