_pkgdown.yml

url: https://statfungen.github.io/qQTLR

template:
 bootstrap: 5
 light-switch: true
 math-rendering: katex
 bslib:
  base_font: { google: "Roboto" }
  heading_font: { google: "Roboto Slab" }
  code_font: { google: "JetBrains Mono" }

development:
 mode: auto

home:
 links:
 - text: Learn more
   href: https://github.com/StatFunGen/qQTLR
 - text: Report a bug
   href: https://github.com/StatFunGen/qQTLR/issues

navbar:
 structure:
  left: [intro, reference, articles]
  right: [search, github, lightswitch]
 components:
  intro:
   icon: fa fa-play-circle
   text: Get Started
   href: articles/basic-qqtl-analysis.html
   aria-label: Get started with qQTLR
  reference:
   icon: fa fa-file-code
   text: Functions
   href: reference/index.html
   aria-label: Function reference
  articles:
   icon: fa fa-book-reader
   text: Vignettes
   href: articles/index.html
   aria-label: Articles and vignettes
  github:
   icon: fab fa-github
   href: https://github.com/StatFunGen/qQTLR
   aria-label: View source on GitHub

articles:
  - title: "Getting Started"
    navbar: ~
    contents:
    - basic-qqtl-analysis
    - interpreting-qqtl-results
  - title: "Relationship to Other Non-linear QTL Approaches"
    desc: >
      How qQTL relates to variance QTL (vQTL) and interaction QTL (iQTL).
    contents:
    - vqtl-special-case
    - iqtl-simulation
  - title: "qTWAS Weight Training"
    desc: >
      Training quantile-based TWAS (qTWAS) prediction weights in qQTLR.
      Weight application to GWAS is performed via the FunGen-xQTL
      pipeline referenced in the manuscript notes.
    contents:
    - qtwas-pipeline
  - title: "Manuscript Pipelines"
    desc: >
      Pointers to the FunGen-xQTL protocol pipelines used in
      Liu et al. 2026+ for qQTL calling, hierarchical multiple testing,
      EOO and sLDSC enrichment, and cTWAS association testing.
    contents:
    - manuscript-pipeline-notes

footer:
 structure:
  left: developed_by
  right: built_with