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svpChange

svpChange implements Smallest Valid Partitioning (SVP) for multiple change-point detection in univariate time series. SVP combines dynamic programming with a local validity constraint: a segment is retained only when its selected validity statistic stays below a user-defined threshold.

The computational validity tests are implemented in C++ through Rcpp.

Installation

install.packages("remotes")
remotes::install_github("vrunge/svpChange")

For local development:

remotes::install_local("path/to/svpChange")

Basic use

library(svpChange)
set.seed(1)
n <- 120
y <- ts_generator(
  chpts = c(40, 80, 120), parameters = c(0, 2, -1),
  sd_noise = 1, type = "gauss"
)

fit <- SVP(y, gamma = 1.5 * log(length(y)), test = "gaussian_mean")
fit$changepoints

changepoints contains the inclusive end index of every segment, including the final observation. R contains the dynamic-programming cost, segment count, and previous boundary for each time point.

Built-in validity tests

SVP() accepts these values of test:

Test	Use
`gaussian_mean`	Gaussian FOCUS mean-change test
`gamma_rate`	Change in rate for positive Gamma observations
`gaussian_variance`	Variance changes through squared observations
`quantile`, `quantileExact`	Quantile-based robust tests
`varCost`	Robust variance cost test
`WilcoxonCost`	Wilcoxon rank-based test
`MedianMoodCost`	Median-Mood rank-based test
`AR1`	Exact fixed-`rho` AR(1) mean-change test
`AR1Profile`	AR(1) test with profiled innovation variance
`AR1Focus`	Faster innovation-based AR(1) approximation

For quantile tests:

SVP(y, gamma = 10, test = "quantile", quantile = 0.05)

For AR(1) data:

fit_ar1 <- SVP(y, gamma = 10, test = "AR1", rho = 0.7, sigma2 = 1)

If rho is omitted, SVP() estimates it robustly using AR1_rho().

Pruning and multiscale thresholds

The subtests argument controls the candidate set:

SVP(y, gamma = 10, test = "gaussian_mean",
    subtests = "both")

The values are "none", "right", and "both". Use "none" for arbitrary validity rules unless the pruning assumptions have been established for the selected test.

User-defined validity tests

Use svp0() when the validity rule is an R function:

my_test <- function(segment, gamma) max(segment) - min(segment) <= gamma
fit <- svp0(y, gamma = 5, test = my_test)

The package also provides valid_FOCUS(), valid_AR1(), valid_SSE(), valid_RANGE(), valid_RANGE_SLACK(), valid_QUANTILE(), valid_SCALE(), and valid_OP() for svp0().

Other algorithms and utilities

OP(), PELT(), and SN() are available for algorithm comparisons. AR1_rho() and AR1_single_change() provide AR(1) diagnostics, and ts_generator() generates simulation signals.

Simulations

The simulations/ directory contains paper-style experiments:

power_gaussian/  Gaussian power studies
power_ar1/       AR(1) power studies
power_robust/    Heavy-tailed power studies
time_gaussian/   Gaussian runtime studies
time_ar1/        AR(1) runtime studies
time_robust/     Robust runtime studies
other_simus/     Supporting, historical, and application studies

Each primary folder contains a README.md. Large Monte Carlo studies are opt-in; see simulations/README.md for the run convention.

Testing

From the package root:

devtools::test()

The tests cover Gaussian, robust, quantile, AR(1), pruning, and algorithm-equivalence behavior.

License

GPL-3. See DESCRIPTION for authorship and package metadata.

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.