Provides tools for processing and analyzing data from the 'O-GlcNAcAtlas' database <https://oglcnac.org/>, 
    as described in Ma (2021) <doi:10.1093/glycob/cwab003>. It integrates 'UniProt' <https://www.uniprot.org/> API calls 
    to retrieve additional information. It is specifically designed for research workflows 
    involving 'O-GlcNAcAtlas' data, providing a flexible and user-friendly interface for customizing 
    and downloading processed results. Interactive elements allow users to easily adjust parameters 
    and handle various biological datasets.

Multiple tools are now available for inferring the personalised 
    germ line set from an adaptive immune receptor repertoire. 
    Output from these tools is converted to 
    a single format and supplemented with rich data such as usage and 
    characterisation of 'novel' germ line alleles. This data can be 
    particularly useful when considering the validity of novel inferences. Use
    of the analysis provided is described in <doi:10.3389/fimmu.2019.00435>.

Extract results into R from the Observational Health Data Sciences and Informatics result database (see <https://ohdsi.github.io/Strategus/results-schema/index.html>) and generate reports/presentations via 'quarto' that summarize results in HTML format. Learn more about 'OhdsiReportGenerator' at <https://ohdsi.github.io/OhdsiReportGenerator/>.

Users can build a single 'shiny' app for exploring population characterization, population-level causal effect estimation, and patient-level prediction results generated via the R analyses packages in 'HADES' (see <https://ohdsi.github.io/Hades/>). Learn more about 'OhdsiShinyAppBuilder' at <https://ohdsi.github.io/OhdsiShinyAppBuilder/>.

Supports the modeling of ordinal random variables, 
    like the outcomes of races, via Softmax regression,
    under the Harville <doi:10.1080/01621459.1973.10482425> and
    Henery <doi:10.1111/j.2517-6161.1981.tb01153.x> models.

Ing and Lai (2011) <doi:10.5705/ss.2010.081> proposed a high-dimensional model selection procedure that comprises three steps: orthogonal greedy algorithm (OGA), high-dimensional information criterion (HDIC), and Trim. The first two steps, OGA and HDIC, are used to sequentially select input variables and determine stopping rules, respectively. The third step, Trim, is used to delete irrelevant variables remaining in the second step. This package aims at fitting a high-dimensional linear regression model via OGA+HDIC+Trim.