Corrects the environmentally inflated predictive draws, which were
built by recycling the predictor perturbation across posterior draws,
and redefines total_var as the measured posterior
predictive variance rather than the sum of its parts. Any
reported variance budget, credible interval, PIT, calibration or shelf
life from an autocorrelation model should be recomputed; iid
models are affected only at the level of Monte Carlo error.
The predictor perturbation was recycled across posterior
draws, inflating the predictive variance of autocorrelation
models. et_predict() perturbed only the first
n_perturb draws (default 500) and
.inflate_env_predictive() then recycled that block across
all n_draws (default 2000) rows,
idx <- rep_len(seq_len(n_perturb), n_draws)
mu_perturbed[idx, ] + (pp - mu_draws)so for three quarters of the rows the env-perturbed mean of draw
s mod n_perturb was paired with the posterior-predictive
residual of draw s. That destroys
Cov_s(mu^(s), y^pp,(s) - mu^(s)). The covariance is
approximately zero for an iid fit, but substantially negative
for an ar() fit, where a draw with large |phi|
carries a wide accumulated residual, so the predictive variance was
biased upward. On an annual rodent series (n_perturb = 500,
n_draws = 2000) the gap V(predictive) - V(pp),
which should equal env_var, came out at 20x, 22x and 196x
env_var for the three most persistent species, against
0.75-1.33x under matched pairing. Because these draws are the single
source of truth for the credible interval, PIT, calibration and
shelf life, the inflation reached all four: 90% intervals were 1-12% too
wide and shelf lives correspondingly one to two periods early.
n_perturb now defaults to n_draws, so every
draw carries its own perturbation and nothing is recycled. When
n_perturb is set below n_draws the prediction
and the whole variance budget are restricted to those draws instead,
with a message; .inflate_env_predictive() errors on
mismatched draw counts rather than recycling.
This changes a returned dimension. With
env_noise (or env_ensemble) supplied and
n_perturb < n_draws, posterior_predict,
posterior_linpred, predictive_draws and
lp_perturbed all now have n_perturb rows, and
the returned n_draws records that effective count
(n_perturb is recorded too). Every array in the object
therefore describes one draw set, which is what makes
total_var, the interval, PIT, calibration and shelf life
mutually consistent. Code that relied on
nrow(posterior_predict) == n_draws while sub-sampling the
perturbation should either drop n_perturb (the default now
uses every draw) or read the count off the object.
binomial() with
trials(n > 1) decomposed on the probability scale while
the predictive draws were counts.
posterior_linpred(transform = TRUE) returns the probability
p, but posterior_predict() returns a count in
0..n, and the decomposition used the former throughout.
With n = 10: residual_var came back as
p(1-p) instead of n p(1-p), ten times too
small; param_var as Var(p) instead of
Var(n p), a hundred times too small; the reported
environmental share was 0.02-0.50% where the correct values are 2-34%;
and the env-inflated draws added a probability to a count residual,
landing outside the support (measured range -0.180 to 10.222). The
predictive variance was effectively flat in the perturbation
SD (2.04 at every level, against a correct 2.08 to 3.03), so
driver uncertainty was barely propagated at all.
Response-scale means are now scaled to n p, putting the
whole budget on the same scale as the predictive draws, and the binomial
branch of the residual variance carries the trials factor. The trials
are read off the trials() term in the formula and evaluated
against newdata.
Under 1.3.1 none of this was visible: total_var was
defined as the channel sum, self-consistent on the probability
scale, while credible_intervals came from the predictive
draws on the count scale, so the reported budget and the reported
interval disagreed by a factor of ten with nothing to flag it. The
measured total of this release reports it as a closure_gap
of ~90% of total_var. Only binomial
with trials > 1 was affected; bernoulli has
n = 1, and for every other supported family
E[y] = g^-1(eta) with no trials factor.
It survived because the only binomial test in the suite used
brms::bernoulli() with size = 1, where
p == n p.
