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Changelog

Source: NEWS.md

grassr 0.8.0

The calibration release. The delta-hat null moves from 385 prevalence-pooled cells to a fully resolved 11,616-cell lattice, and the lookups interpolate instead of snapping. The lattice combines the completed open calibration program (2,166 blocks, contributed and maintainer-run) with a 9,306-block maintainer campaign that resolved every rater count, added quality nodes at 0.885, 0.945, and 0.98, a prevalence node at 0.875, and a sample-size node at 25 (581 million null draws in total, 50,000 per cell). Off-grid Report Card readings change; on-node readings are unchanged up to storage quantization.

  • The delta-hat null is prevalence-resolved everywhere. The bundled delta_null_ecdf holds 11,616 cells: eleven rater counts (3-25), twelve sample sizes (15-1,000), eleven quality levels (0.65-0.99), and eight prevalence levels (0.05-0.95), every cell prevalence-resolved at 50,000 draws. No cell pools prevalence and no card ever discloses pooling, because there is none. The pooled null’s realized flag size ran to roughly twice nominal at extreme prevalence; the resolved null removes that by construction (realized rates on 144,000 audit panels are at or below nominal in every prevalence and quality stratum).
  • lookup_delta_null() gains a pi_hat argument and interpolates between the calibrated cells bracketing the panel’s design in quality, log sample size, and prevalence; rater count snaps; edge queries clamp with a card disclosure. On 280 held-out off-grid designs the interpolated percentile lands within 0.9 percentile points of each design’s own 25,000-draw truth on average (worst design 7.6, in the direction of under-flagging).
  • The surface q-sweep lookup interpolates log sample size and prevalence (within the fixed-shape logit-normal family; preset profiles stay nearest-key with a note), so surface percentiles and consistency bands are computed for the panel’s own design rather than the nearest simulated one.
  • The null ships in an integer-delta layout (1.7 MB for 11,616 cells) selected by a rate-distortion audit against exact storage; reconstruction error is two orders of magnitude below Monte-Carlo noise (data-raw/build_delta_null_v3.R).
  • The two large reference arrays – the 44,616-cell q-hat surface and the fitted-ICC curves – are stored as integer-delta byte planes rather than doubles, which cuts the bundled data from 8.6 MB to 4.9 MB. Reconstruction is exact, not approximate: the decoded arrays are identical() to the ones that shipped through 0.7.4, so no percentile, band, or example value moves (data-raw/encode_sysdata_for_release.R, design/v0.8.0_surface_encoding.md).
  • Validation: a pre-registered hold-out program (280 off-grid designs, 25,000 draws each) scores the interpolating lookup against direct simulation truth; an end-to-end audit measures realized flag size through the production path on 144,000 panels; and the aligned-case out-of-bag re-measurement fires 5.25% and 1.15% against nominal 5% and 1% (simulation/v070_program/g2_holdout/). Two defects found by the hold-out program during development were fixed before release: a prevalence-bridge omission in the matched-null selection and a near-ceiling quality gap closed by the 0.945/0.98 nodes.
  • The open calibration program is complete, and its three functions (grass_calibration_manifest(), grass_contribute(), grass_verify_contribution()) are removed along with the bundled block manifest. The program’s blocks were defined against the earlier pooled null; the lattice that supersedes it is finished, so the manifest no longer describes work that needs doing. Thanks to the contributors whose blocks are part of the shipped lattice; they are credited in the repository’s contributor record.

grassr 0.7.4

CRAN release: 2026-07-21

CRAN resubmission fixes. No computational changes; cards are identical to 0.7.2 output apart from the version stamp.

  • The DESCRIPTION explains every acronym (PABAK: prevalence-adjusted bias-adjusted kappa; AC1: first-order agreement coefficient) and cites the method sources with DOIs (Byrt, Bishop, and Carlin 1993; Gwet 2008; Fleiss 1971; Dawid and Skene 1979; Hui and Walter 1980).
  • All examples are unwrapped and run during checks. The latent_class_fit() example had an unnecessary \dontrun{} wrapper; the four \donttest{} wrappers elsewhere dated from when those code paths were slower. Every example now completes in under a second.
  • Fixed a checkRd “Lost braces” NOTE in ?grass_roadmap (a stray LaTeX-style thousands separator).
  • The vignette’s “Extending the calibration” section records the live state of the open calibration program: 165 of the 2,166 blocks had been run and verified by outside contributors when this release was built. Contributed cells fold into the bundled calibration in a future data release.

grassr 0.7.2

The open-calibration release. The calibration’s remaining bounds are compute limits rather than method limits, and the package now includes the machinery for anyone to remove them.

Contribute compute from the installed package

Three new functions:

  • grass_calibration_manifest() – the 2,166 open calibration blocks (null tail top-ups, a prevalence-stratified null, intermediate rater counts), each a seeded bite-sized cell.
  • grass_contribute() – benchmarks the machine, answers “what would five hours buy?” (dry_run = TRUE), runs selected blocks from their declared seeds, and writes a submission bundle with checksums and session information. Runs are bit-reproducible at a pinned package version. No network access; writes only to the caller’s directory.
  • grass_verify_contribution() – checks a bundle by checksum, completeness, and seed replay, for contributors before submission and maintainers at intake.

