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.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.
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.Initial CRAN submission.
Initial CRAN release.
Added full Bayesian error propagation pipeline using
brms.
Added three-way variance decomposition and forecast shelf life metrics.