The env-inclusive predictive draws were always a location
shift, which is wrong for any non-Gaussian family.
mu_env + (y_pp - mu) keeps the dispersion of the
distribution at mu while moving its location to
mu_env. For a Gaussian identity link that is exactly a draw
from N(mu_env, sigma^2); otherwise it is not. Measured on a
Poisson/log fit (beta = 0.8), the predictive variance was understated by
0.6%, 2.4% and 4.8% at perturbation SDs of 0.1, 0.25 and 0.5, because
E[mu_env] = mu exp(b^2 d^2 / 2) under a log link; and
100% of the draws for a count model were non-integer, with 6-8%
negative, which PIT and calibration were then computed
from.
et_predict() now redraws from the response distribution
at the perturbed mean (.family_rng()) for gaussian,
student, poisson, negbinomial, bernoulli, binomial, gamma and beta, the
same set the residual channel supports. The residual channel is
evaluated at the mean that generated the draws, so the Jensen term is
attributed rather than left in closure_gap; for a
non-identity link this means residual_var depends on driver
uncertainty through the variance function, which is a property of the
model rather than an artefact. After the fix the measured total agrees
with Var(mu_env) + E[V(mu_env)] to within 1.7% (Poisson)
and 0.6% (binomial), and no draw falls outside the support.
Two cases keep the location shift. Gaussian
identity, where it is exact and keeps each residual paired with
its own parameter draw; the Portal replay is bit-for-bit unchanged. And
any family with a residual autocorrelation term,
because brms generates the posterior predictive by
iterating the fitted process forward and (y_pp - mu) is
what carries that accumulation, so redrawing would collapse the forecast
to a single innovation. A non-Gaussian autocorrelation model therefore
reports a first-order approximation and says so. A family with no
sampler defined falls back to the shift with a warning.
total_var is now measured, not
defined. It is the sampled variance of the environmentally
inclusive posterior predictive draws, i.e. the distribution the reported
credible interval is drawn from. Previously it was defined as
param_var + env_var + residual_var (+ temporal_var), with
the posterior predictive variance taken on the
unperturbed predictors, so the shares reconciled to
100% by construction and env_var was an add-on to a
predictive variance that did not contain it. The component sum is now
recovered as a result instead of imposed.
temporal_var is signed. It is the
remainder total_var - (param_var + env_var + residual_var)
and is no longer clamped at zero. A negative value is meaningful:
autocorrelated errors can reduce predictive variance relative to the
independent-innovation baseline. Being a remainder it also absorbs Monte
Carlo error from the other channels, so read a temporal_var
of the same order as v_env_mcse as noise.
New closure_gap column for iid
models, reporting
total_var - (param_var + env_var + residual_var). For a
correctly specified iid model it is Monte Carlo error, of the order of
v_env_mcse; a systematically large value means a channel is
mis-estimated.
include_env_in_ci now governs only which draws are
reported as the interval. The env-inclusive draws are always
built when there is environmental uncertainty, because
total_var is their variance.
The budget-closure tests no longer assert an identity. Checking
total_var == param_var + env_var + residual_var to
1e-8 was tautological while total_var was
defined as that sum; the suite now bounds the closure gap by its Monte
Carlo scale, 3 * sqrt(2 / n_eff) on the median relative gap
(expect_budget_closes()), and asserts the exact identity
only where temporal_var makes it exact by
construction.
shelf_life() now rejects a time_col
that is a column rather than a column name. Passing
time_col = df$year, or time = df$year (which
partial-matches to time_col), used to fail deep in the call
stack with
'length = 16' in coercion to 'logical(1)'.
et_plot_decomposition(proportional = TRUE)
normalises against total_var rather than against the
plotted component sum, so the iid closure gap is visible instead of
being rescaled away, and a negative component is drawn below the zero
line rather than silently clamped. The plot now also includes
group_var when present.
Corrects shelf_life(), which could report a forecast
horizon that rested on a single noisy period. Any reported shelf
life should be re-checked; series whose CI-width /
response-scale ratio degrades monotonically are unaffected.
shelf_life() reported the first threshold
exceedance as the horizon. When the ratio hovers near
threshold without trending, one period can exceed it by
chance, and that isolated excursion was promoted to a “shelf life”.
Observed on a real series whose ratio sat at ~0.92 across a 16-period
window with a single excursion to 1.038: the reported horizon was not
reproducible across sampler settings, varying between “no crossing”, a
projection far outside the window, and two different crossing periods,
from MCMC noise alone.
shelf_life() now requires the exceedance to
persist. The new min_run argument (default
2) sets how many consecutive uninformative periods
constitute a crossing; min_run = 1 restores the previous
behaviour. A crossing already in force at the first lead is still
detected, and a dip inside an otherwise degrading series no longer
resets the horizon.
