<?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>Adaptive Bootstrap Inference for Mediation Analysis with
Enhanced Statistical Power</dc:title>
  <dc:title>R package abima version 1.1</dc:title>
  <dc:description>Assess whether and how a specific continuous or categorical exposure affects the outcome of interest through one- or multi-dimensional mediators using an  adaptive bootstrap (AB) approach. The AB method allows to make inference for composite null hypotheses of no mediation effect, providing valid type I error control and thus optimizes statistical power. For more technical details, refer to He, Song and Xu (2024) &lt;doi:10.1093/jrsssb/qkad129&gt;.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Imports: boot, stats</dc:relation>
  <dc:relation>Suggests: testthat (&gt;= 3.0.0)</dc:relation>
  <dc:creator>Canyi Chen &lt;cychen.stats@outlook.com&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Canyi Chen [aut, cre] (ORCID: &lt;https://orcid.org/0000-0002-0673-5812&gt;),
  Yinqiu He [aut],
  Gongjun Xu [aut],
  Peter X.-K. Song [aut, cph]</dc:contributor>
  <dc:rights>MIT + file LICENSE (https://CRAN.R-project.org/package=abima/LICENSE)</dc:rights>
  <dc:date>2024-10-25</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=abima</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.abima</dc:identifier>
</oai_dc:dc>
