Welcome to rupturesRcpp

Description

The R package provides an efficient, object-oriented R6 interface for offline change point detection, implemented in C++ for high performance. This was created as part of the Google Summer of Code 2025 program (see edelweiss611428/rupturesRcpp at gsoc-2025 for the project archive).

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|                                                            |
|          Google Summer of Code 2025 Program                |
|                                                            | 
|  Project: rupturesRcpp                                     |
|  Contributor: @edelweiss611428                             |
|  Mentors: @tdhock & @deepcharles                           |
|  Organisation: The R Project for Statistical Computing     |
|                                                            |
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Installation

To install the newest version of the package, use the following R code:

library(devtools)
install_github("edelweiss611428/rupturesRcpp") 

Getting started

To detect change-points using rupturesRcpp you need three main components:

• Cost function (costFunc)
• Segmentation method (binSeg, Window, PELT)
• Linear penalty threshold

Each component is implemented using an R6-based object-oriented design for modularity and maintainability.

Cost functions

Create a costFunc object by specifying the desired (supported) cost function and optional parameters:

library("rupturesRcpp")
costFuncObj = costFunc$new("L2")
costFunc$pass() #output attributes corresponding to the specified cost function.

$costFunc
[1] "L2"

The following table shows the list of supported cost functions. Here, n is segment length.

Cost function	Description	Parameters/active bindings	Dimension	Time complexity
"L1"	Sum of L1 distances to the segment-wise median; robust to outliers.	costFunc	multi	O(nlog(n))
"L2"	Sum of squared L2 distances to the segment-wise mean; faster but less robust than L1.	costFunc	multi	O(1)
"SIGMA"	Log-determinant of empirical covariance; models varying mean&variance.	costFunc, addSmallDiag, epsilon	multi	O(1)
"VAR"	Sum of squared residuals from a vector autoregressive model with constant noise variance.	costFunc, pVAR	multi	O(1)
"LinearL2"	Sum of squared residuals from a linear regression model with constant noise variance.	costFunc, intercept	multi	O(1)

If active binding costFunc is modified by assigning to costFuncObj$costFunc and the required parameters are missing, the default parameters will be used.

costFuncObj$costFunc = "VAR"
costFuncObj$pass()

$costFunc
[1] "VAR"

$pVAR
[1] 1

Segmentation methods

After initialising a costFunc object, create a segmentation object such as binSeg, Window, or PELT.

R6 Class	Method	Description	Parameters/active bindings
binSeg	Binary Segmentation	Recursively splits the signal at points that minimise the cost.	minSize, jump, costFunc, tsMat, covariates
Window	Slicing Window	Detects change-points using local gains over sliding windows.	minSize, jump, radius, costFunc,tsMat, covariates
PELT	Pruned Exact Linear Time	Optimal segmentation with pruning for linear-time performance.	minSize, jump, costFunc, tsMat, covariates

The covariates argument is optional and only required for models involving both dependent and independent variables (e.g., "LinearL2"). If not provided, the model is force-fitted using only an intercept term (i.e., a column of ones).

A PELT object, for example, can be initialised as follows:

detectionObj = PELT$new(minSize = 1L, jump = 1L, costFunc = costFuncObj)

All segmentation objects implement the following methods:

• $describe(printConfig): Views the (current) configurations of the object.
• $fit(tsMat, covariates): Constructs a C++ detection module corresponding to the current configurations.
• $predict(pen): Performs change-point detection given a linear penalty value.
• $eval(a,b): Evaluates the cost of a segment (a,b].
• $plot(d, endPts,...): Plots change-point segmentation in ggplot style.

Active bindings (such as minSize or tsMat) can be modified at any time—either before or after the object is created via the $ operator. For consistency, if the object has already been fitted, modifying any active bindings will automatically trigger the re-fitting process.

detectionObj$minSize = 2L #Before fitting
detectionObj$fit(a_time_series_matrix) #Fitted
detectionObj$minSize = 1L #After fitting - automatically trigger `$fit()`

Simulated data examples

2-regime SIGMA example via binary segmentation

To demonstrate the package usage, we first consider a simple 2d time series with two piecewise Gaussian regimes and varying variance.

set.seed(1)
tsMat = cbind(c(rnorm(100,0), rnorm(100,5,5)),
              c(rnorm(100,0), rnorm(100,5,5)))

As our example involves regimes with varying variance, a suitable costFunc option is "SIGMA". Since the segmentation objects' interfaces are similar, it is sufficient to demonstrate the usage of binSeg only.