The projection mode extrapolated slopes indistinguishable
from flat. min_slope_for_projection is a magnitude
gate only, so a slope of ~1e-3 with a one-sided p-value of 0.42 cleared
it and was extrapolated to a confident-looking crossing time. On the
same real series above this produced a projected horizon 45 years out
whose delta-method 95% interval spanned 328 years and included
the past. The projection now additionally requires the slope to
be significantly positive, controlled by the new
projection_alpha argument (default 0.05); when
it is not, the result is reported as a lower bound — the honest outcome
for a forecast whose precision does not degrade in-window. Set
projection_alpha = 1 to restore the previous behaviour.
The two fixes compose: a series that neither sustains a crossing nor
trends detectably now returns lower_bound instead of either
a spurious observed horizon or a spurious projection.
et_skill_score() had the same first-crossing
defect in its forecast limit. The null-relative forecast limit
was the first lead time with skill <= 0, with no
persistence requirement. Skill hovering near zero crosses by chance, so
one unlucky lead was promoted to “the horizon” — found on a real
comparison whose mean skill over the window was comfortably positive.
et_skill_score() now takes min_run (default
2, matching shelf_life()) and the returned
"forecast_limit" attribute carries n_below,
first_below and min_run, so isolated dips are
visible rather than load-bearing. min_run = 1 restores the
previous behaviour.
Group-level variance channel for hierarchical
models. The decomposition now handles
(1 | g)-style fits correctly, and distinguishes the two
budgets that a hierarchical model implies:
param_var, and there is no separate channel. Conditioning
on a known group reduces predictive variance relative to the
population level, so the naive Var(full) - Var(population)
is negative and would be meaningless as a “group variance” (measured on
a 6-group fixture: 0.022 vs 0.244).group_var column
appears, carrying the between-group variance. Verified to recover the
fitted tau^2 on a known-truth fixture.et_predict() detects new levels automatically and
reports which budget it is computing.
param_var + group_var + env_var + residual_var (+ temporal_var)
still equals total_var exactly.
et_shelf_life_pool() — pool shelf lives
across forecast origins. A horizon from a single forecast
origin is a property of that training window as much as of the system.
This pools horizons from repeated origins and returns a median with an
interval. Origins where the forecast was still informative when the data
ran out are right-censored, not missing: discarding
them biases the pooled horizon downward, because the censored origins
are exactly the ones with long horizons. Estimation is Kaplan–Meier via
survival (a Recommended package, in Suggests),
with a documented, deliberately interval-free fallback when it is
unavailable. All origins censored is reported as a result — the forecast
never became uninformative — not as an estimation failure.
et_priors_split() — remove the
prior/likelihood double use entirely.
shrinkage = "zero" already stopped reusing point estimates
as prior means, but the prior scale, and any variable
selection performed by the first-stage model, still came from
the rows that form the likelihood. This fits the first stage on a random
(optionally stratified) subset and returns the disjoint complement for
the Bayesian fit, so selection and likelihood share no observations and
no post-selection caveat applies. The cost — a smaller likelihood sample
— is documented rather than hidden. The user-supplied seed is restored
afterwards so it cannot leak into the caller’s RNG stream.
Exceedance diagnostics on every horizon. The
horizon attribute now carries n_exceedances,
frac_exceedance, first_exceedance and
min_run alongside value and type,
so an isolated excursion is visible rather than silent. Comparing
first_exceedance with value shows directly
whether a horizon rests on a persistent crossing. Trend-mode horizons
additionally carry slope and slope_p.
Corrects the variance decomposition for models carrying a residual
autocorrelation term. Any analysis using ar() /
ma() / arma() / cosy() /
unstr() / sar() / car() must be
re-run; iid models are unaffected (all three changes below are
no-ops without an autocorrelation term). Note that 1.2.1 ships the
defect.
env_var was silently destroyed for any model
with an ar() / ma() / arma()
term. Under brms’s default ar(cov = FALSE)
parameterisation the autocorrelation term enters the
mean (mu[n] += ar * err[n-1]), not only
the covariance. decompose_uncertainty() computed
env_var = Var(mu_perturbed) - Var(mu_draws) where
mu_perturbed came from an internal hand-rolled linear
predictor (no AR term) while mu_draws came from
posterior_linpred() (AR term included). The difference was
therefore V_env - V_AR, which turns negative as the AR
error grows and was then clamped to zero by pmax(., 0).
