Implements the chain binomial model for analysis of infectious disease data.
    Contains functions for calculating probabilities of the final size of infectious
    disease outbreaks using the method from D. Ludwig (1975) 
    <doi:10.1016/0025-5564(75)90119-4> and for outbreaks that are not 
    concluded, from Lindstrøm et al. (2024) <doi:10.48550/arXiv.2403.03948>. The package
    also contains methods for estimation and regression analysis of 
    secondary attack rates.

Various statistical methods and models which are
    typically used for the estimation of outstanding claims reserves
    in general insurance, including those to estimate the claims
    development result as required under Solvency II.

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.

The Genetic Algorithm (GA) is used to perform changepoint analysis in time series data. The package also includes an extended island version of GA, as described in Lu, Lund, and Lee (2010, <doi:10.1214/09-AOAS289>). By mimicking the principles of natural selection and evolution, GA provides a powerful stochastic search technique for solving combinatorial optimization problems. In 'changepointGA', each chromosome represents a changepoint configuration, including the number and locations of changepoints, hyperparameters, and model parameters. The package employs genetic operators—selection, crossover, and mutation—to iteratively improve solutions based on the given fitness (objective) function. Key features of 'changepointGA' include encoding changepoint configurations in an integer format, enabling dynamic and simultaneous estimation of model hyperparameters, changepoint configurations, and associated parameters. The detailed algorithmic implementation can be found in the package vignettes and in the paper of Li (2024, <doi:10.48550/arXiv.2410.15571>).