For instructions, check <https://github.com/Hzhang-ouce/ARTofR>. This is a wrapper of 'bannerCommenter', for inserting neat comments, headers and dividers.

Reads and writes clinical-trial datasets losslessly across
    'SAS' XPORT (XPT), Clinical Data Interchange Standards Consortium
    (CDISC) Dataset-JSON, and 'Apache Parquet', applying a specification to
    produce submission-ready Study Data Tabulation Model (SDTM) and
    Analysis Data Model (ADaM) datasets. A single canonical metadata model
    carries labels, CDISC data types, lengths, 'SAS' display formats,
    controlled-terminology references, and sort keys identically across
    every format, so conversion between any two formats is lossless by
    construction. Pure 'R' and lightweight, with no external 'SAS' or
    'Java' runtime. Implements the published format specifications for
    CDISC Dataset-JSON
    (<https://cdisc-org.github.io/DataExchange-DatasetJson/doc/dataset-json1-1.html>)
    and 'SAS' XPORT
    (<https://www.loc.gov/preservation/digital/formats/fdd/fdd000466.shtml>).

The aligned rank transform for nonparametric
    factorial ANOVAs as described by Wobbrock,
    Findlater, Gergle, and Higgins (2011) <doi:10.1145/1978942.1978963>. 
    Also supports aligned rank transform contrasts as described
    by Elkin, Kay, Higgins, and Wobbrock (2021)
    <doi:10.1145/3472749.3474784>.

Create data that displays generative art when mapped into a 'ggplot2' plot. Functionality includes specialized data frame creation for geometric shapes, tools that define artistic color palettes, tools for geometrically transforming data, and other miscellaneous tools that are helpful when using 'ggplot2' for generative art.

Provides algorithms for creating artworks in the 'ggplot2' language that incorporate some form of randomness.

Adaptive and Robust Transfer Learning (ART) is a flexible framework for transfer learning that integrates information from auxiliary data sources to improve model performance on primary tasks. It is designed to be robust against negative transfer by including the non-transfer model in the candidate pool, ensuring stable performance even when auxiliary datasets are less informative. See the paper, Wang, Wu, and Ye (2023) <doi:10.1002/sta4.582>.