Skip to content

Commit 2d89c67

Browse files
JaeyeongYangJaeyeongYang
committed
Merge branch 'release/v0.7.2'
2 parents b1128e7 + e78a0ac commit 2d89c67

File tree

99 files changed

+2547
-613
lines changed

Some content is hidden

Large Commits have some content hidden by default. Use the searchbox below for content that may be hidden.

99 files changed

+2547
-613
lines changed

‎DESCRIPTION‎

Lines changed: 7 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -1,7 +1,7 @@
11
Package: hBayesDM
22
Title: Hierarchical Bayesian Modeling of Decision-Making Tasks
3-
Version: 0.7.1
4-
Date: 2019-01-21
3+
Version: 0.7.2
4+
Date: 2019-02-11
55
Author:
66
Woo-Young Ahn [aut, cre],
77
Nate Haines [aut],
@@ -20,8 +20,8 @@ Maintainer: Woo-Young Ahn <wooyoung.ahn@gmail.com>
2020
Description:
2121
Fit an array of decision-making tasks with computational models in
2222
a hierarchical Bayesian framework. Can perform hierarchical Bayesian analysis of
23-
various computational models with a single line of coding.
24-
Ahn et al. (2018) <doi:10.1162/CPSY_a_00002>
23+
various computational models with a single line of coding
24+
(Ahn et al., 2017) <doi:10.1162/CPSY_a_00002>.
2525
Depends:
2626
R (>= 3.4.0),
2727
Rcpp (>= 0.12.0),
@@ -54,8 +54,11 @@ Collate:
5454
'hBayesDM_model.R'
5555
'bandit2arm_delta.R'
5656
'bandit4arm2_kalman_filter.R'
57+
'bandit4arm_2par_lapse.R'
5758
'bandit4arm_4par.R'
5859
'bandit4arm_lapse.R'
60+
'bandit4arm_lapse_decay.R'
61+
'bandit4arm_singleA_lapse.R'
5962
'bart_par4.R'
6063
'choiceRT_ddm.R'
6164
'choiceRT_ddm_single.R'

‎NAMESPACE‎

Lines changed: 3 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -4,8 +4,11 @@ S3method(plot,hBayesDM)
44
export(HDIofMCMC)
55
export(bandit2arm_delta)
66
export(bandit4arm2_kalman_filter)
7+
export(bandit4arm_2par_lapse)
78
export(bandit4arm_4par)
89
export(bandit4arm_lapse)
10+
export(bandit4arm_lapse_decay)
11+
export(bandit4arm_singleA_lapse)
912
export(bart_par4)
1013
export(choiceRT_ddm)
1114
export(choiceRT_ddm_single)

‎NEWS.md‎

Lines changed: 7 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1,3 +1,9 @@
1+
# hBayesDM 0.7.2
2+
3+
* Add three new models for the bandit4arm task: `bandit4arm_2par_lapse`,
4+
`bandit4arm_lapse_decay` and `bandit4arm_singleA_lapse`.
5+
* Fix various (minor) errors.
6+
17
# hBayesDM 0.7.1
28

39
* Make it usable without manually loading `rstan`.
@@ -6,7 +12,7 @@
612
# hBayesDM 0.7.0
713

814
* Now, in default, you should build a Stan file into a binary for the first time to use it. To build all the models on installation, you should set an environmental variable `BUILD_ALL` to `true` before installation.
9-
* Now all the implemented models are refactored using `hBayesDM_model` function. You don't have to change anything to use them, but developers can easily implement new model now!
15+
* Now all the implemented models are refactored using `hBayesDM_model` function. You don't have to change anything to use them, but developers can easily implement new models now!
1016
* We added a Kalman filter model for 4-armed bandit task (`bandit4arm2_kalman_filter`; Daw et al., 2006) and a probability weighting function for general description-based tasks (`dbdm_prob_weight`; Erev et al., 2010; Hertwig et al., 2004; Jessup et al., 2008).
1117
* Initial values of parameter estimation for some models are updated as plausible values, and the parameter boundaries of several models are fixed (see more on issue #63 and #64 in Github).
1218
* Exponential and linear models for choice under risk and ambiguity task now have four model regressors: `sv`, `sv_fix`, `sv_var`, and `p_var`.

