andorR0.3.1 package

Optimisation of the Analysis of AND-OR Decision Trees

andorR_interactive

Enter Interactive Analysis Mode

andorR-package

andorR: An Analysis and Optimisation Tool for AND-OR Decision Trees

assign_indices

Calculate Dynamic True/False Indices for a Parent Node

calculate_influence

Calculate the Influence Index for a Leaf Node

calculate_tree

Propagate Answers and Confidence Up the Tree

get_confidence_boosters

Find Actions to Most Effectively Boost Confidence

get_highest_influence

Identify the Most Influential Question(s)

get_questions

Get a Data Frame Summary of All Leaf Questions

load_tree_csv_path

Load a decision tree from a CSV file (Path String Format)

load_tree_csv

Load a decision tree from a CSV file (Relational Format)

load_tree_df_path

Build a decision tree from a path-string data frame

load_tree_df

Build a decision tree from a relational data frame

load_tree_json

Load a decision tree from a JSON file (Hierarchical Format)

load_tree_node_list

Build a decision tree from a hierarchical list

load_tree_yaml

Load a decision tree from a YAML file (Hierarchical Format)

print_tree

Print a Styled, Formatted Summary of the Decision Tree

set_answer

Set an Answer and Confidence for a Leaf Node

update_tree

Update a Tree Based on Answers Provided

validate_tree_df_path

Validate the structure of a path-string tree data frame

validate_tree_df

Validate the structure of a relational tree data frame

validate_tree_list

Validate the structure of a hierarchical tree list

Download source packageRead PDF manual

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.