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correctHeaps() and correctSingleHeap()
gain survey-weight support via a new weight= argument.
Heaping ratios and the selection of records to correct then use weighted
counts (records are drawn uniformly and accumulated until their
cumulative weight covers the excess mass).
Breaking: model-based correction is redesigned.
Instead of a random-forest sign-adjustment heuristic,
correctHeaps(model=, dataModel=) now fits an imputation
model for age given the covariates on the retained (“trusted”)
records and draws covariate-conditional replacements for the selected
heaped records. The engine is selectable via
model.engine = c("ranger", "lm") (a ranger
quantile forest by default). Corrected values therefore differ from
0.1.x, even with the same seed.
New width= argument controls the truncation
half-window; 10-year heaps now use a single symmetric window instead of
the previous two-stage +/-4 / +/-5 correction.
New correctHeapsMI() produces m
corrected datasets (with deterministically derived seeds) for
multiple-imputation inference, with a print() method for
the returned heapingMI object.
.adjust_signs() and the
legacy .draw_replacements(); the correction engine now
lives in R/impute-model.R.Fixed cascading drift in correctHeaps() when using
custom heap positions. When heaps were specified at consecutive integers
(e.g., heaps = seq(2, max(x), by = 1)), observations
corrected for one heap could be picked up and re-corrected at subsequent
heaps, causing values to drift far from their original position
(reported by Saskia Schirmer).
Fixed R’s sample() single-value trap in both
correctHeaps() and correctSingleHeap(). When
only one observation was available for correction at a heap,
sample(n, size = 1) would sample from 1:n
instead of returning n, potentially writing replacement
values to wrong indices.
Added a warning when more than 50% of unique values in the data
are declared as heaps, indicating likely misspecification of the
heaps argument. Heaping correction is designed for sparse
heap positions (e.g., multiples of 5 or 10), not for every value in the
data.
correctHeaps() and correctSingleHeap() for
individual-level heaping correction using truncated log-normal, normal,
uniform, or kernel density distributions.whipple(), myers(),
bachi(), noumbissi(),
spoorenberg(), coale_li(),
jdanov(), kannisto(), and
heaping_indices().sprague() for disaggregating 5-year age groups using
Sprague multipliers.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.