Reinforcement Learning Tools for Two-Alternative Forced Choice Tasks

Documentation for package ‘binaryRL’ version 0.8.0

add_NA	Create NULL columns and the line 0
arrange_data	Arrange Data based on Block and Trial
check_dependency	Check Package Dependencies
decision_making	Markov Decision Process
digits	Round Digital
fit_p	Fit parameters
func_epsilon	Epsilon Greedy
func_eta	Learning Rate
func_gamma	Utility Function
func_tau	Soft-Max Function
Mason_2024_Exp1	Experiment 1 from Mason et al. (2024)
Mason_2024_Exp2	Experiment 2 from Mason et al. (2024)
mode	Pretend to be Raw Data
model_fit	Calculate the Model Fit
optimize_para	Fit Parameters
output	Summary the Results
rcv_d	Parameter and Model Recovery
recovery_data	recovery_d
rev_e	Review Experimental Effect
RSTD	RSTD model for fit
run_m	Building Reinforcement Learning Model
set_initial_value	Set initial values for all options
simulate_list	simulate_l
summary.binaryRL	summary
TD	TD model for fit
unique_choice	Figure out how many options exist
Utility	Utility model for fit