Introduces a 'Copilot'-like completion experience, but it knows how
    to talk to the objects in your R environment. 'ellmer' chats are integrated 
    directly into your 'RStudio' and 'Positron' sessions, automatically 
    incorporating relevant context from surrounding lines of code and your
    global environment (like data frame columns and types). Open the package 
    dialog box with a keyboard shortcut, type your request, and the assistant 
    will stream its response directly into your documents.

Generative Adversarial Networks are applied to generate generative data for a data source. A generative model consisting of a generator and a discriminator network is trained. During iterative training the distribution of generated data is converging to that of the data source. Direct applications of generative data are the created functions for data evaluation, missing data completion and data classification. A software service for accelerated training of generative models on graphics processing units is available. Reference: Goodfellow et al. (2014) <doi:10.48550/arXiv.1406.2661>.

A network-based gene weighting algorithm for pathway enrichment analysis, using either RNA-seq or microarray data. Zhaoyuan Fang, Weidong Tian and Hongbin Ji (2012) <doi:10.1038/cr.2011.149>.

This is a dataset package for GANPA, which implements a
        network-based gene weighting approach to pathway analysis. This
        package includes data useful for GANPA, such as a functional
        association network, pathways, an expression dataset and
        multi-subunit proteins.

Create Primavera-style interactive Gantt charts with Work Breakdown
    Structure (WBS) hierarchy and activities. Features include color-coded WBS
    items, indented labels, scrollable views for large projects, dynamic date
    formatting, and the ability to dim past activities. Built on top of 'plotly'
    for interactive visualizations.

As first reported [Zhao, J. H. 2007. "gap: Genetic Analysis Package". J Stat Soft 23(8):1-18.
        <doi:10.18637/jss.v023.i08>], it is designed as an integrated package for genetic data
        analysis of both population and family data. Currently, it contains functions for
        sample size calculations of both population-based and family-based designs, probability
        of familial disease aggregation, kinship calculation, statistics in linkage analysis,
        and association analysis involving genetic markers including haplotype analysis with or
        without environmental covariates. Over years, the package has been developed in-between
        many projects hence also in line with the name (gap).