peroxymanova 0.0.3

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Peroxymanova

This project is essentially an implementation of PERMANOVA (wiki link, doi:10.1111/j.1442-9993.2001.01070.pp.x) in Rust.

PERMANOVA (Permutational Multivariate Analysis of Variance) is a method for comparing groups of mathematical objects, requiring only a dissimilarity matrix between them, as opposed to having a notion of an average, like the one used in classical ANOVA. This is incredibly useful, since it is massively easier to define a dissimilarity than a mean: there is no obvious "average graph", "average neural network" or an "average RL policy", but with a little bit of hand waving one can define distances, or dissimilarities between a pair of such entities.

This package aims to provide quality-of-life bells and whistles that turn this incredible method into something useful day to day. It implements the following workflow:

1. Accept a Collection of some things T, a Callable that can compare two of those T, returning a float, and a Collection of labels that indicate to which group a given thing T belongs to
2. Efficiently run the Callable[[T,T], float] for every possible pair of objects in the Collection and build a dissimilarity matrix
3. Given the dissimilarity matrix and a Collection of group-indicating labels, run the PERMANOVA algorithm to get a test statistic and a p-value for the null hypothesis of the groups being all and the same. This step requires a lot of permutations to get the p-value so run it blazingly fast in Rust

Strategic roadmap:

• Actually make it multivariate since a single p-value for a single "difference" is kind of just one-way permutational ANOVA.
• Make a fancy parallelization backend interface for computing pairwise distances. Maybe there could be a backend='ray' that would actually search for a full ray cluster?
• Since we dream of ray, should we get a cluster for rust side as well? :)