‎R/bandit4arm2_kalman_filter.R‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1,6 +1,6 @@
11
#' @templateVar MODEL_FUNCTION bandit4arm2_kalman_filter
22
#' @templateVar CONTRIBUTOR \href{https://zohyos7.github.io}{Yoonseo Zoh}, \href{https://lei-zhang.net/}{Lei Zhang}
3-
#' @templateVar TASK_NAME 4-Armed Bandit Task (2)
3+
#' @templateVar TASK_NAME 4-Armed Bandit Task (modified)
44
#' @templateVar MODEL_NAME Kalman Filter
55
#' @templateVar MODEL_CITE (Daw et al., 2006, Nature)
66
#' @templateVar MODEL_TYPE Hierarchical

‎R/bandit4arm_2par_lapse.R‎

Lines changed: 69 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,69 @@
1+
#' @templateVar MODEL_FUNCTION bandit4arm_2par_lapse
2+
#' @templateVar TASK_NAME 4-Armed Bandit Task
3+
#' @templateVar MODEL_NAME 3 Parameter Model, without C (choice perseveration), R (reward sensitivity), and P (punishment sensitivity). But with xi (noise)
4+
#' @templateVar MODEL_CITE (Aylward et al., 2018, PsyArXiv)
5+
#' @templateVar MODEL_TYPE Hierarchical
6+
#' @templateVar DATA_COLUMNS "subjID", "choice", "gain", "loss"
7+
#' @templateVar PARAMETERS "Arew" (reward learning rate), "Apun" (punishment learning rate), "xi" (noise)
8+
#' @templateVar LENGTH_DATA_COLUMNS 4
9+
#' @templateVar DETAILS_DATA_1 \item{"subjID"}{A unique identifier for each subject in the data-set.}
10+
#' @templateVar DETAILS_DATA_2 \item{"choice"}{Integer value representing the option chosen on the given trial: 1, 2, 3, or 4.}
11+
#' @templateVar DETAILS_DATA_3 \item{"gain"}{Floating point value representing the amount of currency won on the given trial (e.g. 50, 100).}
12+
#' @templateVar DETAILS_DATA_4 \item{"loss"}{Floating point value representing the amount of currency lost on the given trial (e.g. 0, -50).}
13+
#'
14+
#' @template model-documentation
15+
#'
16+
#' @export
17+
#' @include hBayesDM_model.R
18+
#'
19+
#' @references
20+
#' Aylward, Valton, Ahn, Bond, Dayan, Roiser, & Robinson (2018) Altered decision-making under
21+
#' uncertainty in unmedicated mood and anxiety disorders. PsyArxiv. 10.31234/osf.io/k5b8m
22+
23+
bandit4arm_2par_lapse <- hBayesDM_model(
24+
task_name = "bandit4arm",
25+
model_name = "2par_lapse",
26+
data_columns = c("subjID", "choice", "gain", "loss"),
27+
parameters = list("Arew" = c(0, 0.1, 1),
28+
"Apun" = c(0, 0.1, 1),
29+
"xi" = c(0, 0.1, 1)),
30+
preprocess_func = function(raw_data, general_info) {
31+
# Currently class(raw_data) == "data.table"
32+
33+
# Use general_info of raw_data
34+
subjs <- general_info$subjs
35+
n_subj <- general_info$n_subj
36+
t_subjs <- general_info$t_subjs
37+
t_max <- general_info$t_max
38+
39+
# Initialize (model-specific) data arrays
40+
rew <- array( 0, c(n_subj, t_max))
41+
los <- array( 0, c(n_subj, t_max))
42+
choice <- array(-1, c(n_subj, t_max))
43+
44+
# Write from raw_data to the data arrays
45+
for (i in 1:n_subj) {
46+
subj <- subjs[i]
47+
t <- t_subjs[i]
48+
DT_subj <- raw_data[subjid == subj]
49+
50+
rew[i, 1:t] <- DT_subj$gain
51+
los[i, 1:t] <- -1 * abs(DT_subj$loss)
52+
choice[i, 1:t] <- DT_subj$choice
53+
}
54+
55+
# Wrap into a list for Stan
56+
data_list <- list(
57+
N = n_subj,
58+
T = t_max,
59+
Tsubj = t_subjs,
60+
rew = rew,
61+
los = los,
62+
choice = choice
63+
)
64+
65+
# Returned data_list will directly be passed to Stan
66+
return(data_list)
67+
}
68+
)
69+

