The package is registered in the [`General`](https://github.com/JuliaRegistries/General) registry and so can be installed at the REPL with `] add StringDistances`.
- The function `compare` returns the similarity score, defined as 1 minus the normalized distance between two strings. It always returns a Float64. A value of 0.0 means completely different and a value of 1.0 means completely similar.
The functions `findnearest` and `findall` are particularly optimized for `Levenshtein`, `DamerauLevenshtein` distances (these distances stop early if the distance is higher than a certain threshold).
### distance modifiers
The package also defines Distance "modifiers" that can be applied to any distance.
- [Partial](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) returns the minimum of the distance between the shorter string and substrings of the longer string.
- [TokenSort](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) adjusts for differences in word orders by returning the distance of the two strings, after re-ordering words alphabetically.
- [TokenSet](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) adjusts for differences in word orders and word numbers by returning the distance between the intersection of two strings with each string.
- [TokenMax](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/) normalizes the distance, and combine the `Partial`, `TokenSort` and `TokenSet` modifiers, with penalty terms depending on string lengths. This is a good distance to match strings composed of multiple words, like addresses. `TokenMax(Levenshtein())` corresponds to the distance defined in [fuzzywuzzy](http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/)