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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.