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>.

Tool for performing computational testing for conditional independence between variables in a dataset. 'CCI' implements permutation in combination with Monte Carlo Cross-Validation in generating null distributions and test statistics. For more details see Computational Test for Conditional Independence (2024) <doi:10.3390/a17080323>.

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.

Implementation of case-control data analysis using likelihood ratio
    approaches and logistic regression for the classification of variants of
    uncertain significance (VUS) in breast, ovarian, or custom cancer
    susceptibility genes.