Contains a function, also called 'cchs', that calculates Estimator III of Borgan et al (2000), <DOI:10.1023/A:1009661900674>. This estimator is for fitting a Cox proportional hazards model to data from a case-cohort study where the subcohort was selected by stratified simple random sampling.

Supporting the use of the Canadian Community Health Survey 
             (CCHS) by transforming variables from each cycle into harmonized, 
             consistent versions that span survey cycles (currently, 2001 to 
             2018). CCHS data used in this library is accessed and adapted in 
             accordance to the Statistics Canada Open Licence Agreement. This 
             package uses rec_with_table(), which was developed from 'sjmisc' 
             rec(). Lüdecke D (2018). "sjmisc: Data and Variable Transformation 
             Functions". Journal of Open Source Software, 3(26), 754. 
             <doi:10.21105/joss.00754>.

Provides efficient implementation of the Cross-Covariance
    Isolate Detect (CCID) methodology for the estimation of the number
    and location of multiple change-points in the second-order
    (cross-covariance or network) structure of multivariate, possibly
    high-dimensional time series. The method is motivated by the detection
    of change points in functional connectivity networks for functional
    magnetic resonance imaging (fMRI), electroencephalography (EEG),
    magentoencephalography (MEG) and electrocorticography (ECoG) data. The
    main routines in the package have been extensively tested on fMRI data. 
    For details on the CCID methodology, please see Anastasiou et
    al (2022), Cross-covariance isolate detect: A new change-point method for
    estimating dynamic functional connectivity. Medical Image Analysis, Volume
    75.

Convex Clustering methods, including K-means algorithm,
  On-line Update algorithm (Hard Competitive Learning) and Neural Gas
  algorithm (Soft Competitive Learning), and calculation of several
  indexes for finding the number of clusters in a data set.