Assign distinct colors to arbitrary multi-dimensional data, considering its structure.

Performs adjustments of a user-supplied independence loglikelihood 
    function using a robust sandwich estimator of the parameter covariance 
    matrix, based on the methodology in Chandler and Bate (2007) 
    <doi:10.1093/biomet/asm015>.  This can be used for cluster correlated data 
    when interest lies in the parameters of the marginal distributions or for 
    performing inferences that are robust to certain types of model 
    misspecification.  Functions for profiling the adjusted loglikelihoods are 
    also provided, as are functions for calculating and plotting confidence 
    intervals, for single model parameters, and confidence regions, for pairs 
    of model parameters.  Nested models can be compared using an adjusted 
    likelihood ratio test.

Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data.  Many popular non-parametric and frequentist methods are included.  The cpt.mean(), cpt.var(), cpt.meanvar() functions should be your first point of call.

Implements the high-dimensional changepoint detection method GeomCP and the related mappings used for changepoint detection. These methods view the changepoint problem from a geometrical viewpoint and aim to extract relevant geometrical features in order to detect changepoints. The geomcp() function should be your first point of call. References: Grundy et al. (2020) <doi:10.1007/s11222-020-09940-y>.

Allows users to input their data, segmentation and function used for the segmentation (and additional arguments) and the package calculates the influence of the data on the changepoint locations, see Wilms et al. (2022) <doi:10.1080/10618600.2021.2000873>.  Currently this can only be used with the changepoint package functions to identify changes, but we plan to extend this.  There are options for different types of graphics to assess the influence.

Implements the multiple changepoint algorithm PELT with a nonparametric cost function based on the empirical distribution of the data. This package extends the changepoint package (see Killick, R and Eckley, I (2014) <doi:10.18637/jss.v058.i03> ).