High-Performance Open-Source Archive
timeROC package (v0.4) is being archived on CRAN. Its
required functions have been integrated directly into CalibrationCurves
(R/timeROC_archived.R) with proper attribution.
timeROC has been removed from Imports;
pec has been added instead (required for
ipcw()).valProbCluster(): cl.level
parameter was ignored (Issue #22): The confidence level was
hardcoded to 0.95 throughout the clustering pipeline.
cl.level is now correctly passed to CGC(),
MAC2(), and MIXC() and propagated to
metaprop(), metagen(), and
rma.mv() calls. Hardcoded “95%” plot labels have been
replaced with dynamic labels via the new ci_pi_labels()
helper function.
valProbSurvival(): crash near max follow-up
(Issue #24): Uno’s time-dependent AUC was evaluated at
max(fit$y) - 0.01 instead of the user-specified
timeHorizon, causing “incorrect number of dimensions”
errors when the risk set was depleted. Fixed to use
times = timeHorizon.
New "default" approach in
valProbCluster(): Combines MAC2 (splines) for the
overall calibration curve, confidence intervals, and prediction
intervals, with MIXC for cluster-specific curves. This is now the
default when approach is not specified. The returned object
contains both the MAC2 overall results (results$overall)
and the MIXC cluster results (results$clusters).
Unified the legend title to “Heterogeneity” across all
valProbCluster() approaches.
Updated plot font to sans-serif and increased base size to 11 for improved readability across MIXC, MAC2, and CGC approaches.
cl.level in
valProbCluster().ci_pi_labels() for formatting
confidence/prediction interval labels dynamically."default" approach to the
package vignette.valProbCluster() for calibration of clustered
data with three approaches: MIXC, MAC2, and CGC.valProbSurvival() for calibration of
survival/time-to-event data.genCalCurve() for generalized calibration across
the exponential family.
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