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.
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.plot_confidence_eye(),
plot_survival_curves(), plot_tdt_curve(),
plot_heat_injury()) and extractors accept the
freq_tls workflow object.aphid_tdt (Li
et al. 2023) and zebrafish_o2 (Saruhashi et al. 2026).data-raw/calibration-study.R, not installed): at df ≈ 10
the asymptotic 95% interval covers ~0.93 and the t-correction restores
~0.96.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.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.comparing-to-bayesTLS carries the live + cached
comparison.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.compute_4pl_bounds); up is
now a direct coordinate.