Changes in version 0.1.0 (2026-07-21)                  

  - Corrected grouped contrast direction so dCTmax:A-B, dlog_z:A-B, and
    dz:A-B now follow their written meaning: group A minus group B.

freqTLS is the frequentist counterpart to the Bayesian bayesTLS package:
it fits the four-parameter logistic thermal-load-sensitivity (thermal
death-time) model by maximum likelihood via TMB, parameterised directly
in CTmax and thermal sensitivity (z). Under the matched
relative-threshold, constant-shape configuration, it targets the same
fitted curve as bayesTLS; uncertainty is reported through a frequentist
trio — Wald (delta), profile-likelihood, and bootstrap — instead of a
posterior. Forked from profileTLS (commit 6f963a9, v0.3.3), which it
supersedes.

bayesTLS-twin API

  - standardize_data() — the shared raw-data entry point for count or
    continuous-proportion responses (adopted from bayesTLS).
  - fit_4pl() + make_4pl_formula() — the direct CTmax/z formula
    interface (ctmax/z/up/low/k/by, plus threshold, t_ref, bounds,
    family), fitted by maximum likelihood through the TMB engine;
    returns a freq_tls workflow object.
  - tls() / tls_z() / tls_ctmax() / tls_tcrit() — z, CTmax, and T_crit
    with confidence intervals at the relative midpoint, the absolute
    (LT50) threshold, or any LTx.
  - extract_tdt() with get_z_* / get_ctmax_* / get_tcrit_* accessors —
    the nested z / CTmax / T_crit structure, with parametric-bootstrap
    replicates as the frequentist analogue of posterior draws.
  - predict_survival_curves() — the fitted survival surface with
    bootstrap bands.
  - diagnose_tdt_fit() and tdt_parameter_table() — convergence
    diagnostics (optimiser/Hessian/gradient) and the 4PL parameter
    table.
  - two_stage (ts_stage1() / ts_stage2() / ts_ci() / ts_curve()) — the
    classical two-stage comparator, reporting both normal and
    small-sample t intervals.
  - The plots (plot_confidence_eye(), plot_survival_curves(),
    plot_tdt_curve(), plot_heat_injury()) and extractors accept the
    freq_tls workflow object.
  - Six shared case-study datasets, including aphid_tdt (Li et al. 2023)
    and zebrafish_o2 (Saruhashi et al. 2026).

Inference and calibration

  - Small-sample Bates–Watts profile-t / Wald-t calibration: confidence
    intervals reference a t distribution with residual df = n − p,
    restoring nominal coverage at small n and reducing to the asymptotic
    interval as n grows. The evidence is a coverage + width simulation
    (repository-only data-raw/calibration-study.R, not installed): at df
    ≈ 10 the asymptotic 95% interval covers ~0.93 and the t-correction
    restores ~0.96.
  - A three-way benchmark (repository-only
    data-raw/benchmark-vs-bayes.R, not installed): freqTLS reproduces
    bayesTLS's CTmax to ~0.07 °C on the brown-shrimp data, beside the
    classical two-stage estimator.

Twin S3 surface

  - confint(), summary(), ranef(), and coef()/logLik()/vcov()/nobs(),
    the heat-injury functions (predict_heat_injury() /
    plot_heat_injury() / heat_injury_envelope()), and check_tls() all
    accept the freq_tls workflow object — this listed post-fit surface
    works on the fit_4pl() result.
  - fit_4pl(by = "g") now labels groups by the bare factor levels
    (CTmax:young_embryos), identical to the column interface, end to
    end.

Case studies and vignettes (render without Stan)

  - The frequentist-and-bayesian centerpiece carries the coverage panel
    and the three-way benchmark; comparing-to-bayesTLS carries the live
    + cached comparison.
  - Worked case studies mirroring the shared manuscript: brown shrimp;
    zebrafish under hypoxia / normoxia / hyperoxia (OCLTT); cereal
    aphids (Li 2023); D. suzukii by sex; and a cross-taxon summary.

Simulation

  - The build-excluded repository directory scripts/simulations/
    contains a freqTLS (ML/TMB) twin of the bayesTLS two-stage-bias
    simulation (shared data-generating process + scoring), with a
    comparison to the bayesTLS results. These maintainer scripts and
    their DRAC launcher are not installed with the package.

Engine

  - Asymptotes use bayesTLS's disjoint-bounds reparameterisation
    (compute_4pl_bounds); up is now a direct coordinate.
  - Convergence / positive-definite-Hessian status is surfaced at fit
    time.

Superseding profileTLS

  - freqTLS is the intended successor to profileTLS, its engine donor.