A python package for binary risky choice modeling for 4 models:
    1. Expected Utility Theory: U = p * A^alpha
    2. Risk-Return: U = EV - b * Var
    3. Coefficient of Variation: U = EV - b *CV where CV = = sqrt(Var)/EV1
    4. Hyperbolic: U = A/(1+h*theta) where theta = (1-p)/p
where A is the payoff, p is the probability of winning that outcome, EV is Expected value (A*p), and Var is variance (P*(A-EV)**2 + (1 - P)*(-EV) ** 2)

The package takes risky choice data (probability, payoffs, and decisions) of 2 options as inputs and returns a util-rc object that stores the estimated parameters, inverse temperature, fit metrics, model type, and number of observations in an instance variable named params.

To use the package,
pip install util-rc

Dependencies: numpy version >= 1.26.4, scipy version >= 1.12.0