Reference

Set-up and display decision tree

costeffectiveness_tree()

Constructor for a Cost-Effectiveness Tree Object

print(<costeffectiveness_tree>)

Print Method for Cost-Effectiveness Trees

Manipulate decision tree

assign_branch_vals()

Assign Branching Values to Decision Tree

assign_branch_values()

Wrapper to Assign Branching Values to Decision Tree

fill_in_missing_tree_probs()

Fill-in Missing Tree Probabilities

myPruneFun()

Prune Tree

Nodelabel()

Node label

get_start_state_proportions()

Get Start State Proportions

Calculate decision analysis outcomes

MonteCarlo_expectedValues()

Monte Carlo Forward Simulation of Decision Tree

calc_expectedValues()

Calculate Expected Values for Each Node of Decision Tree

calc_pathway_probs()

Calculate Total Pathway Probabilities of Decision Tree

calc_riskprofile()

Calculate Risk Profile

payoff()

Calculate Weighted Expectations

decision()

Calculate Optimal Decision

Sample from and transform distributions

sample_distributions()

Sample from Standard Distributions

get_sd_from_normalCI()

Get Standard Deviation from Normal Confidence Interval

MoM_beta()

Method of Moments Beta Distribution Parameter Transformation

MoM_gamma()

Method of Moments Gamma Distribution Parameter Transformation

rbeta_more_params()

rbeta_more_params

rgamma_more_params()

rgamma_more_params

rpert()

Sample from Beta-PERT Distribution

means_distributions()

Means values from distributions

Distributions on the decision tree

f_NodeUniform()

f_NodeUniform

meanNodeUniform()

Mean of a data.tree uniform node

sampleNode()

Sample a data.tree Node

sampleNodeUniform()

Sample a data.tree uniform node