Submissions land as pull requests on the repository’s calibration-contrib branch; see CONTRIBUTING.md (new). Every block’s seed (20300000 + block_id) is disjoint from the shipped programs’ seeds, so contributed draws can never replay shipped draws.

The vignette gains a closing section, “Extending the calibration”, that walks the contributor path from the bundled manifest to the pull request.

grassr 0.7.1

The card-redesign release. The headline reading changes from a nearest-cell surface percentile with a four-band adjective to a pooled percentile plus a consistency band on panel quality, and delta_hat becomes an implied-quality spread. Supersedes the 0.7.0 tarball.

Single vignette

The reporting-card and methods-companion vignettes are merged into one: vignette("grassr") carries the whole story, from why fixed labels mislead through two synthetic worked panels, the full API, and the calibration machinery behind the flag.

Pooled percentile + consistency band (sweep redesign)

position_on_surface() now evaluates the observed coefficient against every calibrated quality level at the matched design – a sweep – and reports three read-outs of one object:

  • Pooled percentile – the trapezoid-weighted average of p(q) = P(coefficient <= obs | quality q, this design) over the calibrated quality axis: the coefficient’s position within the design’s achievable agreement range, monotone in the coefficient by construction. This retires the nearest-q_hat-cell percentile, whose cohort selection by a statistic derived from the coefficient made it a non-monotone sawtooth (7-9 full 0-100 cycles across the PABAK range at k=8-10, N=200; five-agent panel review 2026-07-05).
  • Consistency band – the 95% test-inversion band on panel quality q_hat: the quality levels whose sampling distributions are consistent with the observed value at this design. q_hat is promoted to the card via this band (previously internal scaffolding, never printed).
  • sweep – the full p(q) profile (the sweep-ridgeline graphic).

The stipulated four-band partition (Poor / Moderate / Strong / Excellent) and the modal-band bootstrap qualifier (decisive / moderate / weak), with their 0.60 / 0.90 cuts, are retired. The bands and band_labels arguments are removed from position_on_surface() and grass_report() outright, along with the legacy delta_thresholds override on check_asymmetry() / grass_report() and the never-wired grass_spec_*() constructor family (and the internal spec-dispatch and retired classify() machinery behind it). None of this ever shipped in a CRAN release, so it is gone without deprecation shims.

Correctness and determinism

  • Fixed an inversion in the consistency band’s two degenerate branches: an observation below the achievable range of every calibrated quality reported a band open above the grid (a worst-possible panel printed as consistent with near-perfect quality). Off-range observations now open the band on the correct side, with regression tests in both directions and a monotonicity test on the band endpoints.
  • The divergent-branch latent-class bootstrap is now seeded inside grass_report(), so the per-rater confidence intervals on a card are deterministic: the same rating matrix always prints the same card. latent_class_fit() restores the caller’s RNG state after a seeded call.

delta-hat is now an implied-quality spread (Option B)

delta_hat becomes the max-min spread of the agreement family’s implied panel qualities (PABAK, mean AC1, Fleiss kappa), in quality percentage points: each coefficient inverts to its own q_hat on the shared (q, pi_+) reference, and their spread measures cross-coefficient model discordance in interpretable units (Option B, ratified 2026-07-05). The previous definition – spread of surface percentiles across four coefficients including Krippendorff alpha – ran through the retired sawtooth machinery. Krippendorff alpha left the panel at 0.6.0 and does not re-enter; ICC is never in delta_hat. The flag convention (percentile on the matched (k, N, q_hat) null, >= 95th caution, >= 99th divergent) is unchanged in form.

  • The delta null was REGENERATED under the Option-B implied-quality definition (2026-07-06): 440 (k, N, q) ridges, ~22M production-pipeline draws, at least 46,002 per ridge. The shipped delta_null_ecdf carries the 385 k >= 3 ridges on a fine 1% + 99.5% probability grid. At k = 2 the two-coefficient family implies identical quality by construction (the null is a point mass at zero on all draws), so delta_hat reports not_applicable there and asymmetry assessment routes to the pairwise/bounds path. Tie runs (point masses) use the mid-p convention. Divergent-flag power at the same asymmetry roughly doubles relative to the retired percentile-spread definition (e.g. TPR 0.82 at mean-norm A = 0.20 pooled over designs, vs 0.38 at the modal design previously); realized null flag rates are slightly conservative (caution 3.7% vs nominal 5%, divergent 0.6% vs 1%).

Bug fixes

  • Zero-plateau percentile. delta_null_percentile() now resolves a value at or below the null’s lowest stored quantile to the TOP of the zero/floor plateau. At small N the null often has a point mass at delta_hat = 0 reaching well past the 1st percentile; 0.7.0 reported a value at the plateau’s lowest prob, so identical raters printed “1.0 percentile” on cells where P(D <= 0) was ~35%.
  • grass_report() lme4 NA-skip. A missing lme4 (Suggests) or a failed glmer no longer hard-errors grass_report(): non-finite panel entries (e.g. ICC when lme4 is unavailable) are dropped before positioning and the coefficient degrades to “absent from the panel”. 0.7.0’s NEWS claimed this fix, but the 0.7.0 code landed it in check_asymmetry() only; the grass_report() positioning loop still errored. It is genuinely fixed here.

