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DESCRIPTION: added method references to the
Description field - Wolfson et al. (2015) doi:10.1002/sim.6526 for the
censored naive Bayes model, and Gerds and Schumacher (2006) doi:10.1002/bimj.200610301 for the IPCW Brier score used
in evaluation.man/nbsurv-class.Rd: added a \value
section documenting the return values of the print() and
plot() methods for "nbsurv" objects.time_smooth)time_smooth = TRUE / bandwidth
arguments to nbsurv()/ cv_nbsurv() (default
FALSE, fully backward-compatible). The real remaining
inefficiency identified after cov_structure = "full"
(0.4.0): every horizon was fit completely independently,
discarding the fact that class-conditional statistics should vary
smoothly with time - costly right where one of the two horizon-defined
classes (already failed vs. known to survive) is smallest, typically
near the earliest or latest requested horizon.
time_smooth = TRUE precomputes class- conditional
statistics once at every point of time_grid at fit time,
then combines them at predict() time via Nadaraya-Watson
kernel regression across horizons (Gaussian kernel, width
bandwidth) - borrowing strength from neighboring horizons
while still allowing genuinely time-varying effects, unlike a
proportional-hazards model that shares one coefficient across all of
time. Because the combination is a convex (weight-normalized) average of
already-valid per-grid-point statistics, smoothed covariance matrices
stay positive semi-definite and smoothed categorical probabilities stay
valid automatically - no PSD repair or renormalization needed.
bandwidth is now a tunable column in
tune_nbsurv()’s param_grid.NA for every smoothed
prediction. 0 * NA is still NA in R, so
zero-weighting an invalid (all-NA) grid point before
summing did not neutralize it; colSums() propagated the
NA. Fixed by zeroing the invalid rows’ values, not
just their weights, before summing. Regression test locks this in
(tests/testthat/test-time-smooth.R).coxph on
lung (20 held-out 70/30 splits,
age + sex + ph.ecog): a genuine, principled bias-variance
tradeoff, confirmed rather than assumed. Concordance improves as
bandwidth widens (0.60 at the default heuristic up to
~0.615-0.619 as bandwidth grows toward pooling most of the time range),
closing roughly half to two-thirds of the gap to coxph’s
0.629 - but IPCW Brier score at the most extreme horizon tested (t=400)
worsens monotonically as bandwidth widens (0.240 to 0.254), the
expected cost of over-smoothing away genuine horizon-specific
calibration. No single bandwidth wins on both metrics at every horizon;
the default heuristic (one quarter of the fitted time range) is a
reasonable balance, not a universally optimal choice - use
tune_nbsurv()’s bandwidth grid column to tune
it for a specific dataset, exactly as any other
hyperparameter.lung benchmark
gap to coxph narrows but does not fully close - reported
honestly in the README rather than oversold.
age/ph.ecog’s covariate effects are close to
log-linear and non-interacting on this dataset, which is exactly what a
correctly-specified Cox model is built to exploit; nbsurv’s
value proposition is flexibility (genuinely time-varying,
non-proportional effects a fixed-coefficient Cox model cannot
represent), not raw accuracy dominance on every dataset.time_smooth = TRUE fit time is ~2.5s
vs ~0.03s without it - a real but tolerable one-time cost at realistic
survival-analysis sample sizes, with predict() itself remaining fast
(~17ms for 500 predictions) since it only does cheap kernel-weighted
averaging over the precomputed grid. The existing time_grid
argument already lets users pass a coarser custom grid to cut this cost
further if needed. Rcpp would be unjustified complexity for a bottleneck
that doesn’t clearly exist at the scale this package targets.cov_structure = c("diagonal", "full") argument to
nbsurv()/ cv_nbsurv() (default
"diagonal", fully backward-compatible). With
"full", continuous predictors are modeled jointly as a
single multivariate Gaussian per class (survivor/event), relaxing naive
Bayes’s conditional-independence assumption between them, instead of the
product of independent univariate Gaussians. The covariance matrix is
shrunk toward its diagonal by the new shrinkage argument
(default 0.2) for numerical stability at small sample
sizes. Categorical predictors are unaffected and remain conditionally
