faSTM - Fast Structural Topic Models

A modern implementation of the Structural Topic Model. faSTM fits the logistic-normal STM (with prevalence and content covariates) via a multithreaded Rust core, with an opt-in stochastic-variational path for large corpora. It is self-contained: text preparation is read from 'quanteda' or 'tidytext' objects, model inspection (labelTopics with FREX/lift/score, findThoughts, semantic coherence, exclusivity, topic correlations) and an estimateEffect() (method-of-composition posterior propagation) are built in. The fitted object is structurally compatible with 'stm' so existing analyses migrate with minimal changes.