In a controlled test (AR(1), phi = 0.9, analytic
V_env = beta^2 * delta^2 = 0.152) the reported
env_var averaged 0.012 and was floored to exactly
zero at 13 of 15 lead times; the iid control recovered 0.173
correctly. Both posterior_linpred() calls now pass
incl_autocor = FALSE, which returns the pure regression
linear predictor — verified bit-identical to the internal predictor, so
the AR term cancels exactly in the subtraction. env_var now
averages 0.145 with no floored rows.
param_var no longer absorbs the
autocorrelation error, and temporal_var no longer collapses
to zero. The same contamination meant param_var
grew with lead time even on non-trending new data (0.154 -> 0.976
over 15 steps, an 8.9x overstatement of parameter uncertainty at the far
lead), which in turn made
temporal_var = pmax(0, pp_var - (param_var + residual_var))
evaluate to zero at every lead for a series with phi =
0.82. After the fix param_var is flat (0.154 ->
0.147) and temporal_var grows monotonically 0 -> 0.637,
correctly reporting zero at lead 1 where a single innovation adds
nothing beyond residual_var.
This changes reported numbers for every autocorrelation
model. Analyses using
ar()/ma()/arma()/cosy()/unstr()/sar()/car()
must be re-run.
The env_var zero-floor is now
instrumented. decompose_uncertainty() warns when
the floor fires on rows whose shortfall exceeds twice its own Monte
Carlo SE — too systematic to be jitter. The silent floor is what hid the
bug above through two review rounds. The test is statistical rather than
a raw count, so small prediction sets (where a floored row genuinely is
jitter) do not trip it.
et_fit() gains ar_prior,
defaulting to "weakly_informative".
brms places a flat, unbounded prior on
class = "ar" / "ma" (get_prior()
reports (flat) with no lb/ub), so
nothing keeps the residual process inside the stationary region. On the
short series typical of ecological panels the posterior routinely
straddles the unit root, and the k-step forecast variance
sigma^2 (1 - phi^(2k)) / (1 - phi^2) then diverges. The new
default normal(0, 0.5) places ~95% of the prior mass inside
(-1, 1) without hard-bounding, so a genuinely
non-stationary series can still say so.
ar_prior = "stationary" truncates to (-1, 1);
ar_prior = "flat" restores the old behaviour. A prior the
caller supplies for these classes is never overridden.
On a near-unit-root fixture (n = 23, true random-walk residual) this reduces the ratio of raw to 1%-winsorized predictive variance from 1072x to 1.7x — i.e. the heavy tail that motivated the winsorization below was largely an artefact of the unbounded prior, not a property of the data.
et_fit() warns when more than 5% of an
autocorrelation posterior sits at |value| >= 1,
reporting the exact fraction and posterior mean. For AR(1) this is
precisely the posterior probability of a non-stationary process. The
message directs the user to read long-lead intervals,
temporal_var and shelf_life() as “no usable
horizon” rather than as calibrated numbers.
et_predict() gains var_trim
(default 0), replacing the always-on, undocumented 1%
winsorization. Two problems are fixed:
param_var and the environmental pair
stayed raw. temporal_var was therefore a difference between
two different estimators, biased low by whatever the winsorization
removed (~3.6% for a Gaussian at trim = 0.01) — which
blinded it to genuine autocorrelation shares below that threshold.
var_trim is now applied uniformly to every
channel.var_trim = 0 default they
describe the same one.The tail diagnostic is still computed (against a fixed 1% reference,
regardless of var_trim, since it is a statement about the
model rather than the estimator) and now recommends
ar_prior = "stationary" instead of implying that a robust
summary makes a diverging forecast trustworthy.
This changes reported variance components wherever the old winsorization was biting — i.e. any near-unit-root autocorrelation fit.
