Function reference • fastcpd

Main function

All implementation of fastcpd is unified into one single function fastcpd().

fastcpd(): Find change points efficiently

Wrapper functions

Simplified functions are provided for each family.

Time series

fastcpd_ts() and fastcpd.ts() are the main functions for time series data (also wrapper functions of fastcpd()).

fastcpd_ar() fastcpd.ar(): Find change points efficiently in AR(\(p\)) models

fastcpd_arima() fastcpd.arima(): Find change points efficiently in ARIMA(\(p\), \(d\), \(q\)) models

fastcpd_arma() fastcpd.arma(): Find change points efficiently in ARMA(\(p\), \(q\)) models

fastcpd_garch() fastcpd.garch(): Find change points efficiently in GARCH(\(p\), \(q\)) models

fastcpd_ts() fastcpd.ts(): Find change points efficiently in time series data

fastcpd_var() fastcpd.var(): Find change points efficiently in VAR(\(p\)) models

Unlabeled data

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

fastcpd_mean() fastcpd.mean(): Find change points efficiently in mean change models

fastcpd_variance() fastcpd.variance(): Find change points efficiently in variance change models

fastcpd_meanvariance() fastcpd.meanvariance() fastcpd_mv() fastcpd.mv(): Find change points efficiently in mean variance change models

Regression data

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

fastcpd_binomial() fastcpd.binomial(): Find change points efficiently in logistic regression models

fastcpd_lasso() fastcpd.lasso(): Find change points efficiently in penalized linear regression models

fastcpd_lm() fastcpd.lm(): Find change points efficiently in linear regression models

fastcpd_poisson() fastcpd.poisson(): Find change points efficiently in Poisson regression models

Utility functions

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

Variance estimation

variance_arma() variance.arma(): Variance estimation for ARMA model with change points

variance_lm() variance.lm(): Variance estimation for linear models with change points

variance_mean() variance.mean(): Variance estimation for mean change models

variance_median() variance.median(): Variance estimation for median change models

Class methods

plot(<fastcpd>) plot(<fastcpd>,<missing>): Plot the data and the change points 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

Data

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

Main class

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

fastcpd-class: An S4 class to store the output created with fastcpd()