Features

At its core effectus

• identifies whether a pareto distribution is present. [1]
• identifies the most relevant cause-effect relationship (Vital Few). [1]
• identifies the Rule 50/5 or a variant of it (related but not identical to Useful Many). [2]
• determines causes and effects for any given share of effects and causes, respectively.
• identifies the separating value for the Vital Few. [3]
• assesses the change af the arithmetic mean and the standard deviation for the split of the Vital Few and the Useful Many.

Builtins

• Intervals module: Allows for any interval of causes or effects.
• Intersection module: Automagically intersects pareto distributions of result and effort attributes into ABCD classification and allows to add and subtract the groups easily.
• TBD: Nested Pareto distributions
• Throughput Accounting
• 30 select examples from real life
• Excel & CSV interfaces
• Python API: Fully documented.

Performance

effectus performs well for any number of values (parallel computation; MacBook Air, SSD):

values	ms for 1k values
0-1000	71-3.3
1000-10000	3.6-2.9
> 10000	3.3-1.4

[1]	(1, 2) Representation of effectus.Effects

[2]	effectus.Effects.the_rule().

[3]	effectus.Effects.separate_causes() & effectus.Effects.separate_effects().