‎R/bandit4arm_lapse_decay.R‎

Lines changed: 71 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,71 @@
1+
#' @templateVar MODEL_FUNCTION bandit4arm_lapse_decay
2+
#' @templateVar TASK_NAME 4-Armed Bandit Task
3+
#' @templateVar MODEL_NAME 5 Parameter Model, without C (choice perseveration) but with xi (noise). Added decay rate (Niv et al., 2015, J. Neuro).
4+
#' @templateVar MODEL_CITE (Aylward et al., 2018, PsyArXiv)
5+
#' @templateVar MODEL_TYPE Hierarchical
6+
#' @templateVar DATA_COLUMNS "subjID", "choice", "gain", "loss"
7+
#' @templateVar PARAMETERS "Arew" (reward learning rate), "Apun" (punishment learning rate), "R" (reward sensitivity), "P" (punishment sensitivity), "xi" (noise), "d" (decay rate)
8+
#' @templateVar LENGTH_DATA_COLUMNS 4
9+
#' @templateVar DETAILS_DATA_1 \item{"subjID"}{A unique identifier for each subject in the data-set.}
10+
#' @templateVar DETAILS_DATA_2 \item{"choice"}{Integer value representing the option chosen on the given trial: 1, 2, 3, or 4.}
11+
#' @templateVar DETAILS_DATA_3 \item{"gain"}{Floating point value representing the amount of currency won on the given trial (e.g. 50, 100).}
12+
#' @templateVar DETAILS_DATA_4 \item{"loss"}{Floating point value representing the amount of currency lost on the given trial (e.g. 0, -50).}
13+
#'
14+
#' @template model-documentation
15+
#'
16+
#' @export
17+
#' @include hBayesDM_model.R
18+
#'
19+
#' @references
20+
#' Aylward, Valton, Ahn, Bond, Dayan, Roiser, & Robinson (2018) Altered decision-making under
21+
#' uncertainty in unmedicated mood and anxiety disorders. PsyArxiv. 10.31234/osf.io/k5b8m
22+
23+
bandit4arm_lapse_decay <- hBayesDM_model(
24+
task_name = "bandit4arm",
25+
model_name = "lapse_decay",
26+
data_columns = c("subjID", "choice", "gain", "loss"),
27+
parameters = list("Arew" = c(0, 0.1, 1),
28+
"Apun" = c(0, 0.1, 1),
29+
"R" = c(0, 1, 30),
30+
"P" = c(0, 1, 30),
31+
"xi" = c(0, 0.1, 1),
32+
"d" = c(0, 0.1, 1)),
33+
preprocess_func = function(raw_data, general_info) {
34+
# Currently class(raw_data) == "data.table"
35+
36+
# Use general_info of raw_data
37+
subjs <- general_info$subjs
38+
n_subj <- general_info$n_subj
39+
t_subjs <- general_info$t_subjs
40+
t_max <- general_info$t_max
41+
42+
# Initialize (model-specific) data arrays
43+
rew <- array( 0, c(n_subj, t_max))
44+
los <- array( 0, c(n_subj, t_max))
45+
choice <- array(-1, c(n_subj, t_max))
46+
47+
# Write from raw_data to the data arrays
48+
for (i in 1:n_subj) {
49+
subj <- subjs[i]
50+
t <- t_subjs[i]
51+
DT_subj <- raw_data[subjid == subj]
52+
53+
rew[i, 1:t] <- DT_subj$gain
54+
los[i, 1:t] <- -1 * abs(DT_subj$loss)
55+
choice[i, 1:t] <- DT_subj$choice
56+
}
57+
58+
# Wrap into a list for Stan
59+
data_list <- list(
60+
N = n_subj,
61+
T = t_max,
62+
Tsubj = t_subjs,
63+
rew = rew,
64+
los = los,
65+
choice = choice
66+
)
67+
68+
# Returned data_list will directly be passed to Stan
69+
return(data_list)
70+
}
71+
)

