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Changelog

Source: NEWS.md

RcppRoll (development version)

Performance improvements

  • The from-scratch window loops now run several windows abreast, so that the compiler vectorizes across them rather than waiting on one window’s chain of additions. Each window still meets its observations in the same order, so results are unchanged, up to fused multiply-add rounding on hardware that has it. Every call below the incremental crossovers benefits – sums, means and products over small windows run 1.5-4x faster – and so does every weighted call, which has no incremental form: weighted sums and means with a 200-element window run 4.7x faster, weighted products 6.8x. The crossovers have been re-measured to match.

  • roll_min() and roll_max() now compute their windows with van Herk / Gil-Werman block scans: three comparisons per observation whatever the window size, in place of the monotonic deque. Windows of 20 or more observations run 4-8x faster. Results are identical, ties and the sign of a zero included.

  • roll_var() and roll_sd() now stop direct window calculations at the first missing value when na.rm = FALSE, avoiding the remaining mean and variance passes. Calls with na.rm = TRUE keep their existing calculation. (#59)

  • roll_median() now selects directly when a call computes at most four windows per column, avoiding the cost of building incremental median state for too few outputs to benefit from it. (#59)

  • roll_prod() now uses direct window calculations for calls with at most sixteen outputs per column, avoiding incremental setup for short results. Unnormalized weights are read directly, avoiding a temporary copy.

  • Ordinary variance and standard deviation use bounded arithmetic, retaining the scaled calculation for extreme inputs and poorly centered windows. Weighted extrema classify inputs once per work chunk, and means finalize ordinary windows together, reducing the cost of numerical and missing-value safeguards on large vectors with small weight vectors. Input checks run within the existing work chunks so they benefit from OpenMP too.

Bug fixes

  • Normalized weighted means retain representable results when removing missing observations leaves tiny weights whose products underflow.

  • Rolling products use forward multiplication for windows at risk of overflow or underflow, including zeros and infinities. Ordinary windows retain the incremental two-stack path; regrouping can change rounding in the low bits. Previously, regrouping could return NaN where a fresh product returned zero.

  • Large finite values no longer overflow the intermediate sum used for means, median midpoints, or centering variance and standard deviation calculations. Normalizing finite weights now tolerates a huge common scale and preserves representable subnormal weights through rescaling. Frequency weights avoid unnecessary overflow or underflow in variance contributions. Squared deviations can still overflow, including when the final standard deviation would be representable.

  • n, by, logical controls, weight normalization, and frequency weights for variance are now validated before dispatch. This also prevents integer overflow for very large valid by values.

  • Weighted minima and maxima now apply missing-value handling to the weighted product and preserve the distinction between NA and NaN. Unweighted extrema preserve that distinction as well.

  • Custom fill values are now recycled to the documented three regions and apply when the input is shorter than the window. na_locf() now preserves valid factor storage while carrying factor levels forward.

  • With unnormalized weights, na.rm = TRUE no longer changes a complete weighted mean’s denominator.

  • Without na.rm, a from-scratch sum, mean or product over a window holding both an NA and a NaN now reports NA, as the incremental routines already did. Previously whichever payload the hardware carried through the arithmetic won, so the answer could depend on the window size.

RcppRoll 0.4.0

New features

  • The rolling window computations are now parallelized with OpenMP, where support for it is available. By default, the number of threads is chosen by the OpenMP runtime (e.g. via OMP_NUM_THREADS); it can be set explicitly with options(RcppRoll.threads = <n>), and options(RcppRoll.threads = 1) disables parallelization. Work is split into chunks whose boundaries do not depend on the thread count, so results are identical whatever the number of threads – including on builds without OpenMP support at all.

  • The new roll_threads() function reports the number of threads in use, or NA when the package was compiled without OpenMP support. The package reports this on attach as well; suppress the startup message with options(RcppRoll.quiet = TRUE). Instructions for enabling OpenMP when installing from sources on macOS are in the README.

  • The partial argument is now implemented. With partial = TRUE, windows at the edges of x are computed over however many elements are in range rather than filled, so the result has one element per element of x. This matches zoo::rollapply(partial = TRUE). (#18)

    partial = TRUE cannot be combined with weights, and only TRUE or FALSE are accepted – zoo’s numeric “minimum observations” form is not supported. fill does not apply, and is warned about if supplied.

