Catalogue of NBER working papers published between June 1973 and December 2021.

An extension of the Beta Kernel Process model designed to handle negative binomial responses for count data modeling, building upon Zhao, Qing and Xu (2025) <doi:10.48550/arXiv.2508.10447>.

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.

Conduct inference on the sample average treatment effect for a 
    matched (observational) dataset with a continuous treatment. Equipped with
    calipered non-bipartite matching, bias-corrected sample average treatment
    effect estimation, and covariate-adjusted variance estimation. Matching, 
    estimation, and inference methods are described in 
    Frazier, Heng and Zhou (2024) <doi:10.48550/arXiv.2409.11701>.