Changes in version 0.0.0.9000                      

A fast, modern Structural Topic Model for R: an stm-compatible fitting
backend on a multithreaded Rust core (topica), plus an effects and
tidy-workflow toolkit on top.

Fitting

  - stm(): fast STM fit via topica's Rust fit_ctm, returning a
    c("faSTM", "STM") object compatible with the stm package's post-fit
    functions (labelTopics, plot, findThoughts, sageLabels, toLDAvis).
    Prevalence and content (SAGE) covariates, spectral / LDA / random /
    custom initialization, and a num_threads knob. ~6x faster than stm
    on the poliblog example (and ~13x on search_k), wall-clock to
    convergence.
  - Multiple content covariates: content = ~ a + b crosses covariates
    into a saturated SAGE content model (one topic-word distribution per
    level combination), beyond stm's single content variable.
  - SVI kwargs (inference, batch_size, tau, kappa) plumbed through ahead
    of topica's STM-SVI landing (topica #231); gated for
    prevalence/content for now.

Covariate effects (estimateEffect())

  - Effects via the method of composition (per-document posterior draws
    pooled by Rubin's rules), drawing from each document's own Laplace
    covariance, so topic-estimation uncertainty propagates into the
    standard errors.
  - Survey weights (weighted least squares) and cluster-robust standard
    errors (sandwich vcov with stm's finite-sample correction).
  - Random effects in prevalence: lme4-style (term | group) terms fit
    per draw and pooled.
  - ame() for average marginal effects; effect_estimates() for tidy
    effect tables; combine for aggregate-topic effects.

Inspection & topic quality

  - coherence() with Mimno, NPMI, and C_V measures;
    semantic_coherence(), exclusivity(), frex_scores(),
    topic_correlation(), topic_corr_graph().
  - label_topics(), sage_labels(), find_thoughts(), topic_terms(),
    topic_proportions(), content_topics().

Model selection

  - search_k(): parallelizes across K (bstewart/stm#262) and selects by
    held-out likelihood or any coherence measure; select_model(),
    many_topics(), make_heldout() / eval_heldout().

Tidyverse & out-of-sample

  - broom generics: tidy() (beta / frex / gamma), glance(), augment();
    predict() for new documents.
  - as_corpus() reads prepared text from quanteda / tidytext;
    from_tidy().

Data

  - poliblog: the CMU 2008 political-blog corpus (the stm vignette
    example).
  - congress: 1,679 balanced U.S. House/Senate floor speeches,
    Congresses 100–111 (party × chamber × time), for the
    multiple-content-covariate and over-time demos.

Vignettes

  - faSTM: the stm vignette, run on faSTM. The stm workflow, fit live.
  - Beyond stm: faSTM's extensions. The tools above, on the congress
    corpus.