Documentation layer

  • DESCRIPTION, the roxygen sources for check_asymmetry(), grass_report(), position_on_surface(), and ?grass_roadmap, and the README were still describing the 0.6.x conventions (per-(k, N) size-alpha thresholds c(9.25, 11.75), the four-band adjective, the four-coefficient percentile spread). All corrected to the 0.7.1 card convention. Suggests-dependent examples wrapped in \donttest{}.

Hygiene

  • Removed macOS duplicate " 2" file strays from the source tree.
  • inst/preview/ and dev-scratch example scripts excluded from the build via .Rbuildignore.
  • data-raw/round_sysdata_for_release.R skips sysdata objects that are absent (the retired delta_thresholds_lookup) instead of erroring.

grassr 0.7.0

The calibration release. Two structural changes, both from the v0.7.0 calibration program (July 2026).

delta-hat flag redesigned: percentile on the matched null

The per-(k, N) threshold table is retired. check_asymmetry() and grass_report() now report delta-hat as its percentile on the null distribution of delta-hat at the matched (k, N, q-hat) cell, with flags as conventions on that percentile (>= 95th caution, >= 99th divergent). Motivation: recalibration across panel quality showed the old single-quality (q = 0.85) thresholds were mis-sized off their calibration point, and at small N the null’s extreme quantiles are not stably invertible; the ECDF-position reading is stable, holds its false-positive rate at every quality by construction, and matches how every other quantity on the card is read.

  • New sysdata object delta_null_ecdf: 440 (k, N, q) null ECDFs generated through the production pipeline (24.85M panels, >= 50k draws per cell), with per-cell instability metadata.
  • New card fields: delta$delta_percentile, delta$matched_null; delta$thresholds now carries the implied pp cuts (95th/99th of the matched null) as context.
  • delta_thresholds argument deprecated (honored with legacy semantics plus a warning); delta_thresholds_lookup removed.
  • Bug fix: a missing lme4 (Suggests) no longer hard-errors check_asymmetry(); non-finite panel entries are dropped before positioning.

Reference surface densified

empirical_q_hat_surface grows from 10,140 to 44,616 cells: N in {15, 20, 30, 50, 75, 100, 150, 200, 300, 500, 1000} (was {50, 200, 1000}) and a unified 13-point q-grid at every k. Lookups that previously clamped (e.g. N = 22 -> 50) now resolve to near cells. sysdata is 6.91 MB (storage-precision reduction to follow before any CRAN submission of this line).

grassr 0.6.2

CRAN resubmission release. No changes to package functionality.

CRAN check surface reduced

The 0.6.1 incoming pretest failed on CRAN’s Windows R-devel machine with silent process deaths in the test and vignette-rebuild steps (no diagnostics in either log; the same tarball checks clean on the same R revision – 2026-07-03 r90206 ucrt – on independent Windows infrastructure, and on the Debian pretest). 0.6.2 minimizes what CRAN machines execute, which is recommended practice regardless:

  • Tests: on CRAN, a fast deterministic smoke subset now runs (~190 assertions: metric arithmetic, input normalization, pairwise agreement, print methods, and the sysdata regression anchors). The full ~650-test suite runs on every push via the GitHub Actions matrix (windows/macos/ubuntu x devel/release).
  • Vignettes: both vignettes are precomputed from .Rmd.orig sources (kept in the repository, regenerated with knitr::knit()); CRAN machines render static markdown only.
  • delta_thresholds_lookup in sysdata is now a plain data.frame; it previously carried a stray data.table class from its build script although the package never depended on data.table.

grassr 0.6.1

CRAN resubmission release. No functional changes.

Package renamed: grass -> grassr

CRAN declined to release the archived name ‘GRASS’ (package names are persistent, case-insensitively). The package is now ‘grassr’. All function names, arguments, and behavior are unchanged – the API keeps the grass_* prefix, matching the GRASS framework the package implements.

Bundled reference surfaces rounded to 5 decimals

The reference surfaces in R/sysdata.rda are now stored at 5-decimal precision (previously full 64-bit doubles), shrinking the file from 9.55 MB to 2.83 MB and the tarball to under 5 MB. The discarded digits are Monte Carlo noise: the surfaces’ simulation error is on the order of 1e-2, three orders of magnitude above the rounding. No lookup, threshold, test, or reported number changes at reported precision. The full-precision surfaces and the rounding script are archived in data-raw/.

grassr 0.6.0

First CRAN release. Two behavioral changes and a legacy-API removal, plus CRAN compliance fixes.

Behavioral change — Krippendorff’s alpha removed from the Report Card panel and delta-hat

In the binary fully-crossed designs this package targets, Krippendorff’s alpha coincides with Fleiss’ kappa asymptotically (median absolute difference 0.00024 across the 10,140-cell calibration grid; the asymptotic argument is in the accompanying paper’s Appendix A.2). The alpha row added no information to the Report Card and its small-sample deviation from Fleiss’ kappa could tip borderline delta-hat readings across a calibrated threshold on noise alone. As of 0.6.0:

  • compute_panel() no longer returns krippendorff_a; the Report Card panel is PABAK / AC1 / Cohen’s kappa at k = 2 and PABAK / AC1 / Fleiss’ kappa / ICC at k >= 3.
  • delta_hat is the surface-percentile spread over PABAK, mean AC1, and Fleiss’ kappa. The bundled per-(k, N) thresholds hold for the 3-coefficient spread (sensitivity check: sub-pp shift, within MC-SE).
  • obs_krippendorff_alpha() is newly exported for users who need the value for cross-study comparison, and position_on_surface(metric = "krippendorff_a") still positions it on its closed-form reference.

