Man pages for hBayesDM
Hierarchical Bayesian Modeling of Decision-Making Tasks

alt_deltaRescorla-Wagner (Delta) Model
alt_gammaRescorla-Wagner (Gamma) Model
bandit2arm_deltaRescorla-Wagner (Delta) Model
bandit4arm2_kalman_filterKalman Filter
bandit4arm_2par_lapse3 Parameter Model, without C (choice perseveration), R...
bandit4arm_4par4 Parameter Model, without C (choice perseveration)
bandit4arm_lapse5 Parameter Model, without C (choice perseveration) but with...
bandit4arm_lapse_decay5 Parameter Model, without C (choice perseveration) but with...
bandit4arm_singleA_lapse4 Parameter Model, without C (choice perseveration) but with...
banditNarm_2par_lapse3 Parameter Model, without C (choice perseveration), R...
banditNarm_4par4 Parameter Model, without C (choice perseveration)
banditNarm_deltaRescorla-Wagner (Delta) Model
banditNarm_kalman_filterKalman Filter
banditNarm_lapse5 Parameter Model, without C (choice perseveration) but with...
banditNarm_lapse_decay5 Parameter Model, without C (choice perseveration) but with...
banditNarm_singleA_lapse4 Parameter Model, without C (choice perseveration) but with...
bart_ewmvExponential-Weight Mean-Variance Model
bart_par4Re-parameterized version of BART model with 4 parameters
cgt_cmCumulative Model
choiceRT_ddmDrift Diffusion Model
choiceRT_ddm_singleDrift Diffusion Model
choiceRT_lbaChoice Reaction Time task, linear ballistic accumulator...
choiceRT_lba_singleChoice Reaction Time task, linear ballistic accumulator...
cra_expExponential Subjective Value Model
cra_linearLinear Subjective Value Model
dbdm_prob_weightProbability Weight Function
dd_csConstant-Sensitivity (CS) Model
dd_cs_singleConstant-Sensitivity (CS) Model
dd_expExponential Model
dd_hyperbolicHyperbolic Model
dd_hyperbolic_singleHyperbolic Model
estimate_modeFunction to estimate mode of MCMC samples
extract_icExtract Model Comparison Estimates
gng_m1RW + noise
gng_m2RW + noise + bias
gng_m3RW + noise + bias + pi
gng_m4RW (rew/pun) + noise + bias + pi
hBayesDM_modelhBayesDM Model Base Function
hBayesDM-packageHierarchical Bayesian Modeling of Decision-Making Tasks
HDIofMCMCCompute Highest-Density Interval
igt_orlOutcome-Representation Learning Model
igt_pvl_decayProspect Valence Learning (PVL) Decay-RI
igt_pvl_deltaProspect Valence Learning (PVL) Delta
igt_vppValue-Plus-Perseverance
multiplotFunction to plot multiple figures
peer_ocuOther-Conferred Utility (OCU) Model
plotDistPlots the histogram of MCMC samples.
plot.hBayesDMGeneral Purpose Plotting for hBayesDM. This function plots...
plotHDIPlots highest density interval (HDI) from (MCMC) samples and...
plotIndPlots individual posterior distributions, using the stan_plot...
printFitPrint model-fits (mean LOOIC or WAIC values in addition to...
prl_ewaExperience-Weighted Attraction Model
prl_fictitiousFictitious Update Model
prl_fictitious_multipleBFictitious Update Model
prl_fictitious_rpFictitious Update Model, with separate learning rates for...
prl_fictitious_rp_woaFictitious Update Model, with separate learning rates for...
prl_fictitious_woaFictitious Update Model, without alpha (indecision point)
prl_rpReward-Punishment Model
prl_rp_multipleBReward-Punishment Model
pst_gainloss_QGain-Loss Q Learning Model
pst_QQ Learning Model
pstRT_ddmDrift Diffusion Model
pstRT_rlddm1Reinforcement Learning Drift Diffusion Model 1
pstRT_rlddm6Reinforcement Learning Drift Diffusion Model 6
ra_noLAProspect Theory, without loss aversion (LA) parameter
ra_noRAProspect Theory, without risk aversion (RA) parameter
ra_prospectProspect Theory
rdt_happinessHappiness Computational Model
rhatFunction for extracting Rhat values from an hBayesDM object
task2AFC_sdtSignal detection theory model
ts_par4Hybrid Model, with 4 parameters
ts_par6Hybrid Model, with 6 parameters
ts_par7Hybrid Model, with 7 parameters (original model)
ug_bayesIdeal Observer Model
ug_deltaRescorla-Wagner (Delta) Model
wcs_sqlSequential Learning Model
hBayesDM documentation built on Sept. 23, 2022, 9:06 a.m.