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First release.
bayesTLS fits joint Bayesian four-parameter logistic
(4PL) models to thermal-tolerance data and derives the classical thermal
death time / thermal load sensitivity quantities from the posterior, so
that every downstream quantity carries full uncertainty and is mutually
consistent within a draw.
standardize_data() maps a raw thermal-tolerance dataset
(binomial counts or continuous proportions) onto the columns the model
expects, and records the duration unit and centring used.fit_4pl() fits the joint 4PL with brms, in
either the midpoint parameterisation or the direct
CTmax/z parameterisation (ctmax = ~ ..., z = ~ ...), with
moderators allowed on any sub-parameter.make_4pl_formula() and make_4pl_priors()
expose the underlying brms formula and default priors for
inspection or customisation.tls() derives z, CTmax and
T_crit per moderator group from any fitted 4PL — including
hand-written brms models — by evaluating the sub-parameters
on a moderator x temperature grid with
brms::posterior_linpred().tls_z(), tls_ctmax() and
tls_tcrit() return the individual quantities;
derive_tdt_curve() and derive_tdt_landscape()
give the TDT curve and landscape.predict_heat_injury() accumulates heat injury over an
arbitrary temperature trace, with optional Sharpe-Schoolfield repair
(repair_rate_schoolfield()).predict_survival_curves() propagates that injury to
survival.make_temperature_scenarios() builds
fluctuating-temperature scenarios.ts_stage1(), ts_stage2(),
ts_curve() and ts_ci() implement the
conventional two-stage TDT workflow, for direct comparison against the
joint model.plot_tdt_curve(), plot_tdt_landscape(),
plot_heat_injury(), plot_survival_curves(),
plot_temperature_scenarios() and friends, all on a shared
theme_tdt().diagnose_tdt_fit() and bayes_R2_tls() for
fit checking.Four publicly available example datasets spanning lethal and
sub-lethal endpoints: aphid_tdt, dsuzukii,
snowgum_psii and zebrafish_o2.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.