partialpro() gains a new vt.filter
argument for selecting the virtual-twin filtering engine. The default,
vt.filter = "isopro", preserves the existing
isolation-forest filtering behavior. New alternatives are
vt.filter = "outpro", which uses outpro-based
out-of-distribution support, and vt.filter = "none", which
disables VT filtering.outpro-based VT filtering to
partialpro(). For vt.filter = "outpro",
virtual twins are scored by an outpro distance, calibrated
against an outpro.null() reference distribution, and
converted to a support score. The existing cut option is
retained: larger values require stronger support and
cut = 0 disables VT filtering.distancef = "knn" to outpro(). The
KNN distance is computed in the standardized selected predictor subspace
and provides a faster option for large prediction or virtual-twin grids
because it does not require the forest-neighborhood distance
construction.outpro VT filter in partialpro() uses
KNN distance by default through the hidden option
out.distancef = "knn". Additional advanced controls are
available through ..., including out.neighbor,
out.reduce, out.cutoff,
out.max.rules.tree, out.max.tree,
out.knn.chunk.size, and out.null.outpro() now supports newdata.xscale,
allowing package-internal callers to pass new data that are already
aligned to the fitted VarPro x-scale. This is useful for functions such
as partialpro(), where virtual data are constructed
directly from the stored VarPro design matrix.outpro.null() now supports
nulldata.xscale, providing the corresponding x-scale option
for null/reference data.partialpro() help file with a fuller
description of the case-local partial-profile method, virtual-twin
filtering, local polynomial smoothing, classification log-odds handling,
binary-variable handling, and advanced options passed through
....outpro() documentation to describe the KNN
distance option and the x-scale handling used by package-internal
calls.partialpro() so that
nodesize is read from nodesize, not from
ntree.outpro.null() now uses cutoff = NULL by
default, matching the main outpro() cutoff-selection rule
and keeping null calibration consistent with ordinary
outpro() calls.importance() is now a true S3 generic rather than an
alias-style front end.partial.ivarpro() has been replaced by
plot.ivarpro().importance(),
predict(), and plot().importance() methods for
"varpro" and "uvarpro" objects.plot() methods for "ivarpro"
and "partialpro" objects.predict() methods
through standard S3 dispatch for "varpro",
"uvarpro", "ivarpro", and
"isopro" objects.plot.ivarpro, plot.partialpro,
predict.ivarpro, predict.varpro,
predict.uvarpro, and predict.isopro so that
method pages remain easy to find in the reference manual and via
?topic.\method{plot}{ivarpro}(x, ...)
and \method{predict}{ivarpro}(object, ...).plot(x, ...), predict(object, ...), and
importance(object).data for
the original feature matrix and documents target explicitly
for multivariate and multiclass outputs.partial.ivarpro(iv, var = ...) with
plot(iv, var = ...).importance(fit) over direct calls to
importance.varpro(fit).predict(fit, ...) over direct calls to
predict.class(fit, ...).varpro.strength() to reduce R-side
post-processing overhead after the native varProStrength
call, improving performance on large forests and large membership
reconstructions.varpro.strength(..., membership = TRUE) for very large
analyses.varpro.strength() now uses the
integrated hazard exposure values stored on the fitted object
(int.haz.oob) as the default working response when
available.cumsum() followed by a downstream missing-value error in
membership reconstruction.varpro() function.ivarPro.mclapply() with PSOCK-based
parallel execution, improving Windows compatibility.
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