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Welcome to rupturesRcpp

rupturesRcpp is an R package for high-performance offline change-point detection in multivariate time series. It provides a unified, object-oriented R6 interface to change-point detection methods implemented efficiently in C++. It is inspired by the highly popular Python library ruptures.

The package detects changes in the underlying structure of a time series, including changes in mean, covariance, autoregressive dynamics, and regression relationships with covariates. It supports a range of cost functions, and segmentation algorithms, from fast heuristic methods to exact dynamic-programming and penalised optimisation methods.

Documentation and case studies are available on the package website. See Getting started for the differences between the two versions and a first detection.

Supported Methods

Cost Functions

Cost function	Detects changes in
`"L1"`, `"L2"`	Mean structure; `"L1"` provides greater robustness to outliers
`"SIGMA"`	Mean and covariance structure
`"VAR"`	Vector autoregressive dynamics
`"LinearL2"`, `"LinearL1"`	Linear regression relationships with covariates; `"LinearL1"` provides greater robustness to outliers
`"LinearSIGMA"`	Regression relationships and residual covariance
`"Custom"`	User-defined structural changes through an R cost function

Segmentation Methods

Method	Search strategy
`binSeg`	Binary segmentation; a fast greedy search
`Window`	Sliding-window search based on local changes
`PELT`	Penalised optimisation with pruning
`Dynp`	Dynamic programming for a specified number of change-points

Installation

# Development version, documented on the package website
install.packages("rupturesRcpp",
                 repos = c("https://edelweiss611428.r-universe.dev",
                           "https://cloud.r-project.org"))

# CRAN release (1.0.3)
install.packages("rupturesRcpp")

Acknowledgement

The package was developed during Google Summer of Code 2025 for The R Project for Statistical Computing by @edelweiss611428, under the mentorship of @tdhock and @deepcharles. The original GSoC project archive is available on the gsoc-2025 branch.

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.
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