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tmb-likelihood-review
Review the freqTLS TMB 4PL likelihood and parameterisation before merging.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Review the freqTLS TMB 4PL likelihood and parameterisation before merging.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | tmb-likelihood-review |
| description | Review the freqTLS TMB 4PL likelihood and parameterisation before merging. |
Use this skill for any change to src/profile_tls.cpp, the numeric header, the
density code, or the parameter transforms. Gauss leads; Noether checks the
math-to-code match.
k, phi, z on log; low on logit; the asymptote gap on the nested-gap
transform up = low + (1 - low) * invlogit(beta_gap) so up > low is
guaranteed?mid = log10(tref) - (temp - CTmax) / z, and the
fitted survival p = low + (up - low) * invlogit(-k * (log10(duration) - mid))
(descending: high survival at short durations)?CppAD::CondExpLt / CondExpGt to
[eps, 1 - eps]), not an if on a Type?y and n DATA_VECTOR (Type), not IVECTOR, so the beta-binomial
lgamma(y + a) works with a a Type? Is there a shape floor on a, b?phi convention the sum of the Beta shapes
(a = p * phi, b = (1 - p) * phi), matching simulate_tls() (R-PHI)?sdreport() report
interpretable transformed parameters (low, up, k, phi, beta_CT,
z_group)?phi toward the binomial limit, threshold never crossed)?z = -1 / beta1, CTmax = Tbar + (log10(tref) - beta0) / beta1)?Any pattern adapted from drmTMB (the Boolean.h pre-include guard, the
beta-binomial lgamma form, the CondExp clamp, the stable log1p_exp switch)
must be recorded in inst/COPYRIGHTS before the change is treated as complete.
Audit the freqTLS-versus-bayesTLS benchmark for a fair configuration, sound cache provenance, and the correct R-SHRIMP data rebuild.
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.