Changes in version 1.1.0 (2026-05-25)                  

Breaking changes / deprecations

  - shelf_life(): the plausible_range argument is deprecated. Use
    response_scale instead. plausible_range still works but emits a
    warning. It will be removed in a future release.

New features

  - shelf_life() now includes se_t_star in the projected-mode horizon
    attribute. This is the delta-method standard error of the projected
    crossing time t* = (τ − a) / b, propagating the linear-fit
    covariance.

  - decompose_uncertainty() and the underlying .decompose_from_arrays()
    helper now support non-Gaussian families (Binomial, Poisson,
    Student-t, Negative-Binomial, Beta, Gamma) by computing all variance
    components on the response scale via the inverse link. For Gaussian
    identity the result is numerically unchanged.

  - et_predict() gains an n_env_draws argument. Setting it > 1 averages
    multiple independent perturbations per posterior draw, reducing
    Monte Carlo noise in the environmental variance estimate. The
    decomposition data frame gains a v_env_mcse column reporting the
    chi-squared standard error of env_var.

  - decompose_uncertainty() now reports a fourth temporal_var component
    when the model formula contains an autocorrelation term (ar(), ma(),
    arma(), cosy(), unstr(), sar(), or car()). The component is computed
    as pmax(0, total_var - (param_var + env_var + residual_var)) and
    captures the autocorrelation-induced predictive variance beyond the
    iid sum, so the four components reconstruct total_var modulo Monte
    Carlo error. residual_var for autocor models is interpreted as the
    innovation variance (not the stationary marginal variance).
    et_plot_decomposition() adds a Temporal segment to the stacked bars
    automatically when the column is present, and print.et_prediction()
    includes the new row in its summary.

Bug fixes

  - et_plot_calibration() previously only recognised a column literally
    named group as the sub-group identifier. Calibration data frames
    built by binding per-group results with descriptive column names
    (e.g.\ cluster_id, species, regime) were silently collapsed into a
    single un-grouped series, producing plots with multiple overlapping
    points per nominal level and a zig-zagging connecting line. The
    function now auto-detects any single non-canonical column (anything
    other than ci_level, nominal, observed_coverage, n_obs,
    calibration_error, sharpness) with more than one unique value and
    uses it as the grouping variable. A new group_col argument allows
    the grouping column to be set explicitly, or group_col = NA to force
    a single un-grouped series.

                        Changes in version 1.0.0                        

  - Initial CRAN submission.

  - Initial CRAN release.

  - Added full Bayesian error propagation pipeline using brms.

  - Added three-way variance decomposition and forecast shelf life
    metrics.