4 Parameter Model, without C (choice perseveration) but with xi (noise). Single learning rate both for R and P.
5 Parameter Model, without C (choice perseveration) but with xi (noise)
Compute Highest-Density Interval
Rescorla-Wagner (Delta) Model
Rescorla-Wagner (Delta) Model
5 Parameter Model, without C (choice perseveration) but with xi (noise). Added decay rate (Niv et al., 2015, J. Neuro).
3 Parameter Model, without C (choice perseveration), R (reward sensitivity), and P (punishment sensitivity). But with xi (noise)
Rescorla-Wagner (Gamma) Model
bandit4arm2_kalman_filter
Kalman Filter
4 Parameter Model, without C (choice perseveration)
5 Parameter Model, without C (choice perseveration) but with xi (noise)
Kalman Filter
4 Parameter Model, without C (choice perseveration) but with xi (noise). Single learning rate both for R and P.
Cumulative Model
5 Parameter Model, without C (choice perseveration) but with xi (noise). Added decay rate (Niv et al., 2015, J. Neuro).
Exponential-Weight Mean-Variance Model
Rescorla-Wagner (Delta) Model
Re-parameterized version of BART model with 4 parameters
4 Parameter Model, without C (choice perseveration)
3 Parameter Model, without C (choice perseveration), R (reward sensitivity), and P (punishment sensitivity). But with xi (noise)
Drift Diffusion Model
Exponential Model
Probability Weight Function
Exponential Subjective Value Model
Constant-Sensitivity (CS) Model
Drift Diffusion Model
Linear Subjective Value Model
Choice Reaction Time task, linear ballistic accumulator modeling
Choice Reaction Time task, linear ballistic accumulator modeling
Constant-Sensitivity (CS) Model
Hyperbolic Model
Hyperbolic Model
Hierarchical Bayesian Modeling of Decision-Making Tasks
RW + noise
RW + noise + bias
Function to estimate mode of MCMC samples
Extract Model Comparison Estimates
hBayesDM Model Base Function
RW (rew/pun) + noise + bias + pi
RW + noise + bias + pi
Function to plot multiple figures
General Purpose Plotting for hBayesDM. This function plots hyper parameters.
Plots individual posterior distributions, using the stan_plot function of the rstan package
Other-Conferred Utility (OCU) Model
Value-Plus-Perseverance
Plots highest density interval (HDI) from (MCMC) samples and prints HDI in the R console.
HDI is indicated by a red line.
Based on John Kruschke's codes.
Plots the histogram of MCMC samples.
Outcome-Representation Learning Model
Prospect Valence Learning (PVL) Delta
Prospect Valence Learning (PVL) Decay-RI
Print model-fits (mean LOOIC or WAIC values in addition to Akaike weights) of hBayesDM Models
Experience-Weighted Attraction Model
Reward-Punishment Model
Fictitious Update Model
Reward-Punishment Model
Fictitious Update Model
Fictitious Update Model, without alpha (indecision point)
Drift Diffusion Model
Fictitious Update Model, with separate learning rates for positive and negative prediction error (PE)
Fictitious Update Model, with separate learning rates for positive and negative prediction error (PE), without alpha (indecision point)
Prospect Theory, without risk aversion (RA) parameter
Signal detection theory model
Prospect Theory, without loss aversion (LA) parameter
Prospect Theory
Q Learning Model
Reinforcement Learning Drift Diffusion Model 1
Gain-Loss Q Learning Model
Function for extracting Rhat values from an hBayesDM object
Reinforcement Learning Drift Diffusion Model 6
Happiness Computational Model
Hybrid Model, with 4 parameters
Hybrid Model, with 7 parameters (original model)
Ideal Observer Model
Sequential Learning Model
Rescorla-Wagner (Delta) Model
Hybrid Model, with 6 parameters