_pkgdown.yml

templates:
  params:
    bootswatch: readable

navbar:
  title: "missSBM"
  type: inverse
  left:
    - icon: fa-home
    - text: "Home"
      href: index.html
    - text: "Reference"
      href: "reference/index.html"
    - text: "Articles"
      menu:
      - text: "A case study on the war networks"
        href: articles/case_study_war_networks.html
    - text: "Changelog"
      href: news/index.html
  right:
    - icon: fa-github
    - text: github
      href: https://github.com/jchiquet/missSBM

reference:
- title: 'Top-level fitting functions'
  desc: >
    Main functions for estimating and sampling from an SBM with missing data
  contents:
    - '`estimateMissSBM`'
    - '`observeNetwork`'
- title: 'Main classes of objects'
  desc: >
    Description of objects missSBM_fit and missSBM_collection. The class missSBM_fit
    is the more central class of object, embedding fits for both the SBM and the sampling model.
    The class missSBM_collection defines objects for storing a collection of missSBM_fit,
    resulting from the the top-level function estimateMissSBM().
  contents:
    - '`missSBM_fit`'
    - '`coef.missSBM_fit`'
    - '`fitted.missSBM_fit`'
    - '`predict.missSBM_fit`'
    - '`plot.missSBM_fit`'
    - '`missSBM_collection`'
- title: Data sets
  desc: ~
  contents:
    - '`war`'
    - '`frenchblog2007`'
    - '`er_network`'
- title: internal
  desc: ~
  contents:
    - '`SimpleSBM_fit`'
    - '`SimpleSBM_fit_MNAR`'
    - '`SimpleSBM_fit_noCov`'
    - '`SimpleSBM_fit_withCov`'
    - '`blockDyadSampler`'
    - '`blockDyadSampling_fit`'
    - '`blockNodeSampler`'
    - '`blockNodeSampling_fit`'
    - '`covarDyadSampling_fit`'
    - '`covarNodeSampling_fit`'
    - '`degreeSampler`'
    - '`degreeSampling_fit`'
    - '`doubleStandardSampler`'
    - '`doubleStandardSampling_fit`'
    - '`dyadSampler`'
    - '`dyadSampling_fit`'
    - '`missSBM`'
    - '`networkSampler`'
    - '`networkSampling`'
    - '`networkSamplingDyads_fit`'
    - '`networkSamplingNodes_fit`'
    - '`nodeSampler`'
    - '`nodeSampling_fit`'
    - '`partlyObservedNetwork`'
    - '`simpleDyadSampler`'
    - '`simpleNodeSampler`'
    - '`snowballSampler`'
    - '`summary.missSBM_fit`'
    - '`l1_similarity`'