Package: andorR 0.3.1

Angus R Cameron

andorR: Optimisation of the Analysis of AND-OR Decision Trees

A decision support tool to strategically prioritise evidence gathering in complex, hierarchical AND-OR decision trees. It is designed for situations with incomplete or uncertain information where the goal is to reach a confident conclusion as efficiently as possible (responding to the minimum number of questions, and only spending resources on generating improved evidence when it is of significant value to the final decision). The framework excels in complex analyses with multiple potential successful pathways to a conclusion ('OR' nodes). Key features include a dynamic influence index to guide users to the most impactful question, a system for propagating answers and semi-quantitative confidence scores (0-5) up the tree, and post-conclusion guidance to identify the best actions to increase the final confidence. These components are brought together in an interactive command-line workflow that guides the analysis from start to finish.

Authors:Angus R Cameron [aut, cre], EpiMundi [cph, fnd]

andorR_0.3.1.tar.gz
andorR_0.3.1.zip(r-4.7)andorR_0.3.1.zip(r-4.6)andorR_0.3.1.zip(r-4.5)
andorR_0.3.1.tgz(r-4.6-any)andorR_0.3.1.tgz(r-4.5-any)
andorR_0.3.1.tar.gz(r-4.7-any)andorR_0.3.1.tar.gz(r-4.6-any)
andorR_0.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
andorR/json (API)

# Install 'andorR' in R:
install.packages('andorR', repos = c('https://epimundi.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/epimundi/andorr/issues

Pkgdown/docs site:https://epimundi.github.io

Datasets:
  • ethical - Ethical investment decision tree for a fictional company - data frame format
  • ethical_nl - Ethical investment decision tree for a fictional company in hierarchical format

On CRAN:

Conda:

and-ordecision-treeoptimisation

5.24 score 560 downloads 15 exports 20 dependencies

Last updated from:a175d07ca7. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK130
source / vignettesOK176
linux-release-x86_64OK156
macos-release-arm64OK128
macos-oldrel-arm64OK134
windows-develOK85
windows-releaseOK83
windows-oldrelOK85
wasm-releaseOK110

Exports:andorR_interactivecalculate_treeget_confidence_boostersget_highest_influenceget_questionsload_tree_csvload_tree_csv_pathload_tree_dfload_tree_df_pathload_tree_jsonload_tree_node_listload_tree_yamlprint_treeset_answerupdate_tree

Dependencies:clicrayondata.treedplyrgenericsgluejsonlitelifecyclemagrittrpillarpkgconfigR6rlangstringitibbletidyselectutf8vctrswithryaml

Example Data Files
Included Files | File documentation | Ethical investment decision tree (ethical) | Introduction | Formats | Source | World Organisation for Animal Health notifiable disease list criteria (woah) | Format | Bovine Spongiform Encephalopathy surveillance decision tree (bse) | Sources | Multiple sclerosis diagnostic decision tree (ms) | Systemic Lupus Erythematous diagnostic decision tree (lupus) | UNESCO World Heritage nomination decision tree (unesco) | Example: Loading a File | CSV relational format | CSV node path format | YAML format
Rendered fromexample-data-files.Rmdusingknitr::rmarkdown

Last update: 2025-10-04
Started: 2025-09-19

Confidence Boosting and Sensitivity Analysis
Introduction: From Conclusion to Conviction | The Algorithm: A Sensitivity Analysis | A Worked Example: Strengthening a Conclusion | Step 1: Reach an Initial Conclusion | Step 2: View the Initial Conclusion | Step 3: Get Guidance on Boosting Confidence | Step 4: Act on the Guidance | Step 5: View the Improved Result
Rendered fromconfidence-boosting.Rmdusingknitr::rmarkdown

Last update: 2025-09-28
Started: 2025-09-20

Data Formats for andorR
1. The Hierarchical Format (YAML or JSON) | YAML format | JSON format | 2. The Relational (ID/Parent) Format | 3. The Path String Format | Summary of Formats
Rendered fromdata-formats.Rmdusingknitr::rmarkdown

Last update: 2025-09-28
Started: 2025-09-20

Introduction to andorR
What are AND-OR Trees? | Core Features of andorR | A Worked Example | Step 1: Load the Tree | Step 2: Get Initial Guidance | Step 3: Provide Initial Answers | Step 4: Follow the Guidance and Reach a Conclusion | Interactive Analysis with andorR_interactive() | Included Datasets
Rendered fromandorR-intro.Rmdusingknitr::rmarkdown

Last update: 2025-09-28
Started: 2025-09-19

Optimisation of AND-OR Decision Trees
The Need for Optimisation | The Influence Index Algorithm | 1. Parent Node Indices: true_index and false_index | 2. Leaf Node influence_index | 3. Prioritisation strategies in practice | A Worked Example | Step 1: Load and Prepare the Tree | Step 2: Calculate the Indices | Step 3: Display and Interpret the Results | Step 4: Observing Dynamic Re-Prioritization | Analysis of the Change | Next Steps: Boosting Confidence
Rendered fromtree-optimisation.Rmdusingknitr::rmarkdown

Last update: 2025-09-28
Started: 2025-09-19