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Audit the freqTLS-versus-bayesTLS benchmark for a fair configuration, sound cache provenance, and the correct R-SHRIMP data rebuild.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Audit and improve freqTLS figures, galleries, pkgdown articles, and ggplot recipes, enforcing the Confidence-Eye uncertainty contract, before Florence, Rose, Pat, Fisher, and Grace call a figure done.
Review freqTLS before a public, CRAN, GitHub, or internal release.
Add simulation-based parameter-recovery tests for freqTLS models.
Audit a completed freqTLS task or phase before closing it, checking implementation, equations, examples, tests, docs, pkgdown, roadmap, NEWS, the capability matrix, known limitations, stale wording, and after-task reporting.
Review freqTLS profile-likelihood confidence intervals for correctness, equivariance, chi-square calibration, and honest open/boundary/multimodal handling.
Review and improve freqTLS prose in README files, vignettes, pkgdown articles, after-task reports, release notes, design docs, and manuscript-style text for clarity, concrete claims, stable terminology, citations, and reader fit.
| name | benchmark-vs-bayesTLS-audit |
| description | Audit the freqTLS-versus-bayesTLS benchmark for a fair configuration, sound cache provenance, and the correct R-SHRIMP data rebuild. |
Use this skill for any change to data-raw/build_benchmark_cache.R, the
vendored datasets, R/data.R, or the
comparing-to-bayesTLS vignette. Jason maps the comparator; Rose audits the
claims and provenance.
The two interval-bearing model fits must use the relative midpoint threshold and the constant-shape configuration so their comparison is fair:
bayesTLS::ts_stage1 -> ts_stage2 -> ts_ci (absolute
LT50 by construction; label it as an approximate comparator and use it only
where fitted lethal asymptotes near zero and one make the thresholds close);bayesTLS::fit_4pl(temp_effects = "mid") -> extract_tdt(target_surv = "relative");fit_tls() -> confint(method = "profile").Confirm the model temperature effect runs through the midpoint only (shared
low, up, k), the time unit and tref match across all three (pin
t_ref/time_multiplier when calling bayesTLS; R-UNITS), and the comparison
reports point estimates plus CI width and asymmetry. Never describe the
classical estimate as using the relative threshold. Zebrafish is a 3x3 per life
stage. Include the fairness footnote.
The bayesTLS shipped shrimp_lethal death counts are corrupted upstream: the
CSV column Mortality_after_trial is a proportion (e.g. 0.0909 = 1/11,
0.5 = 5/10), and the upstream make_datasets.R mislabels it a count and
applies as.integer(...), flooring proportions below 1 to 0. freqTLS must
rebuild the counts from the CSV:
deaths = round(prop * N), hence survived = N - round(prop * N);R/data.R and a friendly upstream report;.rda before finalising.Zebrafish counts ship correctly (n_surv, n_total); rename to native columns
only.
Stan will not run in CI, so the benchmark reads a maintainer-built cache at
inst/extdata/bayesTLS_benchmark_cache.rds. Confirm the cache meta records
bayesTLS_version, a verified 40-character git_sha, source_url,
cmdstan_version, date_built, seed, the
configuration, and the R-SHRIMP note. The vignette shows the live calls with
eval = FALSE, reads the cache with eval = TRUE, runs freqTLS live, and
prints the provenance. A test-benchmark-sanity tripwire checks the cached
numbers against a live freqTLS fit within a loose tolerance and a one-command
regeneration path exists.
Licensing is component-specific. Confirm each installed file appears in the data
licence ledger with source-specific terms, attribution in R/data.R (@source),
inst/CITATION (freqTLS + bayesTLS bibentry), and the README where relevant,
with code under GPL-3 and provenance in inst/COPYRIGHTS. Permission-pending
snow-gum and Kristineberg material must remain build-excluded.