‎R/bandit4arm_singleA_lapse.R‎

Lines changed: 69 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,69 @@
1+
#' @templateVar MODEL_FUNCTION bandit4arm_singleA_lapse
2+
#' @templateVar TASK_NAME 4-Armed Bandit Task
3+
#' @templateVar MODEL_NAME 4 Parameter Model, without C (choice perseveration) but with xi (noise). Single learning rate both for R and P.
4+
#' @templateVar MODEL_CITE (Aylward et al., 2018, PsyArXiv)
5+
#' @templateVar MODEL_TYPE Hierarchical
6+
#' @templateVar DATA_COLUMNS "subjID", "choice", "gain", "loss"
7+
#' @templateVar PARAMETERS "A" (learning rate), "R" (reward sensitivity), "P" (punishment sensitivity), "xi" (noise)
8+
#' @templateVar LENGTH_DATA_COLUMNS 4
9+
#' @templateVar DETAILS_DATA_1 \item{"subjID"}{A unique identifier for each subject in the data-set.}
10+
#' @templateVar DETAILS_DATA_2 \item{"choice"}{Integer value representing the option chosen on the given trial: 1, 2, 3, or 4.}
11+
#' @templateVar DETAILS_DATA_3 \item{"gain"}{Floating point value representing the amount of currency won on the given trial (e.g. 50, 100).}
12+
#' @templateVar DETAILS_DATA_4 \item{"loss"}{Floating point value representing the amount of currency lost on the given trial (e.g. 0, -50).}
13+
#'
14+
#' @template model-documentation
15+
#'
16+
#' @export
17+
#' @include hBayesDM_model.R
18+
#'
19+
#' @references
20+
#' Aylward, Valton, Ahn, Bond, Dayan, Roiser, & Robinson (2018) Altered decision-making under
21+
#' uncertainty in unmedicated mood and anxiety disorders. PsyArxiv. 10.31234/osf.io/k5b8m
22+
23+
bandit4arm_singleA_lapse <- hBayesDM_model(
24+
task_name = "bandit4arm",
25+
model_name = "singleA_lapse",
26+
data_columns = c("subjID", "choice", "gain", "loss"),
27+
parameters = list("A" = c(0, 0.1, 1),
28+
"R" = c(0, 1, 30),
29+
"P" = c(0, 1, 30),
30+
"xi" = c(0, 0.1, 1)),
31+
preprocess_func = function(raw_data, general_info) {
32+
# Currently class(raw_data) == "data.table"
33+
34+
# Use general_info of raw_data
35+
subjs <- general_info$subjs
36+
n_subj <- general_info$n_subj
37+
t_subjs <- general_info$t_subjs
38+
t_max <- general_info$t_max
39+
40+
# Initialize (model-specific) data arrays
41+
rew <- array( 0, c(n_subj, t_max))
42+
los <- array( 0, c(n_subj, t_max))
43+
choice <- array(-1, c(n_subj, t_max))
44+
45+
# Write from raw_data to the data arrays
46+
for (i in 1:n_subj) {
47+
subj <- subjs[i]
48+
t <- t_subjs[i]
49+
DT_subj <- raw_data[subjid == subj]
50+
51+
rew[i, 1:t] <- DT_subj$gain
52+
los[i, 1:t] <- -1 * abs(DT_subj$loss)
53+
choice[i, 1:t] <- DT_subj$choice
54+
}
55+
56+
# Wrap into a list for Stan
57+
data_list <- list(
58+
N = n_subj,
59+
T = t_max,
60+
Tsubj = t_subjs,
61+
rew = rew,
62+
los = los,
63+
choice = choice
64+
)
65+
66+
# Returned data_list will directly be passed to Stan
67+
return(data_list)
68+
}
69+
)

‎R/bart_par4.R‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1,7 +1,7 @@
11
#' @templateVar MODEL_FUNCTION bart_par4
22
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/harhim-park/}{Harhim Park}, \href{https://ccs-lab.github.io/team/jaeyeong-yang/}{Jaeyeong Yang}, \href{https://ccs-lab.github.io/team/ayoung-lee/}{Ayoung Lee}, \href{https://ccs-lab.github.io/team/jeongbin-oh/}{Jeongbin Oh}, \href{https://ccs-lab.github.io/team/jiyoon-lee/}{Jiyoon Lee}, \href{https://ccs-lab.github.io/team/junha-jang/}{Junha Jang}
33
#' @templateVar TASK_NAME Balloon Analogue Risk Task
4-
#' @templateVar TASK_CITE (Ravenzwaaij et al., 2011, Journal of Mathematical Psychology)
4+
#' @templateVar TASK_CITE (Ravenzwaaij et al., 2011)
55
#' @templateVar MODEL_NAME Re-parameterized version (by Harhim Park & Jaeyeong Yang) of BART Model (Ravenzwaaij et al., 2011) with 4 parameters
66
#' @templateVar MODEL_TYPE Hierarchical
77
#' @templateVar DATA_COLUMNS "subjID", "pumps", "explosion"

‎R/dbdm_prob_weight.R‎

Lines changed: 1 addition & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -1,4 +1,5 @@
11
#' @templateVar MODEL_FUNCTION dbdm_prob_weight
2+
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/yoonseo-zoh/}{Yoonseo Zoh}
23
#' @templateVar TASK_NAME Description Based Decison Making Task
34
#' @templateVar MODEL_NAME Probability Weight Function
45
#' @templateVar MODEL_CITE (Erev et al., 2010; Hertwig et al., 2004; Jessup et al., 2008)

