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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.
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.