independent either way. With fewer than 2 continuous predictors,
"full" is numerically identical to "diagonal"
(verified by regression test).tests/testthat/test-cov-structure.R): on a synthetic case
with two continuous predictors correlated at r=0.85,
cov_structure = "full" gives a consistent ~5-6% relative
improvement in held-out IPCW Brier score over "diagonal" at
every horizon tested. Its effect on concordance is small and
inconsistent even under strong correlation - concordance
depends only on the ranking of risk scores, which a monotonic
transform of the naive product often preserves even when the underlying
probabilities are miscalibrated; Brier score, which scores the actual
probability values, is the metric this correction reliably improves. On
the real lung dataset
(age/ph.ecog correlated at r=0.31, n=167), the
improvement was smaller and mixed (Brier improved at 2 of 3 horizons
tested, concordance improved at 2 of 3) - "full" is a
genuine, validated improvement when continuous predictors are
correlated, not a guaranteed win on every dataset, and did not close the
gap to coxph on lung specifically (see README
benchmark).evaluate_nbsurv()/cv_nbsurv()/tune_nbsurv()
reported the complement of concordance
(1 - true concordance), not concordance itself.
concordance_at_horizon() calls
survival::concordance(Surv(time, status) ~ risk), whose
formula interface defaults to the opposite direction convention
from a risk score (it expects higher risk to mean
longer survival unless told otherwise). Since every internal
caller passes a genuine risk-direction score (higher = more likely to
fail), the returned value was systematically inverted - a model with a
true C-index of 0.63 was reported as 0.37, making a genuinely
discriminating model look worse than random. Fixed by adding
reverse = TRUE to the survival::concordance()
call. Verified against survival::concordance() on both a
coxph linear predictor and an nbsurv fit
(matches to full precision either way), and a new regression test on a
clearly-separable synthetic case that would fail loudly if the direction
ever flips back. Brier scores, predictions, and every other metric were
unaffected - this only touched the concordance calculation.plot.nbsurv() silently double-scaled continuous
predictors before plotting, distorting every survival curve it
drew. nbsurv() only ever stored the
scaled training data on the fit object;
plot.nbsurv() passed that already-scaled data into
predict(), whose prepare_newdata() step scales
its input again using the same stored means/sds. The result was visually
“chunky” curves with implausible flat segments and near-zero collapses
for some rows, especially past t=400 in the README example - not real
model behavior, an artifact of predicting from twice-standardized
covariates. Fixed by storing the raw (unscaled) training data separately
(fit$training_data_raw) and having
plot.nbsurv() use that instead.
predict()/evaluate_nbsurv()/
cv_nbsurv()/tune_nbsurv()/varimp_nbsurv()
were never affected - they operate on user-supplied
newdata, which is only ever scaled once. New regression
test locks plot.nbsurv()’s curves to match
predict() called directly on the same raw rows.varimp_nbsurv(): permutation-based variable
importance. For each predictor, its values are independently shuffled
(n_repeats times), evaluate_nbsurv() is
recomputed on the permuted data, and importance is the resulting
degradation in the chosen metric ("brier" or
"concordance"), averaged over the requested horizons and
repeats. Returns a data.frame (class
varimp_nbsurv) sorted by decreasing importance, with its
own print() method.plot() methods:
plot.varimp_nbsurv() (horizontal bar chart of importance),
plot.cv_nbsurv() (mean cross-validation metric
vs. horizon), and plot.tune_nbsurv() (mean tuning metric
per candidate, best candidate highlighted).evaluate_nbsurv() now computes the integrated
Brier score (IBS) via the trapezoidal rule in addition to
horizon-specific Brier scores and concordance.calibration_plot_nbsurv() function for visual
calibration assessment at a single prediction horizon.min_sd in both the survivor and event classes,
preventing numerical collapse in degenerate training subsets.predict.nbsurv() now enforces monotone survival curves
via cumulative minimum across increasing horizons.laplace argument, with IPCW-weighted event-class
counts.CNB, single-horizon
prediction).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.