Functional Change Point Detection and Analysis
ACF/PACF Functions
Adaptive_bandwidth
Estimate the autocorrelation function of the series
Estimate the autocovariance function of the series
Average Functions for dfts Objects
Generic Centering of Data
Compute (Overnight) Cumulative Intraday Returns
Change Point Confidence Intervals
Group Generic Functions
dfts Objects
Difference dfts
Dimension of dfts Object
Extract or Replace parts of dfts object
General Information for fChange
Change Point Detection
Generate a Brownian Bridge Process
Generate a Brownian Motion Process
Generate Functional Data
Generate FAR(1) Data
Generate functional data
Generate Data via Ornstein-Uhlenbeck Process
Functional Imputation
Kernel Functions
Functional KPSS Test
Lag dfts objects
Estimate Long-run Covariance Kernel
Max / Min for dfts Objects
Functional PCA Components
Principal Component Exploration
Generic Function for Principal Component Analysis
Pipe operator
Plot dfts objects
Functional Hypothesis Tests for Functional Data
Print dfts objects
Projection-based functional data model
QQ Plot Generic Function
Quantile dfts
Functional Spherical Autocorrelation Function
Generic Function for Variance and Standard Deviation Computation
Compute Spacing Measuring Functions
Draw 3D Geoms for ggplot2
Functional Stationarity Test
Summary for dfts Object
Analyze functional data and its change points. Includes functionality to store and process data, summarize and validate assumptions, characterize and perform inference of change points, and provide visualizations. Data is stored as discretely collected observations without requiring the selection of basis functions. For more details see chapter 8 of Horvath and Rice (2024) <doi:10.1007/978-3-031-51609-2>. Additional papers are forthcoming. Focused works are also included in the documentation of corresponding functions.