Regression tests added in
tests/testthat/test-decompose-autocor.R, covering
env_var recovery under ar(), floor-rate, no
decay with lead time, positive temporal_var, flat
param_var, and an iid control. Reproduction scripts in
tests/manual/. The previous suite missed all of this
because its mock builds mu_perturbed as
lp + noise, i.e. it assumed the two arrays were already on
the same footing.
v_env_mcse on aarch64
(macOS arm64). This is the failure that caused the CRAN
archival on 2026-06-08. decompose_uncertainty() detected
the “no environmental noise” case by testing the variance difference
v_env_raw == 0 exactly, and it computed the two column
variances with different stats::var() na.rm
settings. Those settings select different C code paths in
cov.c, which round identically on x86_64 but not on
aarch64. A last-bit difference therefore flipped the reported Monte
Carlo SE of env_var from 0 to a large value
(0.20 in the CRAN log) when env_noise = 0. The two
variances are now computed the same way, and the zero test uses a
relative tolerance instead of exact equality, so the result no longer
depends on bit-level floating-point reproducibility.
env_var itself was already correct to within 1e-10; only
its Monte Carlo SE was affected. Regression tests added.This release addresses the correctness issues raised in peer review, starting with the uncertainty-budget consistency of the decomposition.
Total variance now contains environmental uncertainty
(T1). decompose_uncertainty() previously computed
total_var from posterior_predict() on the
unperturbed predictors, so env_var was an add-on
that sat outside the total — the reported components could sum
to well over 100% of total_var. total_var is
now defined as the sum of its components (law of total
variance): param_var + env_var + residual_var for iid
models, plus temporal_var for autocorrelation models. The
percentage shares now sum to 100% exactly, and env_var is a
genuine sub-share of a total that contains it.
et_predict() folds environmental uncertainty
into the credible interval by default when env_noise is
supplied. The include_env_in_ci default changed
from FALSE to NULL (auto): env is now included
whenever the caller passes non-zero env_noise, so the
reported forecast interval — and therefore shelf_life() and
et_calibrate() coverage — reflect predictor/driver
uncertainty and stay coherent with total_var. Pass
include_env_in_ci = FALSE to recover the previous
parameter+residual interval. The env-inclusive predictive is now
constructed family-generally (re-centring each posterior-predictive
residual on the perturbed mean), rather than the previous Gaussian-only
lp + sigma * N(0,1).
et_sensitivity_profile() /
et_plot_sensitivity(): env_share is now
env_var / total_var (previously
env_var / (env_var + total_var)), which double-counted env
once env entered the total.
extract_priors() gains a
shrinkage argument, defaulting to "zero"
(T5). This addresses the prior/likelihood double-use objection
at its source. The previous behaviour — using the fitted coefficients as
prior means and then refitting the Bayesian model on the same
data — reused the data twice and could make posteriors overconfident.
The new default centres every coefficient prior at 0 (a regularizing,
ridge-style prior) with a width scaled by the coefficient
magnitude multiplier * |coef| (floored at
min_sd), so a well-estimated large coefficient is not
shrunk toward 0 while its estimated location is no longer
reused. Set shrinkage = "estimate" to recover the previous
informative-mean behaviour (with a documented double-use caveat). For
ranger the prior mean is always 0, so
shrinkage has no effect there. The
et_prior_spec object and its print() method
now report the shrinkage mode.
et_fit() now documents its boundary plainly (a thin
brms wrapper; the decomposition of hierarchical fits is
provisional pending a group-variance term, and grouping=
fits independent per-group models rather than one multilevel model), and
emits a warning on high Rhat / divergent transitions pointing to
et_diagnose() before prediction.
extract_priors.ranger() is documented and flagged
(advisory) as : random-forest importance has no sign or GLM-coefficient
scale, so its importance→prior-SD mapping is heuristic — prefer
glmnet/lm/glm for a principled
prior.AR/MA forecast variance now accumulates with lead time
(T3). For autocorrelation models
(ar()/ma()/arma()/cosy()/unstr()/sar()/
car()), et_predict() previously called
posterior_predict() on the forecast rows alone, which does
not propagate the residual autocorrelation across the
horizon — the predictive variance (and hence the credible interval,
temporal_var, and shelf life) stayed flat with lead time.
et_predict() now continues the training series with the
forecast rows (response blanked to NA) so brms iterates the
fitted process forward: the forecast variance grows with lead time,
integrates over the posterior of the AR coefficient (draws with |φ| ≥ 1
contribute super-linear growth, correctly handling the non-stationary /
random-walk regime), and carries the initial-condition uncertainty from
the forecast origin. The forecast rows’ responses are always treated as
unknown, so passing a hindcast’s observed future values no longer leaks
them into the interval.
