We developed a negative binomial factor regression model to estimate structured (sparse) associations between a feature matrix X and overdispersed count data Y.  With 'nbfar', microbiome count data Y can be used, for example, to associate host or environmental covariates with microbial abundances. Currently, two models are available: a) Negative Binomial reduced rank regression (NB-RRR), b) Negative Binomial co-sparse factor regression (NB-FAR). Please refer the manuscript 'Mishra, A. K., & Müller, C. L. (2021). Negative Binomial factor regression with application to microbiome data analysis. bioRxiv.' for more details.

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.

Run simple direct gravitational N-body simulations. The package can access different external N-body simulators (e.g. GADGET-4 by Springel et al. (2021) <doi:10.48550/arXiv.2010.03567>), but also has a simple built-in simulator. This default simulator uses a variable block time step and lets the user choose between a range of integrators, including 4th and 6th order integrators for high-accuracy simulations. Basic top-hat smoothing is available as an option. The code also allows the definition of background particles that are fixed or in uniform motion, not subject to acceleration by other particles.