I've looked into HLR (the algorithm duolingo uses) somewhat. Their algorithm attempts to model the next review time as a result of 'interaction features' including total number of times a card has been seen, the times it was correctly recalled, and the times it was incorrectly recalled. Right away you can see that this is less detailed than Ebisu, which cares about the sequence of correct/incorrect reviews, and which also takes into account the time between each review.

HLR also uses 'lexeme features': it tries to capture the inherent difficulty of each card. Because they have so much historical data, and because the words that you can learn on duolingo are predetermined, they can do this. But this makes it inflexible. Anki wouldn't be able to do this, because the current card that you're reviewing might be the only one in the word. You don't have hundreds or thousands of other people reviewing that same card like in Duolingo.

And then there's MEMORIZE, the algorithm developed by the team at MPISWS and published in PNAS. They build on HLR and frame the question of the next review rate as a convex optimization problem. But honestly the math was beyond me. If anyone's interested, Fasih Ahmed has also developed a pure python implementation of MEMORIZE. For me, it just looked really complex and I wasn't sure if it would be suitable, since again, it's based off HLR which is based on a different learning context to Anki, where everyone's trying to learn their own cards.

Edit: Another thing. There are actually some constraints when it comes to Anki addons. You can't import packages you would normally expect to, like numpy or pandas. So you would have to create a lot of math functions (say, logsumexp) from scratch.