AMR/_pkgdown.yml

# ==================================================================== #
# TITLE                                                                #
# AMR: An R Package for Working with Antimicrobial Resistance Data     #
#                                                                      #
# SOURCE                                                               #
# https://github.com/msberends/AMR                                     #
#                                                                      #
# CITE AS                                                              #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
# Data. Journal of Statistical Software, 104(3), 1-31.                 #
# doi:10.18637/jss.v104.i03                                            #
#                                                                      #
# Developed at the University of Groningen, the Netherlands, in        #
# collaboration with non-profit organisations Certe Medical            #
# Diagnostics & Advice, and University Medical Center Groningen.       # 
#                                                                      #
# This R package is free software; you can freely use and distribute   #
# it for both personal and commercial purposes under the terms of the  #
# GNU General Public License version 2.0 (GNU GPL-2), as published by  #
# the Free Software Foundation.                                        #
# We created this package for both routine data analysis and academic  #
# research and it was publicly released in the hope that it will be    #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
#                                                                      #
# Visit our website for the full manual and a complete tutorial about  #
# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
# ==================================================================== #

title: "AMR (for R)"
url: "https://msberends.github.io/AMR/"

template:
  bootstrap: 5
  bootswatch: "flatly"
  assets: "pkgdown/logos" # use logos in this folder
  bslib:
    code_font: {google: "Fira Code"}
    # body-text-align: "justify"
    line-height-base: 1.75
    # the green "success" colour of this bootstrap theme should be the same as the green in our logo
    success: "#128f76"
    link-color: "#128f76"
    navbar-padding-y: "0.5rem"
  opengraph:
    twitter:
      creator: "@msberends"
      card: summary_large_image

news:
  one_page: true
  cran_dates: true

footer:
  structure:
    left: [devtext]
    right: [logo]
  components:
    devtext: '<code>AMR</code> (for R). Developed at the <a target="_blank" href="https://www.rug.nl">University of Groningen</a> in collaboration with non-profit organisations<br><a target="_blank" href="https://www.certe.nl">Certe Medical Diagnostics and Advice Foundation</a> and <a target="_blank" href="https://www.umcg.nl">University Medical Center Groningen</a>.'
    logo: '<a target="_blank" href="https://www.rug.nl"><img src="https://github.com/msberends/AMR/raw/main/pkgdown/logos/logo_rug.svg" style="max-width: 150px;"></a>'

home:
  sidebar:
    structure: [toc, links, authors, citation]

navbar:
  title: "AMR (for R)"
  left:
    - text: "Home"
      icon: "fa-home"
      href: "index.html"
    - text: "How to"
      icon: "fa-question-circle"
      menu:
      - text: "Conduct AMR analysis"
        icon: "fa-directions"
        href: "articles/AMR.html"
      - text: "Predict antimicrobial resistance"
        icon: "fa-dice"
        href: "articles/resistance_predict.html"
      - text: "Data sets for download / own use"
        icon: "fa-database"
        href: "articles/datasets.html"
      - text: "Conduct principal component analysis for AMR"
        icon: "fa-compress"
        href: "articles/PCA.html"
      - text: "Determine multi-drug resistance (MDR)"
        icon: "fa-skull-crossbones"
        href: "articles/MDR.html"
      - text: "Work with WHONET data"
        icon: "fa-globe-americas"
        href: "articles/WHONET.html"
      - text: "Import data from SPSS/SAS/Stata"
        icon: "fa-file-upload"
        href: "articles/SPSS.html"
      - text: "Apply EUCAST rules"
        icon: "fa-exchange-alt"
        href: "articles/EUCAST.html"
      - text: "Get properties of a microorganism"
        icon: "fa-bug"
        href: "reference/mo_property.html" # reference instead of an article
      - text: "Get properties of an antibiotic"
        icon: "fa-capsules"
        href: "reference/ab_property.html" # reference instead of an article
      # - text: "Other: benchmarks"
      #   icon: "fa-shipping-fast"
      #   href: "articles/benchmarks.html"
    - text: "Manual"
      icon: "fa-book-open"
      href: "reference/index.html"
    - text: "Authors"
      icon: "fa-users"
      href: "authors.html"
    - text: "Changelog"
      icon: "far fa-newspaper"
      href: "news/index.html"
  right:
    - text: "Source Code"
      icon: "fab fa-github"
      href: "https://github.com/msberends/AMR"
    # - text: "Survey"
    #   icon: "fa-clipboard-list"
    #   href: "survey.html"