SIGMAObj = costFunc$new("SIGMA", addSmallDiag = TRUE, epsilon = 1e-6)
binSegObj = binSeg$new(minSize = 1L, jump = 1L, costFunc = SIGMAObj) 
binSegObj$fit(tsMat) 

Once fitted, $predict() and $eval() can be used. To view the configurations of the binSeg object, we can use $describe().

binSegObj$describe(printConfig = TRUE) 

Binary Segmentation (binSeg)
minSize      : 1L
jump         : 1L
costFunc     : "SIGMA"
addSmallDiag : TRUE
epsilon      : 1e-06
fitted       : TRUE
n            : 200L
p            : 2L

To obtain an estimated segmentation, we can use the $predict() method and specify a non-negative penalty value pen, which should be properly tuned. This returns a sorted integer vector of end-points, including the number of observations by design.

Here, we set pen = 100.

binSegObj$predict(pen = 100)

[1] 100 200

After running $predict(), the segmentation output is temporarily saved to the binSeg object, allowing users to use the $plot() method without specifying endPts.

binSegObj$plot(d = 1:2, 
               main = "method: binSeg; costFunc: SIGMA; pen: 100")

2-regime VAR example: Modifying a binSeg object through its active bindings

You can also modify a binSeg object's fields through its active bindings. To demonstrate this, we consider a piecewise vector autoregressive example with constant noise variance.

set.seed(1)
tsMat = matrix(c(filter(rnorm(100), filter = 0.9, method = "recursive"), 
                 filter(rnorm(100), filter = -0.9, method = "recursive")))

Here, the most suitable cost function is "VAR". Instead of creating a new binSeg object, we will modify the current binSegObj as follows:

VARObj = costFunc$new("VAR")
binSegObj$tsMat = tsMat
binSegObj$costFunc = VARObj

Modifying tsMat (or any other bindings) will automatically trigger self$fit() if a tsMat has already existed.

binSegObj$describe(printConfig = TRUE)

Binary Segmentation (binSeg)
minSize      : 1L
jump         : 1L
costFunc     : "VAR"
pVAR         : 1L
fitted       : TRUE
n            : 200L
p            : 1L

We can then perform binary segmentation with pen = 25 and plot the segmentation results.

binSegObj$predict(pen = 25)
binSegObj$plot(d = 1L, 
               main = "method: binSeg; costFunc: VAR; pen: 25")

Future development

• Increase testing for robustness and correctness of existing modules (e.g., mathematical correctness, time complexities).
• Improve the "L1" cost module, potentially allowing queries in O(log(n)) time using data structures such as a persistent segment tree with O(nlog(n)) precomputation.
• Clean and enhance the existing object-oriented interface for improved efficiency, robustness, and accessibility.
• Implement additional cost functions (e.g., "Poisson" and "Linear-L1").
• Implement other offline change-point detection classes (e.g., Opt and BottomUp).
• Enhance existing $eval() methods for parameter estimation.
• Develop a costFactory class for users focusing solely on fast cost computation and parameter estimation.
• Improve $plot() method for models involving both dependent and independent variables.
• Provide instructions for future contributors.

Contributing

We welcome all contributions, whether big or small. If you encounter a bug or have a feature request, please open an issue to let us know.

Feel free to fork the repository and make your changes. For significant updates, it’s best to discuss them with us first. When your changes are ready, submit a pull request.

Thanks for helping us improve this project!

License

This project is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

References

• Hocking, T. D. (2024). Finite Sample Complexity Analysis of Binary Segmentation. arXiv preprint arXiv:2410.08654.
• Truong, C., Oudre, L., & Vayatis, N. (2020). Selective review of offline change point detection methods. Signal Processing, 167, 107299.
• Killick, R., Fearnhead, P., & Eckley, I. A. (2012). Optimal detection of change points with a linear computational cost. Journal of the American Statistical Association, 107(500), 1590–1598.