We proposed a package for the classification task which uses Negative Binomial distribution within Linear Discriminant Analysis (NBLDA). It is an extension of the 'PoiClaClu' package to Negative Binomial distribution. The classification algorithms are based on the papers Dong et al. (2016, ISSN: 1471-2105) and Witten, DM (2011, ISSN: 1932-6157) for NBLDA and PLDA, respectively. Although PLDA is a sparse algorithm and can be used for variable selection, the algorithm proposed by Dong et al. is not sparse. Therefore, it uses all variables in the classifier. Here, we extend Dong et al.'s algorithm to the sparse case by shrinking overdispersion towards 0 (Yu et al., 2013, ISSN: 1367-4803) and offset parameter towards 1 (as proposed by Witten DM, 2011). We support only the classification task with this version.

Allow users to obtain basketball statistics for 
    the Australian basketball league 'NBL'<https://nbl.com.au/>. 
    Stats include play-by-play, shooting locations, results and 
    box scores for teams and players.

Perform non-bipartite matching and matched randomization. A
    "bipartite" matching utilizes two separate groups, e.g. smokers being
    matched to nonsmokers or cases being matched to controls. A "non-bipartite"
    matching creates mates from one big group, e.g. 100 hospitals being
    randomized for a two-arm cluster randomized trial or 5000 children who
    have been exposed to various levels of secondhand smoke and are being
    paired to form a greater exposure vs. lesser exposure comparison. At the
    core of a non-bipartite matching is a N x N distance matrix for N potential
    mates. The distance between two units expresses a measure of similarity or
    quality as mates (the lower the better). The 'gendistance()' and
    'distancematrix()' functions assist in creating this. The 'nonbimatch()'
    function creates the matching that minimizes the total sum of distances
    between mates; hence, it is referred to as an "optimal" matching. The
    'assign.grp()' function aids in performing a matched randomization. Note
    bipartite matching can be performed using the prevent option in
    'gendistance()'.