_pkgdown.yml

url: https://dmphillippo.github.io/multinma


home:
  title: Bayesian network meta-analysis of individual and aggregate data
  description: >
    multinma is an R package for network meta-analysis and (multilevel) network
    meta-regression of aggregate data, individual patient data, and mixtures of
    both. Models are estimated in a Bayesian framework using Stan.


template:
  bootstrap: 5
  bslib:
    base_font: {google: "Source Sans Pro"}
    heading_font: {google: "Source Sans Pro"}
    #code_font: {google: "JetBrains Mono"}
  includes:
    after_body: >
      <script data-goatcounter="https://multinma.goatcounter.com/count" async src="//gc.zgo.at/count.js"></script>


development:
  mode: auto


articles:
- title: Overview of Examples
  navbar: ~
  contents:
  - vignette_overview

- title: Examples
  navbar: Examples
  contents: starts_with("example_")


reference:
- title: Package overview
  contents:
  - multinma-package

- title: Defining a network
  desc: Setting up a network from different data sources, creating network plots.
  contents:
  - starts_with("set_")
  - combine_network
  - multi
  - print.nma_data
  - plot.nma_data
  - as.igraph.nma_data
  - nma_data-class
  - is_network_connected

- title: Setting up numerical integration (ML-NMR only)
  desc: >
    Multilevel network meta-regression models require numerical integration
    points to be specified for the distributions of covariates in each aggregate
    data study in the network.
  contents:
  - add_integration
  - unnest_integration
  - distr
  - matches("[pdq]bern")
  - matches("[pdq]gamma")
  - matches("[pdq]gent")
  - matches("[pdq]logt")
  - matches("[pdq]logitnorm")

- title: Prior distributions
  desc: Specify and summarise prior distributions.
  contents:
  - priors
  - summary.nma_prior
  - nma_prior-class
  - plot_prior_posterior
  - matches("[pdq]gent")
  - matches("[pdq]logt")

- title: Model fitting
  desc: Model specification and fitting is accomplished using the `nma()` function.
  contents:
  - nma
  - print.stan_nma
  - summary.stan_nma
  - plot.stan_nma
  - pairs.stan_nma
  - stan_nma-class
  - adapt_delta
  - which_RE
  - RE_cor
  - .default

- title: Model checking and comparison
  desc: Checking model fit and comparing models.
  contents:
  - plot_prior_posterior
  - plot_integration_error
  - dic
  - print.nma_dic
  - plot.nma_dic
  - nma_dic-class
  - loo
  - waic

- title: Node-splitting
  desc: Generate and summarise node-splitting models for assessing inconsistency.
  contents:
  - has_direct
  - has_indirect
  - get_nodesplits
  - nma_nodesplit-class
  - print.nma_nodesplit_df
  - summary.nma_nodesplit_df
  - nodesplit_summary-class
  - print.nodesplit_summary
  - plot.nodesplit_summary

- title: Posterior summaries and working with fitted models
  desc: >
    Producing and plotting relative effects, absolute predictions, marginal
    effects, posterior ranks and rank probabilities. Converting to MCMC arrays
    and matrices.
  contents:
  - relative_effects
  - marginal_effects
  - predict.stan_nma
  - posterior_ranks
  - posterior_rank_probs
  - print.nma_summary
  - plot.nma_summary
  - nma_summary-class
  - as.array.stan_nma
  - mcmc_array-class
  - as.matrix.stan_nma
  - as.stanfit.stan_nma

- title: M-spline hazards
  desc: Functions for flexibile M-splines on the baseline hazard.
  contents:
  - matches("[pdqhH]mspline")
  - rmst_mspline
  - make_knots

- title: ggplot functions
  desc: Functions for creating or customising ggplot outputs.
  contents:
  - theme_multinma
  - geom_km

- title: Datasets
  desc: Datasets used for examples and vignettes.
  contents:
  - has_keyword("datasets")
  - has_keyword("examples")