Changes in version 0.2.0                        

Tracks the Python cat-stack 1.6.0 release. The version floor in
install_cat_stack() is now cat-stack >= 1.6.0 — older Python installs
will hit unexpected keyword argument errors when R forwards the new
parameters listed below.

New parameters in classify()

  - batch_mode, batch_poll_interval, batch_timeout — opt into the async
    batch APIs (OpenAI / Anthropic / Google / Mistral / xAI) for ~50%
    cost savings and higher rate limits. HuggingFace / Perplexity /
    Ollama fall back to synchronous calls.
  - json_retries — per-row retries when the LLM returns JSON that fails
    schema validation. On the final attempt the formatter fallback fires
    (if json_formatter is enabled).
  - json_formatter — three-state (TRUE / FALSE / NULL) control for the
    local JSON-repair model. Default NULL triggers an interactive
    consent prompt on the first malformed row; non-TTY contexts decline
    silently. Requires cat-stack[formatter].
  - two_step_classify — split classification into reasoning + JSON
    formatting steps for weaker models (lower-tier API + local Ollama).
    Auto-enables json_formatter when set.
  - embedding_tiebreaker + min_centroid_size — resolve true
    ensemble 50/50 ties via embedding-centroid similarity instead of the
    default "tie → 0". Adds category_N_resolved_by audit column ("vote"
    or "centroid"). Multi-model ensemble + text input only; not yet
    supported in batch_mode. Requires cat-stack[embeddings].

Behavior changes

  - consensus_threshold = "majority" is now strict majority. A 50/50 tie
    on an even-model ensemble (2-2 of 4, 3-3 of 6, 1-1 of 2) resolves to
    "0", not "1". Matches sklearn's VotingClassifier default and
    standard ensemble literature. Numeric thresholds (e.g.
    consensus_threshold = 0.5) keep >= semantics — the user picked a
    number, they get the literal interpretation. For 2-model ensembles,
    "majority" now effectively requires both models to agree on
    positive. Use 3+ models for a non-degenerate majority vote, or pair
    with embedding_tiebreaker = TRUE.
  - batch_retries default lowered from 2L to 1L to match the Python
    default (changed in cat-stack 1.4.1).

Documentation

  - consensus_threshold man page now describes the strict-majority
    semantics, the 2-model degeneracy, the numeric-input escape hatch,
    and the embedding_tiebreaker companion.
  - Added a new example block showing strict-majority + tiebreaker and
    batch_mode = TRUE.

Python-side fixes that flow through automatically

These are in Python cat-stack 1.6.0 and pass through the reticulate
bridge without R-side changes — listed here so users know the behaviors
they're getting after install_cat_stack(upgrade = TRUE):

  - Google preflight 400 fixed (additionalProperties stripped before
    reaching responseSchema).
  - Per-model batch-job failure isolation — one model's batch failure no
    longer kills the ensemble run.
  - Anthropic batch terminal-state inspection — all-errored batches
    raise instead of silently returning empty results.
  - system_prompt is no longer silently dropped in batch_mode.
  - PDF summary synthesis grounds on actual page text instead of the
    page label.
  - HuggingFace small-model strip-on-5xx (Llama-3.2-1B /
    response_format).
  - Image directory loading is case-insensitive; large images warn.
  - prompt_tune returns system_prompt = "" when no improvement was found
    (instead of returning a non-improving prompt as the default).

                        Changes in version 0.1.0                        

  - Initial release of the R interface to the Python catstack package.
  - Exposes classify(), extract(), explore(), and summarize() for
    domain-agnostic LLM-powered text, image, and PDF classification.
  - install_cat_stack() installs the Python dependency via reticulate.
  - Internal helpers (.strip_quotes(), .as_py_int(), .convert_models(),
    .validate_add_other()) exported for reuse by sibling domain packages
    (cat.survey, cat.ademic, cat.pol, cat.web, etc.).