Changes in version 1.0.0 (2026-07-21)                  

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.

Model fitting

  - 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.

Deriving thermal load sensitivity

  - 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.

Prediction under fluctuating temperatures

  - 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.

Two-stage comparison

  - ts_stage1(), ts_stage2(), ts_curve() and ts_ci() implement the
    conventional two-stage TDT workflow, for direct comparison against
    the joint model.

Plotting and diagnostics

  - 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.

Data

Four publicly available example datasets spanning lethal and sub-lethal
endpoints: aphid_tdt, dsuzukii, snowgum_psii and zebrafish_o2.