reference:
  - title: "Preparing data: microorganisms"
    desc: >
      These functions are meant to get taxonomically valid properties of microorganisms from any input.
      Use `mo_source()` to teach this package how to translate your own codes to valid microorganism codes.
    contents:
    - "`as.mo`"
    - "`mo_property`"
    - "`mo_source`"

  - title: "Preparing data: antibiotics"
    desc: >
      Use these functions to get valid properties of antibiotics from any input or to clean your input.
      You can even retrieve drug names and doses from clinical text records, using `ab_from_text()`.
    contents:
    - "`as.ab`"
    - "`ab_property`"
    - "`ab_from_text`"
    - "`atc_online_property`"
    - "`add_custom_antimicrobials`"

  - title: "Preparing data: antimicrobial resistance"
    desc: >
      With `as.mic()` and `as.disk()` you can transform your raw input to valid MIC or disk diffusion values.
      Use `as.rsi()` for cleaning raw data to let it only contain "R", "I" and "S", or to interpret MIC or disk diffusion values as R/SI based on the lastest EUCAST and CLSI guidelines.
      Afterwards, you can extend antibiotic interpretations by applying [EUCAST rules](https://www.eucast.org/expert_rules_and_intrinsic_resistance/) with `eucast_rules()`.
    contents:
    - "`as.rsi`"
    - "`as.mic`"
    - "`as.disk`"
    - "`eucast_rules`"
    - "`custom_eucast_rules`"

  - title: "Analysing data: antimicrobial resistance"
    desc: >
      Use these function for the analysis part. You can use `susceptibility()` or `resistance()` on any antibiotic column.
      Be sure to first select the isolates that are appropiate for analysis, by using `first_isolate()` or `is_new_episode()`.
      You can also filter your data on certain resistance in certain antibiotic classes (`carbapenems()`, `aminoglycosides()`), or determine multi-drug resistant microorganisms (MDRO, `mdro()`).
    contents:
    - "`proportion`"
    - "`count`"
    - "`is_new_episode`"
    - "`first_isolate`"
    - "`key_antimicrobials`"
    - "`mdro`"
    - "`count`"
    - "`plot`"
    - "`ggplot_rsi`"
    - "`bug_drug_combinations`"
    - "`antibiotic_class_selectors`"
    - "`mean_amr_distance`"
    - "`resistance_predict`"
    - "`guess_ab_col`"

  - title: "Background information on included data"
    desc: >
      Some pages about our package and its external sources. Be sure to read our [How To's](./../articles/index.html)
      for more information about how to work with functions in this package.
    contents:
    - "`AMR`"
    - "`example_isolates`"
    - "`microorganisms`"
    - "`microorganisms.codes`"
    - "`antibiotics`"
    - "`intrinsic_resistant`"
    - "`dosage`"
    - "`WHOCC`"
    - "`example_isolates_unclean`"
    - "`rsi_translation`"
    - "`WHONET`"

  - title: "Other: miscellaneous functions"
    desc: >
      These functions are mostly for internal use, but some of
      them may also be suitable for your analysis. Especially the
      'like' function can be useful: `if (x %like% y) {...}`.
    contents:
    - "`age_groups`"
    - "`age`"
    - "`availability`"
    - "`get_AMR_locale`"
    - "`ggplot_pca`"
    - "`italicise_taxonomy`"
    - "`join`"
    - "`like`"
    - "`mo_matching_score`"
    - "`pca`"
    - "`random`"

  - title: "Other: statistical tests"
    desc: >
      Some statistical tests or methods are not part of base R and were added to this package for convenience.
    contents:
    - "`g.test`"
    - "`kurtosis`"
    - "`skewness`"

  - title: "Other: deprecated functions"
    desc: >
      These functions are deprecated, meaning that they will still
      work but show a warning with every use and will be removed
      in a future version.
    contents:
    - "`AMR-deprecated`"