[R] Event2Vec: Additive geometric embeddings for event sequences

I’ve released the code for Event2Vec, a model for discrete event sequences that enforces a linear additive structure on the hidden state: the sequence representation is the sum of event embeddings.

The paper analyzes when the recurrent update converges to ideal additivity, and extends the model to a hyperbolic (Poincaré ball) variant using Möbius addition, which is better suited to hierarchical / tree‑like sequences.

Experiments include:

• A synthetic “life‑path” dataset showing interpretable trajectories and analogical reasoning via A − B + C over events.
• An unsupervised Brown Corpus POS experiment, where additive sequence embeddings cluster grammatical patterns and improve silhouette score vs a Word2Vec baseline.

Code (MIT, PyPI): short sklearn‑style estimator (Event2Vec.fit / transform) with CPU/GPU support and quickstart notebooks.

I’d be very interested in feedback on:

• How compelling you find additive sequence models vs RNNs / transformers / temporal point processes.
• Whether the hyperbolic variant / gyrovector‑space composition seems practically useful.

Happy to clarify details or discuss other experiment ideas.