Legacy API removed

The pre-0.2.0 grass_result object and its consumers were deprecated at 0.2.0 and have been unreachable from the public API since then (no exported constructor produced a grass_result). Removed: grass_format_report(), grass_methods(), grass_report_by(), print.grass_result(), summary.grass_result(), tidy.grass_result(), as.data.frame.grass_result(). The modern equivalents are print(grass_report(Y)), summary(), as.data.frame(), and the Methods-paragraph templates in the reporting-card vignette.

CRAN compliance

  • All R sources ASCII-clean; Rd math fixed in pairwise_agreement; broken Rd cross-links resolved.
  • bootstrap_delta_B argument documented.
  • ggplot2 NSE column names declared via globalVariables().
  • DESCRIPTION rewritten (placeholder self-citation removed).

grassr 0.5.2

Bug-fix and sysdata-correction release. The v0.5.0 / v0.5.1 bundled empirical_q_hat_surface had a build-time mislabeling bug at k = 2 that paired empirical q-hat distributions with the wrong scenarios for roughly half of all k = 2 sysdata cells; a corrected k = 2 sub-grid plus a high-q augmentation are bundled here. Internal logic is unchanged; the 0 FAIL test posture is preserved and 12 new regression-anchor tests pin known design points.

Behavioral change (BUG FIX) — k = 2 surface percentile lookup corrected

  • Cause. The script that originally built the k = 2 portion of empirical_q_hat_surface (grass/data-raw/extend_empirical_bands_k2.R, pre-2026-05-10) concatenated per-rep batches via per_rep <- c(per_rep, batch) over list.files(...) (alphabetical filename order), then assigned names(per_rep) <- s$scenario_id assuming the resulting list was in the same order as summaries.rds. The two source machines (laptop / sebastian) wrote scenarios in randomized scenario-id order within each batch, so the alphabetical concat misaligned 864 of 1728 k = 2 entries. The empirical q-hat distribution under scenario_id = X was, in half of all k = 2 cells, actually the q-hat distribution from a different scenario.

  • Effect. position_on_surface(metric, pi_hat, k = 2L, N, ...) returned percentiles that were correct for ~50% of the (F-key, q_true, N) cells and silently wrong for the rest. The most visible manifestation was the published Soares (2021) HEART-score panel: the v0.5.0 surface placed it at the 96th percentile; the corrected surface places it at the ~9th percentile (a structural inversion, not a small drift).

  • Fix. The replacement sim (paper1_2_merged/output/multirater_sim_k2_extension/) writes one RDS per scenario keyed by scenario_id in the filename, so the alphabetical-concat bug class is structurally impossible. The new extend_empirical_bands_k2.R reads per-scenario files directly. k = 2 q-true grid is densified from the previous 6-point set {0.60, 0.70, 0.80, 0.85, 0.90, 0.95} to the canonical 11-point set {0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, 0.97, 0.99}. F-key set densified from 16 to the canonical 52 (matches k >= 3). Total k = 2 scenario count: 1728 -> 1716 (slightly lower because the new sim does not include the 5 asymmetric profiles per cell that were inappropriately mixed into the symmetric-anchor surface in v0.5.0; those are preserved separately at paper2/simulation_output/k2_asym_sim/ for non-surface uses such as the §4.2 op_strong worked example and pairwise-PABAK validation).

  • Migration impact. Anyone who computed grass surface percentiles on a k = 2 panel with v0.5.0 or v0.5.1 should re-run under v0.5.2. Aggregate downstream: the merged GRASS paper §5 Soares anchor was written against the buggy v0.5.0 surface and reports 96th pct; corrected reading is ~9th pct, with the Klein 2018 panels stable (50th, 51st pct unchanged) and the Hinde 2025 k = 10 panels unaffected (canonical k >= 3 surface; built correctly in v0.5.0).

Robustness checks — high-q surface coverage

Three targeted sub-sims added at v0.5.2 verify the surface lookup behaves as designed at high-quality boundary cells where per-cohort empirical distributions are tightest and grid coverage matters most. These are robustness checks of the framework’s reach into the high-q corners of the design space, not bug fixes.

  • k = 2 q = 0.97 (multirater_sim_k2_q097_aug/, 156 scenarios). At k = 2 with the canonical 10-point q-grid, panels with closed-form-inverted q_hat in (0.96, 0.98) snap to q_true_near = 0.99, where the empirical distribution is centered above q_hat. Adding q = 0.97 to the grid lets such panels snap to a cohort whose empirical distribution brackets q_hat, returning a centered percentile.

