Estimate a suite of normalizing transformations, including 
    a new adaptation of a technique based on ranks which can guarantee 
    normally distributed transformed data if there are no ties: ordered 
    quantile normalization (ORQ). ORQ normalization combines a rank-mapping
    approach with a shifted logit approximation that allows
    the transformation to work on data outside the original domain. It is 
    also able to handle new data within the original domain via linear 
    interpolation. The package is built to estimate the best normalizing 
    transformation for a vector consistently and accurately. It implements 
    the Box-Cox transformation, the Yeo-Johnson transformation, three types 
    of Lambert WxF transformations, and the ordered quantile normalization 
    transformation. It estimates the normalization efficacy of other
    commonly used transformations, and it allows users to specify 
    custom transformations or normalization statistics. Finally, functionality
    can be integrated into a machine learning workflow via recipes.

Decision tree algorithm with a major feature added. Allows for users to define an ordering on the partitioning process.
    Resulting in Branch-Exclusive Splits Trees (BEST). Cedric Beaulac and Jeffrey S. Rosentahl (2019) <arXiv:1804.10168>.

Provides a practical tool for estimating the burden of common communicable diseases in settlements of displaced populations. An online version of the tool can be found at <http://who-refugee-bod.ecdf.ed.ac.uk/shiny/app/>. Estimates of burden of disease aim to synthesize data about cause-specific morbidity and mortality through a systematic approach that enables evidence-based decisions and comparisons across settings. The focus of this tool is on four acute communicable diseases and syndromes, including Acute respiratory infections, Acute diarrheal diseases, Acute jaundice syndrome and Acute febrile illnesses.

Nonparametric detection of nonuniformity and dependence with Binary Expansion Testing (BET). See Kai Zhang (2019) BET on Independence, Journal of the American Statistical Association, 114:528, 1620-1637, <DOI:10.1080/01621459.2018.1537921>, Kai Zhang, Wan Zhang, Zhigen Zhao, Wen Zhou. (2023). BEAUTY Powered BEAST, <doi:10.48550/arXiv.2103.00674>  and Wan Zhang, Zhigen Zhao, Michael Baiocchi, Yao Li, Kai Zhang. (2023) SorBET: A Fast and Powerful Algorithm to Test Dependence of Variables, Techinical report.