# Hierarchical Bayesian Modeling of Decision-Making Tasks

## Documentation for package ‘hBayesDM’ version 2.0.0

- DESCRIPTION file.
- Package NEWS.

## Help Pages

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
hdi | Compute Highest-Density Interval
hgf_ibrb | Hierarchical Bayesian version of the Hierarchical Gaussian Filter model for binary inputs and binary responses
hgf_ibrb_single | Individual-level Bayesian version of the Hierarchical Gaussian Filter model for binary inputs and binary responses
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_dist | Plots the histogram of MCMC samples.
plot_hdi | 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.
plot_ind | Plots individual posterior distributions using 'bayesplot'.
print_fit | 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