robustrolling

High-performance rolling-window statistics for R and Python — six algorithms implemented in C++17, exposed through idiomatic bindings in both languages, with O(1) or O(log n) updates per element.

API Reference

• Python API Reference
  • High-level functions
  • Low-level classes
• R API Reference
  • Rolling Correlation
  • Rolling Covariance
  • Rolling Kurtosis
  • Rolling Maximum
  • Rolling Mean
  • Rolling Median
  • Rolling Minimum
  • Rolling Skewness
  • Rolling Sample Variance

Algorithm overview

C++ class	Algorithm	Complexity	R function(s)	Python function(s)
SlidingWelfordRing	Welford online variance (ring buffer)	O(1)	rolling_variance	rolling_variance, SlidingWelford
MonotonicMax	Monotonic deque maximum	O(1) amortised	rolling_max	rolling_max, MonotonicMax
MonotonicMin	Monotonic deque minimum	O(1) amortised	rolling_min	rolling_min, MonotonicMin
MultisetMedian	std::multiset tracked-iterator median	O(log n)	rolling_median	rolling_median, MultisetMedian
SlidingMoments	Terriberry 4th-moment online algorithm	O(1)	rolling_mean, rolling_skewness, rolling_kurtosis	rolling_mean, rolling_skewness, rolling_kurtosis, SlidingMoments
SlidingCovariance	2-D Welford online covariance	O(1)	rolling_cov, rolling_cor	rolling_cov, rolling_cor, SlidingCovariance

Install for R

# Requires a C++17 compiler (GCC ≥ 7, Clang ≥ 5, MSVC ≥ 2017)
install.packages("remotes")
remotes::install_github("IgorPtak/rolling_window")

Install for Python

# Requires Python ≥ 3.8 and a C++17 compiler
pip install git+https://github.com/IgorPtak/rolling_window.git#subdirectory=py_package

# Or clone and install locally:
git clone https://github.com/IgorPtak/rolling_window.git
pip install rolling_window/py_package/