  • k = 25 q in {0.92, 0.97} (multirater_sim_k25_q092_097_aug/, 312 scenarios). At k = 25 with N = 1000, per-cohort q-hat sd is ~0.003 (panels are tightly informative), so grid spacing of 0.05 leaves cells in the gap between adjacent cohorts. Adding q = 0.92 and q = 0.97 closes the 0.90–0.95 and 0.95–0.99 gaps.

  • k = 2 + k = 25 q = 0.94 (multirater_sim_q094_aug/, 312 scenarios). A residual 0.92–0.95 midpoint gap surfaced when tab:card_summary row 6 (k = 25, PABAK = 0.78, q_hat = 0.9416) still snapped to q = 0.95 after the q = {0.92, 0.97} augmentation. Adding q = 0.94 to k = 2 and k = 25 lets such panels snap to q = 0.94 cohort and return a centered percentile.

Net q-grids after all v0.5.2 augmentations: - k = 2: 12 points {0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.94, 0.95, 0.97, 0.99} — canonical 10-point grid plus 0.94 and 0.97. - k = 25: 13 points {0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.92, 0.94, 0.95, 0.97, 0.99} — canonical 10-point grid plus 0.92, 0.94, and 0.97. - k in {3, 5, 8, 15}: 10 points (canonical) — unchanged.

The augmentation sims use a per-scenario rds layout (kill-safe, resume-safe) and skip glmer (the bundled surface uses only logit_mixed_icc_oracle, not the glmer-fitted ICC; glmer was the slow path that initially stalled the k = 25 run before being disabled).

Why three sub-sims rather than one denser grid up front? Each boundary gap surfaced through diagnostic work on a specific paper anchor (post-hoc figure A at k = 2; tab:card_summary row 6 at k = 25). We added grid points where the diagnostics surfaced gaps, keeping the augmentation tight to the actual coverage need rather than running an order-of-magnitude denser global grid. The structural fix — bilinear interpolation across two adjacent q_true cohorts in lookup_empirical_q_hat() — is designed in grass/design/v0.5.3_lookup_interpolation.md and queued for the next minor release; it would resolve the boundary-snap pathology without requiring ever-denser grids, but the augmentations bring the v0.5.2 surface to a defensible coverage level for the paper’s specific anchor cells.

Defensive guards in sysdata builders

  • Stronger preconditions. extend_empirical_bands_k2.R and the new augment_empirical_bands_k2_q097.R both assert setequal(names(per_rep), as.character(s$scenario_id)) and !any(duplicated(names(per_rep))) after labeling. These would have caught the original mislabeling at write time.
  • Recommended for v0.5.3. Mirror the same preconditions in build_empirical_bands.R (canonical k >= 3 builder) — currently safe because multirater_sim_v3_combined_full.rds saves per_rep named by scenario_id, but the silent positional-fallback at L171-173 could mask a future regression.

New regression tests

  • tests/testthat/test-sysdata-regression-anchors.R (12 tests, all PASS): pins surface-percentile lookups for the merged GRASS paper’s §4 / §5 anchors. Soares now bracketed below 20 pct (was inflated to 96 in v0.5.0), Klein AE/PR ~50 pct, Hinde DSM-5-TR ~10 pct, Hinde ICD-11 PTSD ~95 pct, §4.1 PABAK 40-55, §4.2 PABAK 30-50 + AC1 50-65 (the divergent split), §4.3 unclamped 30-80 (the q = 0.97 augmentation guard). Plus three structural invariants on the k = 2 index. Regenerating sysdata in the future without producing these values will fail-fast.

Sysdata size and shape

  • empirical_q_hat_surface: 9,360 -> 10,140 scenarios after the fix and the three v0.5.2 augmentations (k = 2 went 1728 -> 1872; k = 25 went 1560 -> 2028; k in {3, 5, 8, 15} unchanged at 1560 each).
  • File size: 9.0 MB -> 9.1 MB on disk (xz-compressed).

Report Card render fixes (R/format.R)

Two render-layer changes surfaced by the 2026-05-11 paper-vs-code audit. Neither changes computation; both affect what print(grass_report(Y)) shows on screen.

  • Aligned and caution flags now render all panel coefficients, not just the primary. Previously, under the aligned/caution branch, format.grass_card() emitted only the primary coefficient line; the other agreement-family coefficients (and ICC) were computed and stored in card$panel but suppressed in the print output. The new render lists every coefficient with its observed value and surface percentile; the primary carries the qualifier (decisive / moderate / weak) inline and a <- primary marker. This matches the §3.2 dictionary table in the paper (coefficient field “lists every coefficient with its own percentile”) and the §3.1 Quick Start example output.

  • [distribution-sensitive] marker now rendered by format.grass_card() on the ICC line. Previously the marker was emitted only inside print.grass_asymmetry_panel, so the Report Card a user saw via print(grass_report(Y)) carried no marker on ICC. The marker now renders in both the aligned/caution branch and the divergent branch, alongside any [oracle-fallback] / [oracle-icc] reference-source markers.

  • tau2_hat removed from the inline sample header. The sample line now reads sample = k raters, N = ..., pi_hat = ... only; the per-panel τ̂² lives in card$sample$tau2_hat and prints in the Notes section when a fitted-ICC F_key is picked via glmer.

