Changes in version 0.1.0 (2026-09-24)                  

Initial CRAN release.

Ranking

  - rank_records() scores a corpus with an ensemble of open-source LLMs
    served locally by Ollama. Every per-call score is cached under the
    project's cache directory, keyed by digest(list(criteria_hash,
    model, replicate, id, temperature)). An interrupted run resumes from
    the cache on the next call. Cache writes are atomic (temp file +
    rename) so a crash mid-write can't leave a truncated file behind.
  - Optional on_score callback for streaming partial-run progress into a
    UI without editing rank_records() itself.
  - default_ensemble() returns the four-LLM mean ensemble reported as
    the universal ranker in Spillias et al. (2026) (gemma3:27b,
    gpt-oss:20b, mistral-small3.2:24b, qwen3:30b-a3b-instruct-2507;
    three replicates each at temperature 0.7).
  - default_ensemble_light() returns a laptop-friendly alternative
    (gemma3:4b, llama3.2:3b, qwen3:4b, mistral:7b; ~10 GB total).
    Slightly less accurate than the paper ensemble but runs on 8 GB of
    RAM.
  - custom_ensemble() accepts any Ollama-served model, replicate count,
    and aggregator (mean, median, max, topk_mean).
  - clear_cache() invalidates cached scores by model or wholesale so a
    bad run can be re-done without discarding the good models' work.
  - estimate_runtime() gives an order-of-magnitude wall-clock estimate
    scaled by model size and hardware profile (GPU / CPU / throttled).

Screening plan

  - plan_screening() applies the SAFE stopping rule at the paper's
    advance-choosable default (target recall 0.95, minimum coverage
    0.50, run length 50, spot-check n = 200) and returns per-gate
    diagnostics (which gate binds, where each gate would fire).

Corpus

  - read_records() accepts data frames, CSV, TSV, XLSX, and RIS exports
    from Zotero / EndNote / Mendeley / Web of Science.
  - find_duplicates() flags DOI matches, normalised-title matches, and
    (optionally, when stringdist is installed) fuzzy-title
    near-duplicates.

Criteria

  - define_criteria() builds a scope-plus-inclusions object.
  - build_prompt() renders the per-criterion partial-credit prompt used
    in the paper. Point weights and the three scale bands auto-scale
    with the number of criteria, so three-, four-, or five-criterion
    reviews all sum to 100.

Shiny app

  - launch_app() opens a seven-tab workflow (Setup / Corpus / Criteria /
    Rank / Plan / Screen / Report). All user artefacts persist under
    tools::R_user_dir("screenllm", "data") so projects survive across R
    sessions. Refuses to run as root and falls back to a URL-only launch
    on headless (no-DISPLAY) systems.
  - Streaming Rank tab: per-model progress bars and a live scores table
    that grows as each model completes.
  - Report tab: click a strong LLM-vs-human disagreement to review it
    and flip your decision.
  - Setup tab includes an in-app Install Ollama button that detects the
    OS package manager and offers a copy-pasteable install command
    (macOS + Windows can also run it directly in a background process).
  - launch_screening_app() provides the standalone incremental screening
    UI for teams working from an already-ranked list.

Backends

  - backend_ollama() (default): local HTTP client, no API keys, no
    network egress. Fails fast on HTTP 4xx (bad model tag / auth) and
    surfaces the actual error message. Handles reasoning-model families
    (gpt-oss, deepseek-r1, qwen3-thinking, phi4-reasoning) that
    misbehave under Ollama's grammar- constrained JSON mode. Suppresses
    chain-of-thought via think = FALSE; recovers JSON from mixed text
    via a balanced- braces fallback parser.
  - backend_mock(): deterministic mock backend used in examples, tests,
    and vignettes so nothing depends on Ollama being installed.

Setup helpers

  - install_prereqs(preset = "light" | "paper" | "none") walks a fresh
    machine through Ollama installation, daemon startup, and model
    pulls. Detects the OS and proposes an OS-appropriate install command
    (brew / winget / official install script) which the user must
    confirm before it runs. Safe to re-run; a no-op if everything is
    already in place.
  - check_setup(), ollama_health(), pull_model(), ollama_catalog()
    exposed for finer-grained control.
  - detect_gpu() + gpu_status(): hardware detection (Apple Silicon /
    NVIDIA / AMD) plus live clock / VRAM / power query, including a
    "throttled" flag that catches the laptop-on-battery case where a
    dGPU reports 99 % utilisation but runs at idle clocks.

Async workers

  - start_rank_job(), rank_job_status(), rank_job_cancel() spawn ranking
    jobs in a background R process (via callr) so Shiny stays
    responsive; the worker writes throttled progress and per-call scores
    back through a per-project file.
  - start_pull_job() / pull_job_status() / pull_job_cancel() do the same
    for Ollama model downloads.

Reporting

  - summarise_screening() summarises ranked-vs-screened outcomes and
    reports SAFE-derived recall bounds.
  - audit_disagreements() highlights strong LLM-vs-human disagreements
    for reviewer follow-up. Tolerates records that carry a pre-existing
    human_decision column (baked-in ground truth) by stripping decision
    columns before the join.
  - export_worksheet() writes an Excel workbook for offline screening.
  - export_report() renders a self-contained HTML report (open in a
    browser and print-to-PDF to archive).