Behavioral Change Point Analysis of Animal Movement
Obtain summary of BCPA analysis
Diagnostic plot for BCPA
Find most likely change point in irregular time series
Obtain log-likelihood and parameter estimates for a given break point.
Partition parameters
Behavioral Change Point Analysis
Obtain likelihood estimates of gappy Gaussian time series
Model selection at a known breakpoint
Characteristic time / auto-correlation for irregular time series
Obtain VT table from Track
Make Track
Path plot of BCPA output
Phase plot of BCPA output
Plotting method for BCPA output
Plot Track
Perform window sweep for BCPA
The Behavioral Change Point Analysis (BCPA) is a method of identifying hidden shifts in the underlying parameters of a time series, developed specifically to be applied to animal movement data which is irregularly sampled. The method is based on: E. Gurarie, R. Andrews and K. Laidre A novel method for identifying behavioural changes in animal movement data (2009) Ecology Letters 12:5 395-408. A development version is on <https://github.com/EliGurarie/bcpa>. NOTE: the BCPA method may be useful for any univariate, irregularly sampled Gaussian time-series, but animal movement analysts are encouraged to apply correlated velocity change point analysis as implemented in the smoove package, as of this writing on GitHub at <https://github.com/EliGurarie/smoove>. An example of a univariate analysis is provided in the UnivariateBCPA vignette.