Performance improvements

  • Taken together, the changes below speed up every rolling operation. Benchmarked against RcppRoll 0.3.2 over a vector of one million observations, single-threaded speedups range from about 1.7x for tiny windows to more than 150x for large ones, and an OpenMP build multiplies that by roughly the core count: roll_median() with a 500-element window drops from 13 seconds to 77 milliseconds single-threaded, and to 9 milliseconds on a 14-thread laptop.

  • Rolling windows are now computed incrementally where the operation admits it: a window carries its state forward and pays only for the observations that enter and leave, so the per-point cost no longer grows with the window size. Compensated summation and a rebuild-whenever-degraded check keep the sliding totals accurate – on badly conditioned inputs the worst relative error falls from about 2e-2 to 3e-14 – and running totals that overflow to infinity recover once the offending values leave the window. (#51)

  • roll_median() now keeps large windows in a pair of heaps meeting at the median, so that sliding costs O(log n) per point rather than O(n). Windows below about two hundred observations keep the sorted-window representation, which remains faster there.

  • roll_prod() now slides its window incrementally rather than recomputing each window in full, so its cost no longer grows with the window size. The window is carried as two stacks of partial products – departing values are never divided out, so zeros, infinities and rounding behave as a fresh multiplication would.

  • Matrices whose columns are too short to split into chunks are now parallelized across their columns instead, so wide matrices benefit from OpenMP too. As before, results are identical whatever the number of threads.

  • The window loops that dominate small-window calls have been rewritten branchlessly where measurement showed a win, with bit-identical results: roll_sum() and roll_mean() with na.rm = TRUE run 1.4-2.5x faster, roll_max() with na.rm = TRUE 1.4-1.8x faster, and the weighted roll_median() about 1.8x faster.

Bug fixes

  • roll_var() and roll_sd() now compute a weighted variance when weights is supplied, rather than the variance of the weighted values. Weights are treated as frequency weights, so an equal weight vector gives the same answer as the unweighted routines. (#47)

  • roll_var() and roll_sd() now keep each weight paired with its own value when na.rm = TRUE, instead of shifting the weights when NAs are dropped. (#47)

  • roll_var() and roll_sd() now return NA for a window holding fewer than two non-missing values, matching var(). Previously an all-NA window gave 0 and a single-value window gave NaN. (#47)

  • Fixed an issue where roll_mean() produced incorrect results when both weights and na.rm = TRUE were used. The weights are now re-normalized after removing NAs. (#23)

  • Fixed an issue where the weighted version of roll_median() ignored na.rm, and associated each weight with the sorted position of a value rather than with the value itself.

  • Fixed an issue where rolling over a matrix with weights sized the output from n rather than from the weights, so each column read past its end and returned undefined values; and an issue where a matrix result with by greater than one was sized as though by were one, leaving rows that were never computed. (#51)

  • Degenerate window geometries are now handled safely: calls with fewer observations than one window return an empty result rather than reading out of bounds (or failing with an opaque error), a window size of zero is rejected rather than corrupting memory, and arrays with three or more dimensions are rejected rather than rolled over their flattened data.

  • The roll_*() functions now warn when n and weights are both supplied and disagree, since weights silently determines the window size. (#39)

RcppRoll 0.3.2

CRAN release: 2026-03-28

  • Resolve NOTE on CRAN.

RcppRoll 0.3.1

CRAN release: 2024-07-07

  • Fixed an issue where roll_median() produced incorrect results in the presence of NAs. (#42)

RcppRoll 0.3.0

CRAN release: 2018-06-05

  • Properly document the align argument – the function accepts “center” rather than “middle”. (#28)

  • Fixed an issue where empty fills were not handled correctly.

  • The interface has now been standardized such that each implemented window function has version center-aligned by default (e.g. roll_mean()), a left-aligned version (roll_meanl()), and right-aligned version (roll_meanr()).

  • Implement rolling window functions for mean(), median(), min(), max(), prod(), sum(), sd() and var().