Hierarchical Bayesian Modeling of Decision-Making Tasks

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Documentation for package ‘hBayesDM’ version 1.1.1

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hBayesDM-package Hierarchical Bayesian Modeling of Decision-Making Tasks
alt_delta Rescorla-Wagner (Delta) Model
alt_gamma Rescorla-Wagner (Gamma) Model
bandit2arm_delta Rescorla-Wagner (Delta) Model
bandit4arm2_kalman_filter Kalman Filter
bandit4arm_2par_lapse 3 Parameter Model, without C (choice perseveration), R (reward sensitivity), and P (punishment sensitivity). But with xi (noise)
bandit4arm_4par 4 Parameter Model, without C (choice perseveration)
bandit4arm_lapse 5 Parameter Model, without C (choice perseveration) but with xi (noise)
bandit4arm_lapse_decay 5 Parameter Model, without C (choice perseveration) but with xi (noise). Added decay rate (Niv et al., 2015, J. Neuro).
bandit4arm_singleA_lapse 4 Parameter Model, without C (choice perseveration) but with xi (noise). Single learning rate both for R and P.
bart_ewmv Exponential-Weight Mean-Variance Model
bart_par4 Re-parameterized version of BART model with 4 parameters
cgt_cm Cumulative Model
choiceRT_ddm Drift Diffusion Model
choiceRT_ddm_single Drift Diffusion Model
cra_exp Exponential Subjective Value Model
cra_linear Linear Subjective Value Model
dbdm_prob_weight Probability Weight Function
dd_cs Constant-Sensitivity (CS) Model
dd_cs_single Constant-Sensitivity (CS) Model
dd_exp Exponential Model
dd_hyperbolic Hyperbolic Model
dd_hyperbolic_single Hyperbolic Model
estimate_mode Function to estimate mode of MCMC samples
extract_ic Extract Model Comparison Estimates
gng_m1 RW + noise
gng_m2 RW + noise + bias
gng_m3 RW + noise + bias + pi
gng_m4 RW (rew/pun) + noise + bias + pi
hBayesDM Hierarchical Bayesian Modeling of Decision-Making Tasks
HDIofMCMC Compute Highest-Density Interval
igt_orl Outcome-Representation Learning Model
igt_pvl_decay Prospect Valence Learning (PVL) Decay-RI
igt_pvl_delta Prospect Valence Learning (PVL) Delta
igt_vpp Value-Plus-Perseverance
multiplot Function to plot multiple figures
peer_ocu Other-Conferred Utility (OCU) Model
plot.hBayesDM General Purpose Plotting for hBayesDM. This function plots hyper parameters.
plotDist Plots the histogram of MCMC samples.
plotHDI 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.
plotInd Plots individual posterior distributions, using the stan_plot function of the rstan package
printFit Print model-fits (mean LOOIC or WAIC values in addition to Akaike weights) of hBayesDM Models
prl_ewa Experience-Weighted Attraction Model
prl_fictitious Fictitious Update Model
prl_fictitious_multipleB Fictitious Update Model
prl_fictitious_rp Fictitious Update Model, with separate learning rates for positive and negative prediction error (PE)
prl_fictitious_rp_woa Fictitious Update Model, with separate learning rates for positive and negative prediction error (PE), without alpha (indecision point)
prl_fictitious_woa Fictitious Update Model, without alpha (indecision point)
prl_rp Reward-Punishment Model
prl_rp_multipleB Reward-Punishment Model
pst_gainloss_Q Gain-Loss Q Learning Model
pst_Q Q Learning Model
ra_noLA Prospect Theory, without loss aversion (LA) parameter
ra_noRA Prospect Theory, without risk aversion (RA) parameter
ra_prospect Prospect Theory
rdt_happiness Happiness Computational Model
rhat Function for extracting Rhat values from an hBayesDM object
task2AFC_sdt Signal detection theory model
ts_par4 Hybrid Model, with 4 parameters
ts_par6 Hybrid Model, with 6 parameters
ts_par7 Hybrid Model, with 7 parameters (original model)
ug_bayes Ideal Observer Model
ug_delta Rescorla-Wagner (Delta) Model
wcs_sql Sequential Learning Model