update readme

README.md

@@ -1,109 +1,19 @@
 # UtilityModels
 
-UtilityModels.jl is a collection of utility based decision models. Currently, expected utlity theory, transfer of attention exchange, and prospect theory are implemented. More models soon to follow. 
+UtilityModels.jl is a collection of utility based decision models. Currently, expected utlity theory, transfer of attention exchange, and prospect theory are implemented. More models soon to follow. See the [documentation](https://itsdfish.github.io/UtilityModels.jl/dev/) for more details.
 
-# Installation
-
-In the REPL, enter `]` to activate package mode, then type
-
-````julia 
-add UtilityModels
-````
-# Help
-
-In the REPL, enter `?` to activate help mode, then type the name of the function or object, such as:
-
-````julia
-TAX
-````
-
-# Examples
-
-## Expected Utility Theory
-````julia
-using UtilityModels
-
-α = .8
-model = ExpectedUtility(α)
-p = [.3,.2,.3,.2]
-v = [10.0,3.0,-2.0,-1.0]
-gamble = Gamble(;p, v)
-````
-### Expected Utility
-
-````julia
-mean(model, gamble)
-1.65219
-````
-
-### Standard Deviation of Utility
-
-````julia
-std(model, gamble)
-3.38863
-````
-
-## Transfer of Attention Exchange
+# Quick Example
 
 ````julia
 using UtilityModels
 # TAX with default values
 model = TAX()
 p = [.25,.25,.50]
 v = [100.0,0.0,-50.0]
-gamble = Gamble(;p, v)
-````
-### Expected Utility
-
-````julia
+gamble = Gamble(; p, v)
+# expected utility
 mean(model, gamble)
--15.51253
-````
-
-*References*
-
-1. Birnbaum, M. H., & Chavez, A. (1997). Tests of theories of decision making: Violations of branch independence and distribution independence. Organizational Behavior and human decision Processes, 71(2), 161-194.
-2. Birnbaum, M. H. (2008). New paradoxes of risky decision making. Psychological review, 115(2), 463.
-
-## Prospect Theory
-````julia
-using UtilityModels
-
-α = .8; γg = .6; λ = 2.25
-# By default, α=β  and γg = γl
-model = ProspectTheory(;α, γg, λ)
-p = [.3,.2,.3,.2]
-v = [10.0,3.0,-2.0,-1.0]
-gamble = Gamble(;p, v)
 ````
-### Expected Utility
-
 ````julia
-mean(model, gamble)
-0.77268
-````
-
-### Standard Deviation of Utility
-
-````julia
-std(model, gamble)
-3.7516
+-15.51253
 ````
-
-*References*
-
-1. Fennema, H., & Wakker, P. (1997). Original and cumulative prospect theory: A discussion of empirical differences. Journal of Behavioral Decision Making, 10(1), 53-64.
-
-2. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and uncertainty, 5(4), 297-323.
-
-### Valence Expectancy
-
-```julia 
-using UtilityModels
-
-parms = (n_options=2, Δ=.3, α=.5, λ=1.5, c=.5)
-gambles = [Gamble(;p=[.5,.5],v=[4.0,-1.0]),Gamble(;p=[.3,.7],v=[2.0,0.0])]
-model = ValenceExpectancy(;parms...)
-choices,outcomes = rand(model, gambles, 100)
-logpdf(model, choices, outcomes)
-```