I’ve stumbled upon a little trick to compute **jenks** breaks faster than with the **classInt** package, just be sure to use **n+1** instead of **n** as the breaks are computed a little bit differently. That is to say, if you want 5 breaks, **n=6**, no biggie there.

For more on the Bayesian Analysis of Macroevolutionary Mixtures see BAMMtools library

`install.packages("BAMMtools")`

library(BAMMtools)

system.time(getJenksBreaks(mydata$myvar, 6))

> user system elapsed

> 0.970 0.001 0.971

On the other hand this takes way more time with large datasets

`library(classInt)`

system.time(classIntervals(mydata$myvar, n=5, style="jenks"))

> Timing stopped at: 1081.894 1.345 1083.511

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## Author: acarioli

is a PostDoc at the Geography and Environment department of the University of Southampton, WorldPop project team. She is also affiliated researcher at CED, UAB and Dondena Centre. Her interests include spatial econometrics and modeling, bayesian methods, machine learning processes, forecasting, micro-data simulation, and data visualization.
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