_pkgdown.yml

## This file configures the building process for pkgdown::build_site()
template:
  params:
    noindex: true # tells search engines not to index the site

home:
  strip_header: false

development:
  mode: auto  # detect based on version number
  version_label: warning
  version_tooltip: "The full API is stable, but sometimes limited"

navbar:
  structure:
    left:
    - home
    - intro
    - reference
    - articles
    - news
    right: github

reference:
- title: Evaluate precision dosing
  desc: >
    All these functions need a model, see section 'Models'
- subtitle: Fit population data
  desc: >
    These functions perform posthoc and/or proseval on population data
  contents:
  - dataTibble
  - posthoc
  - proseval

- subtitle: Add predictions for the population
  desc: >
    These functions can be used to append predictions to each tdmorefit instance
    in the population
  contents:
  - predict.tdmorefit
  - logLik.tdmorefit
  - as.population
  - as.sample

- subtitle: Simulate precision dosing
  contents:
  - doseSimulation
  - findDose
  - findDoses

- title: Models
- subtitle: Tdmore models
  desc: >
    Parameter estimation and dose recommendation needs a model. The methods below
    allow you to construct a model using `nlmixr`, `RxODE`, or algebraic equations.
  contents:
  - tdmore
  - errorModel

- subtitle: Metadata
  desc: >
    Model metadata allows to intelligently guess certain default settings
  contents:
  - metadata
  - getDosingInterval
  - covariate
  - output
  - formulation
  - target
  - getMetadataByName
  - getMetadataByClass
  - observed_variables
  - getObservedVariables

- subtitle: Algebraic models
  desc: >
    Use an algebraic model in tdmore
  contents:
  - algebraic
  - tdmore.algebraic
  - linCmt
  - pk_
  - pk
  - pkmodel

- subtitle: Model-predictive control
  desc: >
    Replace empirical bayesian estimation with model-predictive control in a tdmore model
  contents:
  - mpc

- subtitle: Mixture models
  desc: >
    A mixture model combines two subpopulations with an a priori probability
    of belonging to either model A or model B
  contents:
  - tdmore_mixture
  - tdmore_mixture_covariates

- subtitle: Example models
  desc: >
    Example datasets, with their respective population PK model as
    estimated by nlmixr.
  contents:
  - theopp
  - theopp_nlmixr
  - pheno
  - pheno_nlmixr
  - getModel
  - defaultModel

- title: Plotting
  desc: >
    These functions can be used to plot more detail for predictions
  contents:
  - autoplot.tdmore
  - autoplot.tdmorefit
  - autolayer.recommendation
  - autoplot.tdmorefit_mixture
  - parameterPlot.tdmorefit
  - predictionLayer