What did NOT change

  • ICC reference surface (fitted_icc_reference_curves) is unchanged.
  • δ̂ threshold lookup (delta_thresholds_lookup) is unchanged.
  • Closed-form reference_binary lookup is unchanged.
  • All exported function signatures and return objects are unchanged.
  • Paper §4 worked examples (k = 5 aligned + divergent) reproduce exactly under the corrected sysdata; the fix touches k = 2 only.

grassr 0.5.1

Cosmetic and documentation release. No internal-logic, sysdata, or test changes; the 0 FAIL across 194 test files posture from v0.5.0 is unchanged.

Behavioral change

  • Print marker rename: [F-shape sensitive] → [distribution-sensitive]. The earlier label collided with the F-statistic / ANOVA reading. The marker references the full subject-prevalence distribution F, not an F-statistic; the rename matches the merged GRASS paper’s manuscript wording. Affects the print method on grass_card and grass_asymmetry_panel objects only. ICC panel rows now print as no [distribution-sensitive] in the in_delta_hat column. Downstream code that parses print output (rare) needs an update; return values, sysdata, and signatures are unchanged.

  • Print format simplified: separate band = ... line dropped. The percentile and qualifier now appear inline as Nth pct (decisive) per the manifesto-locked Report Card format. The internal card$coefficient$band field is still computed and remains in the object slot for callers that need it; only the printed rendering changed. Divergent panels print panel-agg. = suppressed (divergent) instead of band = suppressed.

Vignette

  • methods-companion.Rmd expanded by ~120 lines absorbing Tier-1-cut content from the merged GRASS paper:
    • §3 ICC and the subject-prevalence distribution — full distribution-sensitivity discussion, bundled-grid coverage, Koo-Li variant decision tree.
    • §5 δ̂ thresholds and operating characteristics — Neyman-Pearson threshold derivation, snap-to-cell behavior, k = 2 collapse, shared-vs-split-bias detection comparison.
    • §5b Pairwise PABAK and pooled-reference recovery — structural why-PABAK-at-pair-level argument, identifiability conditional, 54-cell recovery numbers (94% direction recovery / 90-97% rank correlation / 98% in-majority direction / Se-tilde mean bias 0.012). The vignette is the canonical landing for practitioners wanting depth beyond the published paper.

Documentation

  • README.md updated with v0.5.1 wording, refreshed example output, and a ref = "v0.5.1" install pin.
  • man/*.Rd regenerated from roxygen blocks to reflect the marker text change throughout function-level documentation.

grassr 0.5.0

Three substantive landings drive this release. (1) Cross-coefficient divergence delta_hat is now computed over the agreement family only (PABAK, mean AC1, Fleiss kappa, Krippendorff alpha). Each has a closed-form reference depending on (q, pi_+) only, so the cross-family spread is DGP-robust at the panel level. ICC remains on the Report Card panel rows but does NOT enter delta_hat: ICC’s reference surface depends on the full F-shape variance structure rather than (q, pi_+), and a panel whose true F does not match the bundled logit-normal reference can drift by ~20 pp at small designs. An earlier delta_hat scope including ICC produced ~80% FPR at the diagonal null under matched DGPs and was abandoned in favor of the agreement-family-only construction adopted here (see § “Behavioral change” below; paper §3 ICC scope and Appendix E for the full quantitative case). (2) The bundled fitted_icc_reference_curves sysdata object is rebuilt from a unified Julia MixedModels.jl simulation extending coverage to k in {3, 5, 8, 15, 25} x N in {30, 50, 75, 100, 200, 300, 500, 1000} on a 14-point q-grid spanning [0.35, 0.99]. Across 1,000 random common-applied-design panels the ICC clamp rate is 7.7% (residual clamping concentrated at small N: 14.3% at N=30; or small k: 16.3% at k=3). (3) The delta_thresholds_lookup table is freshly derived under the agreement-family delta_hat definition (4-coefficient max-min) for size-alpha calibration.

Behavioral change

  • delta_hat over the agreement family only. The panel-level card$panel data frame now carries an in_delta_hat logical column (TRUE for PABAK, AC1, Fleiss kappa, Krippendorff alpha; FALSE for ICC). The print method shows the column inline so users can see which coefficients enter the cross-family spread. The notes vector for any divergent / caution panel records the v0.5 scope decision: ICC’s surface depends on the full F-shape and is reported separately on the Report Card, but does not contribute to the asymmetry signal.

  • ICC marked [F-shape sensitive]. The print method labels ICC’s panel row with the [F-shape sensitive] marker, signaling to the reader that ICC’s percentile reading carries a misspecification cost the agreement family does not. The cost is quantified in Appendix E of the merged GRASS paper.

New

  • Per-(k, N) threshold lookup – grass_report() and check_asymmetry() now default delta_thresholds = NULL, which triggers a lookup against the delta_thresholds_lookup table (k in {2, 3, 5, 6, 8, 10}, N in {15, 20, 30, 50, 75, 100, 200, 500}). When the exact (k, N) has both thresholds calibrated, those are used directly. Otherwise the lookup searches all fully-calibrated cells and returns the nearest one in (k, log10(N)) space (each axis range-normalized). Falling back to the modal default c(9.25, 11.75) is reserved for the impossible-with-bundled-sysdata case of an empty calibration table; in normal operation users always receive thresholds calibrated somewhere on the grid.

