Nonparametric change point detection for multivariate time series. Implements the NP-MOJO methodology proposed in McGonigle and Cho (2025).
You can install the released version of CptNonPar from CRAN with:
install.packages("CptNonPar")
You can install the development version of CptNonPar from GitHub with:
devtools::install_github("https://github.com/EuanMcGonigle/CptNonPar")
For further examples, see the help files within the package. We can generate an example for change point detection as follows.
We generate a univariate time series of length 1000, with a mean change at time 300, and an autocovariance (but not marginal) change at time 650. Then, we perform the multi-lag NP-MOJO algorithm with lags 0 and 1, and print the estimated change points and the associated clusters:
library(CptNonPar) n <- 1000 set.seed(123) noise1 <- stats::arima.sim(model = list(ar = -0.5), n = n, sd = sqrt(1 - 0.5^2)) noise2 <- stats::arima.sim(model = list(ar = 0.5), n = n, sd = sqrt(1 - 0.5^2)) noise <- c(noise1[1:650], noise2[651:n]) signal <- c(rep(0, 300), rep(0.7, 700)) x <- signal + noise x.c <- np.mojo.multilag(x, G = 166, lags = c(0, 1)) x.c$cpts #> cpt lag score #> [1,] 295 0 1.00 #> [2,] 648 1 0.99 x.c$cpt.clusters #> [[1]] #> cpt lag score #> [1,] 295 0 1 #> [2,] 296 1 1 #> #> [[2]] #> cpt lag score #> [1,] 648 1 0.99
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