Package index • fastcpd

Main function

All implementations are unified behind detect().

detect() fastcpd(): Find change points efficiently
detect_family fastcpd_family fastcpd.family: Family-specific change point detection

Simplified functions are provided for each family.

Family-specific convenience functions for supported time-series models.

detect_ar() fastcpd_ar() fastcpd.ar(): Find change points efficiently in AR(\(p\)) models
detect_arima() fastcpd_arima() fastcpd.arima(): Find change points efficiently in ARIMA(\(p\), \(d\), \(q\)) models
detect_arma() fastcpd_arma() fastcpd.arma(): Find change points efficiently in ARMA(\(p\), \(q\)) models
detect_garch() fastcpd_garch() fastcpd.garch(): Find change points efficiently in GARCH(\(p\), \(q\)) models
detect_var() fastcpd_var() fastcpd.var(): Find change points efficiently in VAR(\(p\)) models

Used for data without response variables, for example, mean change and variance change.

detect_exponential() fastcpd_exponential() fastcpd.exponential(): Find change points efficiently in exponentially distributed data
detect_kernel() detect_kcp() fastcpd_kcp() fastcpd.kcp(): Find change points efficiently via kernel change point detection
detect_mean() fastcpd_mean() fastcpd.mean(): Find change points efficiently in mean change models
detect_rank() fastcpd_rank() fastcpd.rank(): Find change points efficiently via rank-based change point detection
detect_variance() fastcpd_variance() fastcpd.variance(): Find change points efficiently in variance change models
detect_meanvariance() detect_mean_variance() fastcpd_meanvariance() fastcpd.meanvariance() fastcpd_mv() fastcpd.mv(): Find change points efficiently in mean variance change models

Detect change points in the coefficients of regression-type data.

detect_binomial() detect_logistic_regression() fastcpd_binomial() fastcpd.binomial(): Find change points efficiently in logistic regression models
detect_lasso() fastcpd_lasso() fastcpd.lasso(): Find change points efficiently in penalized linear regression models
detect_lm() detect_linear_regression() fastcpd_lm() fastcpd.lm(): Find change points efficiently in linear regression models
detect_poisson() detect_poisson_regression() fastcpd_poisson() fastcpd.poisson(): Find change points efficiently in Poisson regression models
detect_quantile() detect_quantile_regression() fastcpd_quantile() fastcpd.quantile(): Find change points efficiently in quantile regression models

The following functions help with visualization and analyzation of the data.

estimate_variance(): Estimate variance for change point models
estimate_variance_arma() variance_arma() variance.arma(): Variance estimation for ARMA model with change points
estimate_variance_linear_regression() estimate_variance_lm() variance_lm() variance.lm(): Variance estimation for linear models with change points
estimate_variance_mean() variance_mean() variance.mean(): Variance estimation for mean change models
estimate_variance_median() variance_median() variance.median(): Variance estimation for median change models

confint(<fastcpd>): Confidence intervals for a fastcpd object
plot(<fastcpd>) plot(<fastcpd>,<missing>): Plot the data and the change points for a fastcpd object
plot(<fastcpd_confint>): Plot confidence intervals for a fastcpd object
print(<fastcpd>): Print the call and the change points for a fastcpd object
show(<fastcpd>): Show the available methods for a fastcpd object
summary(<fastcpd>): Show the summary of a fastcpd object

fastcpd comes with a selection of built-in datasets that are used in examples to illustrate various change point detection challenges.

bitcoin: Bitcoin Market Price (USD)
occupancy: Occupancy Detection Data Set
transcriptome: Transcription Profiling of 57 Human Bladder Carcinoma Samples
uk_seatbelts: UK Seatbelts Data
well_log: Well-log Dataset from Numerical Bayesian Methods Applied to Signal Processing

The results of detect() are stored in the class fastcpd with accompanied several utility functions.