Package: ViralEntropR
Title: A Computational Pipeline for Entropy-Informed Detection of
        Emerging Viral Variants
Version: 0.6.2
Authors@R: c(
              person("Vadim", "Tyuryaev", ,"vadim.tyuryaev@gmail.com", 
                      role = c("aut", "cre"), 
                      comment = c(ORCID = "0009-0008-1361-6265")),
              person("Jane", "Heffernan", , "jmheffer@yorku.ca", 
                      role = c("aut")),
              person("Hanna", "Jankowski", , "hkj@yorku.ca", 
                      role = c("aut"))
             )
Description: Implements an entropy-informed pipeline for detecting
        emerging variants in viral amino acid sequence data, extending
        prior clustering-based approaches including hemagglutinin
        clustering methods (Li et al., 2015)
        <doi:10.1142/9789814667944_0018>. Provides a fully vectorized
        FASTA preprocessing toolkit covering header parsing, two-pass
        date and country extraction, ambiguous-residue filtering, and
        integer encoding under a 25-symbol amino acid alphabet.
        Computes per-site Shannon entropy across user-defined
        cumulative, sliding, or disjoint temporal partitions and
        clusters per-site entropy values using Gaussian mixture models
        via 'mclust' (Scrucca et al., 2016) <doi:10.32614/RJ-2016-021>.
        Quantifies temporal distributional shifts between partitions
        using the Hellinger distance (van der Vaart, 1998)
        <doi:10.1017/CBO9780511802256>, and detects temporal change
        points non-parametrically using energy statistics (Matteson and
        James, 2014) <doi:10.1080/01621459.2013.849605> via 'ecp' or
        wild binary segmentation (Fryzlewicz, 2014)
        <doi:10.1214/14-AOS1245> via 'HDcpDetect'. Per-site amino-acid
        frequency tables and entropy trajectory plots characterize
        sequence composition and evolutionary dynamics across time. A
        configurable multi-variant simulation engine generates
        synthetic sequence time series with known ground truth for
        benchmarking detection pipelines. A curated dataset of
        SARS-CoV-2 Variants of Concern and Variants of Interest with
        associated lineage and surveillance metadata is included, along
        with a bundled National Center for Biotechnology Information
        (NCBI) Spike protein sample and vignettes demonstrating the
        full workflow.
License: MIT + file LICENSE
Language: en-GB
Date: 2026-05-07
URL: https://github.com/vadimtyuryaev/ViralEntropR,
        https://doi.org/10.5281/zenodo.19040165,
        https://vadimtyuryaev.github.io/ViralEntropR/
BugReports: https://github.com/vadimtyuryaev/ViralEntropR/issues
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.3
Imports: ggplot2 (>= 3.4.0), grDevices, HDcpDetect, ecp, kableExtra,
        lubridate, magrittr, mclust, rlang, stats, stringr, utils, zoo
Suggests: Biostrings, DT, dplyr, here, knitr, readxl, rmarkdown, R.rsp,
        testthat (>= 3.0.0)
Config/testthat/edition: 3
VignetteBuilder: knitr, R.rsp
Config/pak/sysreqs: cmake libfontconfig1-dev libfreetype6-dev
        libfribidi-dev make libharfbuzz-dev libicu-dev libpng-dev
        libuv1-dev libxml2-dev
Repository: https://vadimtyuryaev.r-universe.dev
Date/Publication: 2026-05-31 07:23:55 UTC
RemoteUrl: https://github.com/vadimtyuryaev/viralentropr
RemoteRef: HEAD
RemoteSha: 405acdac3ec36c078f7ca963dc047b1961de2674
NeedsCompilation: no
Packaged: 2026-10-04 07:32:08 UTC; root
Author: Vadim Tyuryaev [aut, cre] (ORCID:
    <https://orcid.org/0009-0008-1361-6265>),
  Jane Heffernan [aut],
  Hanna Jankowski [aut]
Maintainer: Vadim Tyuryaev <vadim.tyuryaev@gmail.com>
Depends: R (>= 3.5.0)