Package index

Example Datasets

Mason_2024_G1

Group 1 from Mason et al. (2024)

Mason_2024_G2

Group 2 from Mason et al. (2024)

Steps

run_m()

Step 1: Building reinforcement learning model

rcv_d()

Step 2: Generating fake data for parameter and model recovery

fit_p()

Step 3: Optimizing parameters to fit real data

rpl_e()

Step 4: Replaying the experiment with optimal parameters

Models

TD()

Model: TD

RSTD()

Model: RSTD

Utility()

Model: Utility

Functions

func_gamma()

Function: Utility Function

func_eta()

Function: Learning Rate

func_epsilon()

Function: Epsilon Related

func_pi()

Function: Upper-Confidence-Bound

func_tau()

Function: Soft-Max Function

func_logl()

Function: Loss Function

Processes

optimize_para()

Process: Optimizing Parameters

simulate_list()

Process: Simulating Fake Data

recovery_data()

Process: Recovering Fake Data

Summary

summary(<binaryRL>)

S3method summary