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Changelog

Source: NEWS.md

RcppRoll 0.3.1 (UNRELEASED)

CRAN release: 2024-07-07

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

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

RcppRoll 0.3.1

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().