The AR/temporal decomposition is now tail-robust and
consistent with the reported interval. Two follow-on fixes to
the autocorrelation forecast above. (i) et_predict() built
the credible-interval / PIT / calibration draws
(predictive_draws) from only the first
n_perturb posterior draws while the variance decomposition
used all n_draws, so the interval and the decomposition
could describe different samples; the env-inflated predictive now spans
all n_draws draws (the env perturbation is recycled across
them).
(ii) temporal_var (and hence total_var) is
computed from a winsorized posterior-predictive
variance, so that when the AR coefficient’s posterior has mass at a
handful of explosive, non-stationary draws can no longer dominate the
estimate. Previously a single near-unit-root draw could inflate the raw
variance by an order of magnitude and make temporal_var
disagree wildly with the (quantile-based, tail-robust) credible interval
reported alongside it. The decomposition now tracks that interval; a
genuinely non-stationary AR posterior still yields a large temporal
share. et_predict() emits a heads-up when the raw
predictive variance greatly exceeds its robust value (the near-unit-root
regime), pointing to et_diagnose().
et_predict() caps n_draws at the number
of draws in the fit (with a warning) instead of erroring, and now uses a
shared, explicit set of draw ids across posterior_predict()
and posterior_linpred() so the two are aligned
draw-for-draw.
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.Optional global (Sobol) variance decomposition
(T7). New et_sobol() provides an order-independent
alternative to the additive budget: a variance-based decomposition of
the predictive mean into first-order and Sobol indices plus their
(Saltelli 2010 / Jansen estimators, with output centring for Monte Carlo
stability). The fast additive budget remains the default;
et_sobol() is for the general case where a non-linear link
or correlated drivers make the parameterdriver interaction
non-negligible, which the sequential additive split folds into its
terms.
Null-model forecast skill + shelf-life skill gate
(T6). New et_skill_score() scores a forecast
against a null model — a random walk (persistence) or climatology — with
the continuous ranked probability score (CRPS) and reports the
per-lead-time skill and the null-relative (the lead time at which the
model stops beating the null), following the ecological-forecasting
convention (Petchey et al. 2015; Wesselkamp et al. 2025; the NEON
challenge). shelf_life() gains a skill
argument: passing an et_skill_score() table gates the shelf
life so a period counts as informative only if it is both precise
skillful, preventing a precise-but-biased forecast from passing.
shelf_life() is now documented as a complement to the
accuracy-relative forecast limit, and its response_scale
now defaults to the training-response range (a documented, reproducible
default) instead of requiring an arbitrary number.
PIT / rank-histogram calibration diagnostics
(T4). New et_pit() computes the probability
integral transform of held-out observations under the reported posterior
predictive distribution (the same distribution the intervals and
et_calibrate() coverage use, environmental uncertainty
included), and et_plot_pit() renders the PIT histogram with
a uniform reference band. This is the full-distribution diagnostic — a
U-shape flags overconfident intervals, a hump flags under-confidence, a
tilt flags bias — that coverage alone cannot provide.
et_pit() supports randomized PIT for discrete predictions.
et_prediction objects now store
predictive_draws, the exact draws the interval / PIT /
calibration all share.
Pathwise driver ensembles for genuine reforecasts
(T2). et_predict() gains an
env_ensemble argument: a named list of matrices (M scenario
trajectories over the H forecast steps), one per uncertain driver
predictor. Each posterior draw is paired with a whole covariate
trajectory, so driver uncertainty is temporally coherent and
accumulates with lead time as a real driver ensemble
(CMIP members, resampled climatology) fans out — unlike the existing
env_noise, whose independent per-observation jitter
averages out and leaves the driver variance flat. This is the
recommended way to build a reforecast that uses only information
available at the issue date, and it reproduces the growing
driver-variance fraction of Dietze (2017) and Thomas et al. (2020).
env_ensemble supersedes env_noise for the
perturbation and is folded into the credible interval by
default.
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.