RcppRoll

• 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)