A Tidy Framework for Changepoint Detection Analysis
Convert a date into a year
Convert, retrieve, or verify a model object
Convert, retrieve, or verify a segmenter object
Convert changepoint sets to binary strings
Bayesian Maximum Descriptive Length
Initialize populations in genetic algorithms
Extract changepoints
Compare various models or algorithms for a given changepoint set
Use a changepoint set to break a time series into regions
Retrieve the degrees of freedom from a logLik object
Diagnose the fit of a segmented time series
Evaluate candidate changepoints sets
Generate a list of candidate changepoints using a genetic algorithm
Compute exceedances of a threshold for a time series
Obtain a descriptive filename for a tidycpt object
Regression-based model fitting
Fast implementation of meanshift model
Fit a model for mean and variance
Fit an NHPP model to one specific region
Fit a non-homogeneous Poisson process model to the exceedances of a ti...
Retrieve the optimal fitness (or objective function) value used by an ...
Class for model-fitting functions
Hannan–Quinn information criterion
Weibull distribution functions
Log-Likelihood functions for regions (Weibull)
Algorithmic coverage through tidychangepoint
Modified Bayesian Information Criterion
Cumulative distribution of the exceedances of a time series
Maximum Descriptive Length
Base class for changepoint models
Retrieve the arguments that a model-fitting function used
Retrieve the name of the model that a segmenter or model used
Compute model variance
Pad and unpad changepoint sets with boundary points
Diagnostic plots for seg_basket objects
Plot the intensity of an NHPP fit
Plot GA information
Objects exported from other packages
Extract the regions from a tidycpt object
Default class for candidate changepoint sets
Base class for segmenters
Retrieve parameters from a segmenter
Algoritmo genético de Bayesian MDL a un paso
Segment a time series using a genetic algorithm
Manually segment a time series
Segment a time series using the PELT algorithm
Segment a time series using a variety of algorithms
Schwarz information criterion
Convert changepoint sets to time indices
Format the coefficients from a linear model as a tibble
Simulate time series with known changepoint sets
tidychangepoint: A Tidy Framework for Changepoint Detection Analysis
Container class for tidycpt objects
Vectors implementation for logLik
Recover the function that created a model
Changepoint detection algorithms for R are widespread but have different interfaces and reporting conventions. This makes the comparative analysis of results difficult. We solve this problem by providing a tidy, unified interface for several different changepoint detection algorithms. We also provide consistent numerical and graphical reporting leveraging the 'broom' and 'ggplot2' packages.