Hierarchical Bayesian Modeling of Decision-Making Tasks

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Documentation for package ‘hBayesDM’ version 1.2.1

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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.
banditNarm_2par_lapse 3 Parameter Model, without C (choice perseveration), R (reward sensitivity), and P (punishment sensitivity). But with xi (noise)
banditNarm_4par 4 Parameter Model, without C (choice perseveration)
banditNarm_delta Rescorla-Wagner (Delta) Model
banditNarm_kalman_filter Kalman Filter
banditNarm_lapse 5 Parameter Model, without C (choice perseveration) but with xi (noise)
banditNarm_lapse_decay 5 Parameter Model, without C (choice perseveration) but with xi (noise). Added decay rate (Niv et al., 2015, J. Neuro).
banditNarm_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
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
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
pstRT_ddm Drift Diffusion Model
pstRT_rlddm1 Reinforcement Learning Drift Diffusion Model 1
pstRT_rlddm6 Reinforcement Learning Drift Diffusion Model 6
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