    The card field card$delta$thresholds_source records one of "calibrated_at_k_N", "snapped_to_nearest_calibrated", "default_fallback", or "user_supplied".

  • Print-method visibility – the Report Card now shows a thresholds = (caution, divergent) [source] line directly under the delta = ... pp (flag) line, so the user sees which pair was applied and why.

  • ICC reference-path transparency – position_on_surface() now records which path produced each coefficient’s reference surface in a structured reference_used field (one of "closed-form", "fitted-icc", "oracle-icc-fallback", "oracle-icc-explicit", "user-supplied"), surfaced in card$panel$reference_used. When the fitted-ICC reference is unavailable at the user’s (k, N) and the function falls back to the older oracle reference, the print output now marks the affected coefficient with [oracle-fallback] in addition to the existing notes-vector entry. Closes a quiet fallback that previously only showed up in the bottom-of-card notes block.

Coverage extension

  • fitted_icc_reference_curves rebuilt for the v0.5 grid. The bundled GLMM-gap-corrected ICC reference now covers k in {3, 5, 8, 15, 25} x N in {30, 50, 75, 100, 200, 300, 500, 1000} on a 14-point q-grid spanning [0.35, 0.99] – 2,080 (F_key, k, N) lookups across 52 F_keys (logit-normal grid + 4 discrete-mixture presets). Built from a unified Julia + MixedModels.jl simulation averaging 500-1000 fitted-ICC reps per cell. Per-cell mean_icc values are monotone-enforced along q (cummax) and linearly interpolated onto the package’s 501-point internal q-grid. Builder source: data-raw/build_fitted_icc_reference_v0.4.R. Sysdata size ~8.9 MB (xz-compressed).

  • Calibrated delta_thresholds_lookup for delta_hat_4. The per-(k, N) threshold table is built against the agreement-family delta_hat definition over a 1,440-cell sim grid (k in {2, 3, 5, 6, 8, 10} x N in {15, ..., 500} x A in {0, ..., 0.30} x mu in {0.05, 0.20, 0.50, 0.80, 0.95}, 1,000 reps per cell). Cells where no threshold satisfies the size-alpha constraint at t in [0, 50] pp return NA and the Report Card defaults to caution-by-default per-rater routing per the paper’s Appendix D specification.

  • §4.1 worked example clean under v0.5 sysdata. The §4.1 design point (k=5, N=1000, mu=-1.386, tau2=1.0) resolves via the fitted-ICC path with no [oracle-fallback] marker; the previously load-bearing ICC clamp at this case is closed.

grassr 0.1.2

Round 5 — spec-dispatch architecture + reviewer 5/5 items.

Architecture

  • Spec-dispatch. grass_report() now takes spec = grass_spec_binary() as its primary argument. Metric families (binary today; ordinal, multirater, continuous planned) are selected by the spec constructor rather than by family-specific function names. One package, one entry point, extensible to future families without API churn. See ?grass_roadmap.
  • Stub constructors grass_spec_ordinal(), grass_spec_multirater(), grass_spec_continuous() return valid placeholder specs so users can write release-ready code; passing one to grass_report() errors with a pointer to ?grass_roadmap.
  • Result objects now carry a $spec slot holding the spec used for the call. All downstream verbs (print, plot, as.data.frame, tidy, grass_methods, grass_format_report) dispatch on result$spec$family.

New features

  • reference_level argument on grass_spec_binary() accepts 0.70 / 0.80 / 0.85 / 0.90. Each band is the analytical Youden-J-optimal expected metric value at Se = Sp = reference_level, in closed form under conditional independence of the two raters given the true class. Previously only "high" (= 0.85) and "medium" (= 0.70) were available.
  • grass_methods(result) — GRRAS-compliant manuscript methods paragraph pre-filled with study numbers. Supports format = c("markdown", "latex", "plain"). Templated so that future metric families slot in without touching the dispatcher.
  • .parallel = TRUE on grass_report_by() — backed by future.apply::future_lapply() with a progressr::progressor() handler. Respects the user’s active future::plan(). Optional dependencies; clean install-command error if absent.
  • ?grass_roadmap help page — framework taxonomy, ecosystem architecture, what is implemented today, what is planned.
  • “The grass ecosystem” vignette — one page on the one-package-many-families design.

Breaking / estimand changes

  • Reference curve estimand changed. Pre-0.1.2 shipped a simulation-derived ROC classification threshold (a classifier cutpoint separating rater scenarios of different ground-truth quality bands). 0.1.2 ships the expected metric value at the Se = Sp calibration point — a cleaner analytical estimand with a closed-form interpretation. Numeric values differ at reference_level = 0.85 vs pre-0.1.2 reference = "high" for most prevalences. Full Monte-Carlo regeneration with empirical confidence bands is deferred to a future release.
  • Legacy reference = "high" / "medium" / "none" argument on grass_report() is deprecated (soft — emits a one-time message). "high" maps to reference_level = 0.85, "medium" to 0.70, "none" to NULL.
  • grass_reference(quality = ...) is deprecated; use grass_reference(reference_level = ...).
  • Internal thresholds sysdata renamed to reference_binary (long-form: prevalence × reference_level × metric). grass_reference_table() now returns the long form by default and accepts an optional reference_level filter. Package-internal only; user impact limited to anyone previously calling grass:::thresholds.

grassr 0.1.1

Reviewer-driven polish (round 4).

