Calculating the net reclassification improvement (NRI) for risk prediction models with time to event and binary data.

Provides a set of functions to scrape and analyze rugby data. 
    Supports competitions including the National Rugby League, New South Wales Cup, 
    Queensland Cup, Super League, and various representative and women's competitions. 
    Includes functions to fetch player statistics, match results, ladders, venues, and coaching data. 
    Designed to assist analysts, fans, and researchers in exploring historical and current rugby league data. 
    See Woods et al. (2017) <doi:10.1123/ijspp.2016-0187> for an example of rugby league performance analysis methodology.

Routines for fitting and simulating data under autoregressive fractionally integrated moving average (ARFIMA) models, without the constraint of covariance stationarity. Two fitting methods are implemented, a pseudo-maximum likelihood method and a minimum distance estimator. Mayoral, L. (2007) <doi:10.1111/j.1368-423X.2007.00202.x>. Beran, J. (1995) <doi:10.1111/j.2517-6161.1995.tb02054.x>.

Two implementations of canonical correlation analysis
        (CCA) that are based on iterated regression. By choosing the
        appropriate regression algorithm for each data domain, it is
        possible to enforce sparsity, non-negativity or other kinds of
        constraints on the projection vectors. Multiple canonical
        variables are computed sequentially using a generalized
        deflation scheme, where the additional correlation not
        explained by previous variables is maximized. nscancor() is
        used to analyze paired data from two domains, and has the same
        interface as cancor() from the 'stats' package (plus some extra
        parameters). mcancor() is appropriate for analyzing data from
        three or more domains. See
        <https://sigg-iten.ch/learningbits/2014/01/20/canonical-correlation-analysis-under-constraints/>
        and Sigg et al. (2007) <doi:10.1109/MLSP.2007.4414315> for more
        details.