Metadata-Version: 2.1
Name: util_rc
Version: 0.1.6
Summary: A PyPI package to model risky choice
Author: Donna Ma
Author-email: donna.ma@berkeley.edu
Description-Content-Type: text/markdown

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) <br>

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. <br>

To use the package,copy the following code into your terminal: <br>
```
>>> pip install util-rc==0.1.6 <br>
>>> python3 <br>
>>> from src.util_rc.main import util_rc <br>
>>> example = util_rc("E",[0,0,1],[10,10,10],[1,1,1],[20,30,40],[.6,.5,.4]) <br>
>>> example.params <br>
```


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