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Changelog

Source: NEWS.md

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.