Customized R Markdown templates for authoring articles
    for Journal of Data Science.

Generates interactive Jellyfish plots to visualize spatiotemporal tumor
    evolution by integrating sample and phylogenetic trees into a unified plot.
    This approach provides an intuitive way to analyze tumor heterogeneity and
    evolution over time and across anatomical locations. The Jellyfish plot
    visualization design was first introduced by Lahtinen, Lavikka, et al.
    (2023, <doi:10.1016/j.ccell.2023.04.017>).
    This package also supports visualizing ClonEvol results, a tool developed
    by Dang, et al. (2017, <doi:10.1093/annonc/mdx517>), for analyzing clonal
    evolution from multi-sample sequencing data. The 'clonevol' package is not
    available on CRAN but can be installed from its GitHub
    repository (<https://github.com/hdng/clonevol>).

Fast extrapolation of univariate and multivariate time features using K-Nearest Neighbors. Version 2.0 adds approximate nearest-neighbor search, optional GPU acceleration, probabilistic forecast distributions, conformal prediction intervals, and multi-scale sequence representations. The compact set of hyper-parameters is tuned via grid or random search.

Interact with the 'Entrez' API
    hosted by the National Center for Biotechnology Information (NCBI),
    <https://www.ncbi.nlm.nih.gov/books/NBK25499/>.
    This package is focused on working with sequence metadata and links.
    It handles pagination and compensates for some API limitations to simplify these tasks.
    API calls are printed to the console to highlight how high-level queries are translated
    into individual HTTP requests.

Manage project dependencies from your DESCRIPTION file. Create a reproducible virtual environment with minimal additional files in your project. Provides tools to add, remove, and update dependencies as well as install existing dependencies with a single function.

The goal of 'jetty' is to execute R functions and code
    snippets in an isolated R subprocess within a 'Docker' container
    and return the evaluated results to the local R session. 'jetty'
    can install necessary packages at runtime and seamlessly propagates
    errors and outputs from the 'Docker' subprocess back to the main
    session. 'jetty' is primarily designed for sandboxed testing and
    quick execution of example code.