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Hi, I’m Jacob Smith

Computing Rank Dependent Utility in R: Getting More out with RDU_Data()

In a previous blog post I discussed how we can compute rank dependent utility in R. Though this function was quite exciting for myself as computing rank dependent utilty by hand is quite labor intensive, I saw the function as limiting in the sense that I was suppressing all the information generated in the process…

Computing Rank Dependent Utility in R

Introduction: What is Rank Dependent Utility? Rank dependent utility theory made its debut in John Quiggin’s 1982 paper “A Theory of Anticipated Utility” with the mission, similar to other papers at the time, to come up with a model which addressed the Allais Paradox which was a major problem for expected utility theory. what all…

Simple code for the probability weighting function according to prospect theory

Prospect theory made its debut back in 1979 and was one of the first major attempts to address empirical deviations from expected utility theory. One of the key ingredients in operationalizing prospect theory involve conversion of probabilities to “weighted probabilities”. It should be noted that while there are more advanced libraries which are designed to…

Coding the Cost Effectiveness Acceptability Curve (CEAC) in R

Introduction The cost effectiveness acceptability curve (CEAC) is a tool used to describe the output of a probabilistic sensitivity analysis conducted on a model used in economic evaluations of health technologies. It communicates the probability of cost effectiveness conditional on our willingness to pay threshold for each unit of effectiveness or QALY gained. while this…


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