New in 0.1.1 (round 4)

  • response = argument — stats-modelling alias for rater_cols = (wide) and rating = (long). Conflicting canonical + alias errors with a “disagree” message.
  • grass_format_report(ci_width = TRUE) — appends "; kappa CI width = 0.26, wide" with half-width cutpoints calibrated for kappa: tight < 0.10, moderate < 0.20, wide >= 0.20. Default FALSE (opt-in). Kappa Wilson-logit CIs run 2-2.5x wider than component Se/Sp CIs at the same N, so these cutpoints are higher than the Se/Sp conventions from diagnostic accuracy reporting.
  • broom::tidy.grass_result() long-form — 9 rows (3 metrics × 3 quantities: estimate, reference, distance). Columns: metric, quantity, value, conf.low, conf.high, n, prevalence. conf.low/conf.high populated only on kappa estimate. value (not estimate) is the generic column so reference/distance rows do not read as point estimates without CIs. Plots via ggplot(aes(metric, value, colour = quantity)) without a reshape.
  • grass_report_by(data, group, ...) — splits data by group (bare symbol or string), runs grass_report() per subset, returns a tidy data.frame with .cohort column carrying the group value. Uses as.data.frame(compact = TRUE) internally.
  • as.data.frame(r, compact = TRUE) argument (already in earlier 0.1.1 draft; surface again here).
  • Wide-format error now adds a soft id_col = hint when exactly one column is non-binary: “Column ‘xxx’ looks like an identifier; you could try id_col = 'xxx'.” No hint when ambiguous (all columns binary-looking).
  • reset_grass_warnings() — clears the once-per-session message cache so loops over many studies with consistently named columns re-emit the fallthrough warning.
  • ?grass_report now has a “Sample size caveats” section documenting the two N thresholds (N < 10 warning, N < 30 print note).

Breaking

  • Default format is now "wide" (was "matrix") across grass_report(), grass_compute(), grass_prevalence(). Users passing a data.frame no longer need to name the format.
  • grass_format_report() default is now ascii = TRUE (was FALSE). Safer for Slack, Markdown, and non-UTF-8 locales. Pass ascii = FALSE for a Unicode kappa in an RStudio console.
  • validate_prevalence() now errors on exactly 0 or 1 (degenerate cases with no reference defined). Values in (0, 0.01) and (0.99, 1) still warn and clamp.

New

  • id_col = NULL argument on format = "wide" — drop a subject-identifier column before the two-rater detection. Example: grass_report(df, id_col = "subject_id").
  • as.data.frame.grass_result() now returns 19 columns including kappa_ref, kappa_distance, PABAK_ref, PABAK_distance, AC1_ref, AC1_distance, reference_quality, and regime_note. Pass compact = TRUE to drop regime_note when binding many rows.
  • print.grass_result() prints a “sensitive to sampling noise” caveat in the reference-comparison section when N < 30. (This is distinct from the N < 10 warning on grass_compute(), which flags the metrics themselves as unreliable at very small N.)

Fixes

  • When no keyword rule matches, pick_positive() now escalates from a message() to a one-time warning() with explicit override guidance. This prevents silent prevalence inversion when rater levels are clinical terms (e.g., "abnormal", "normal") that fall outside the yes / 1 / true / positive keyword list. Flagged by Dr. M. Chen.
  • Three-column wide-format error message now names both fix options (rater_cols = c(...) and id_col = "...") and lists the actual column names present, without guessing which are raters. Flagged by J. Okafor.

Still v0.1.0 entry-points

grass_compute(), grass_report(), grass_reference(), grass_reference_table(), grass_prevalence(), grass_format_report(), grass_plot().

grassr 0.1.0

Initial local draft.

Public API

  • grass_compute() — raw metric panel from 2x2 or rater data
  • grass_report() — contextual profile: metrics + PI + BI + regime + reference
  • grass_reference() — look up reference curves at a given prevalence
  • grass_reference_table() — full internal reference-curve table (23 prevalence points, High and Medium calibration levels)
  • grass_prevalence() — estimate prevalence from rater marginals
  • grass_format_report() — one-line paper-ready summary of a result
  • grass_plot() — plot a grass object

Design

  • grass_report() returns a contextual profile (metrics + PI + BI + regime + signed distance from the reference curve). Regime is one of balanced, prevalence-dominated, bias-dominated, mixed; each carries a short structural-implication note describing what the algebra of that regime forces on the metrics.
  • Plotting: plot.grass_result() landing plot and regime scatter, plot.grass_reference() curves, theme_grass(). Inline or legend curve labels via labels = c("auto", "inline", "legend").
  • Vignettes: grass-intro, skewed-examples.

Notes for early readers

  • In the paper, the reference values are called prevalence-conditioned thresholds (Youden-J-optimal metric values from the simulation). The package uses the name reference throughout its public API to signal that the values serve as comparison points reported alongside PI and BI, rather than as cutoffs for a binary verdict.