<?xml version="1.0" encoding="UTF-8"?>
<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>Unified Classical and Canonical Biplots</dc:title>
  <dc:title>R package BiGaRx version 0.5.1</dc:title>
  <dc:description>Classical and canonical biplots computed from a single singular
    value decomposition (SVD) engine. Includes the two biplots of Gabriel
    (1971) &lt;doi:10.1093/biomet/58.3.453&gt;: the GH-biplot, named after its row
    markers G and column markers H, which preserves the column metric
    (covariances and correlations between variables), and the JK-biplot, named
    after its row markers J and column markers K, which preserves the row
    metric (distances between individuals). Also includes the HJ-biplot of
    Galindo (1986)
    &lt;https://upcommons.upc.edu/entities/publication/e17797dd-e20b-4fe5-b34a-f4cde41695ed&gt;,
    which combines the row markers J and the column markers H so that rows
    and columns are both represented with maximum quality. Options include
    configurable preprocessing, supplementary rows and columns, quality of
    representation, contributions, goodness of fit, bootstrap confidence
    intervals following Nieto, Galindo, Leiva and Vicente-Galindo (2014)
    &lt;doi:10.15446/rce.v37n2spe.47944&gt;, and imputation of missing values by
    iterative low-rank approximation following Gabriel and Zamir (1979)
    &lt;doi:10.1080/00401706.1979.10489819&gt;. The canonical biplot, also known as
    the multivariate analysis of variance (MANOVA) biplot, follows Amaro,
    Vicente-Villardon and Galindo (2004)
    &lt;https://www.redalyc.org/pdf/339/33908807.pdf&gt;. It is available for one-way
    designs and for two-way designs with total, row, column and interaction
    biplots. It comes with multivariate tests, pairwise comparisons with
    Hotelling's T-squared statistic, log-ratio transformations for
    compositional data and confidence regions for the group means. The
    regions are circles or the ellipses of Krzanowski (1989)
    &lt;doi:10.1093/biomet/76.1.107&gt;, computed consistently in the scale of the
    within-groups covariance matrix. Plots use base graphics or 'ggplot2'.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Depends: R (&gt;= 3.6.0)</dc:relation>
  <dc:relation>Imports: stats, graphics, grDevices, utils</dc:relation>
  <dc:relation>Suggests: ggplot2, generics, MASS, testthat (&gt;= 3.0.0)</dc:relation>
  <dc:creator>Kleber Garzón &lt;garzon.kleber@gmail.com&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Kleber Garzón [aut, cre] (ORCID:
    &lt;https://orcid.org/0009-0009-4378-3002&gt;),
  Roxana Barrezueta [aut] (ORCID:
    &lt;https://orcid.org/0009-0007-1591-1121&gt;)</dc:contributor>
  <dc:rights>GPL (&gt;= 3)</dc:rights>
  <dc:date>2026-10-29</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=BiGaRx</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.BiGaRx</dc:identifier>
  <dc:language>es</dc:language>
</oai_dc:dc>