‎R/hBayesDM.R‎

Lines changed: 30 additions & 23 deletions
Original file line numberDiff line numberDiff line change
@@ -1,64 +1,71 @@
11
#' Hierarchical Bayesian Modeling of Decision-Making Tasks
22
#' @docType package
33
#' @name hBayesDM-package
4-
#' @aliases hBayesDM
5-
#' @useDynLib hBayesDM, .registration = TRUE
6-
#'
4+
#' @aliases hBayesDM
5+
#' @useDynLib hBayesDM, .registration = TRUE
6+
#'
77
#' @import methods
88
#' @import Rcpp
99
#' @description
1010
#' Fit an array of decision-making tasks with computational models in a hierarchical Bayesian framework. Can perform hierarchical Bayesian analysis of various computational models with a single line of coding.
11-
#' Bolded tasks, followed by their respective models, are itemized below.
12-
#'
11+
#' Bolded tasks, followed by their respective models, are itemized below.
12+
#'
1313
#' \describe{
1414
#' \item{\strong{Bandit}}{2-Armed Bandit (Rescorla-Wagner (delta)) --- \link{bandit2arm_delta} \cr
1515
#' 4-Armed Bandit with fictive updating + reward/punishment sensitvity (Rescorla-Wagner (delta)) --- \link{bandit4arm_4par} \cr
1616
#' 4-Armed Bandit with fictive updating + reward/punishment sensitvity + lapse (Rescorla-Wagner (delta)) --- \link{bandit4arm_lapse}}
17+
#' \item{\strong{Bandit2}}{Kalman filter --- \link{bandit4arm2_kalman_filter}}
18+
#' \item{\strong{Choice RT}}{Drift Diffusion Model --- \link{choiceRT_ddm} \cr
19+
#' Drift Diffusion Model for a single subject --- \link{choiceRT_ddm_single} \cr
20+
#' Linear Ballistic Accumulator (LBA) model --- \link{choiceRT_lba} \cr
21+
#' Linear Ballistic Accumulator (LBA) model for a single subject --- \link{choiceRT_lba_single}}
22+
#' \item{\strong{Choice under Risk and Ambiguity}}{Exponential model --- \link{cra_exp} \cr
23+
#' Linear model --- \link{cra_linear}}
24+
#' \item{\strong{Description-Based Decision Making}}{probability weight function --- \link{dbdm_prob_weight}}
1725
#' \item{\strong{Delay Discounting}}{Constant Sensitivity --- \link{dd_cs} \cr
18-
#' Constant Sensitivity for single subject --- \link{dd_cs_single} \cr
26+
#' Constant Sensitivity for a single subject --- \link{dd_cs_single} \cr
1927
#' Exponential --- \link{dd_exp} \cr
2028
#' Hyperbolic --- \link{dd_hyperbolic} \cr
21-
#' Hyperbolic for single subject --- \link{dd_hyperbolic_single}}
29+
#' Hyperbolic for a single subject --- \link{dd_hyperbolic_single}}
2230
#' \item{\strong{Orthogonalized Go/Nogo}}{RW + Noise --- \link{gng_m1} \cr
2331
#' RW + Noise + Bias --- \link{gng_m2} \cr
2432
#' RW + Noise + Bias + Pavlovian Bias --- \link{gng_m3} \cr
2533
#' RW(modified) + Noise + Bias + Pavlovian Bias --- \link{gng_m4}}
26-
#' \item{\strong{Iowa Gambling}}{Prospect Valence Learning-DecayRI --- \link{igt_pvl_decay} \cr
34+
#' \item{\strong{Iowa Gambling}}{Outcome-Representation Learning --- \link{igt_orl} \cr
35+
#' Prospect Valence Learning-DecayRI --- \link{igt_pvl_decay} \cr
2736
#' Prospect Valence Learning-Delta --- \link{igt_pvl_delta} \cr
2837
#' Value-Plus_Perseverance --- \link{igt_vpp}}
2938
#' \item{\strong{Peer influence task}}{OCU model --- \link{peer_ocu}}
30-
#' \item{\strong{Probabilistic Reversal Learning}}{Fictitious Update --- \link{prl_fictitious} \cr
39+
#' \item{\strong{Probabilistic Reversal Learning}}{Experience-Weighted Attraction --- \link{prl_ewa} \cr
40+
#' Fictitious Update --- \link{prl_fictitious} \cr
3141
#' Fictitious Update w/o alpha (indecision point) --- \link{prl_fictitious_woa} \cr
3242
#' Fictitious Update and multiple blocks per subject --- \link{prl_fictitious_multipleB} \cr
33-
#' Experience-Weighted Attraction --- \link{prl_ewa} \cr
3443
#' Reward-Punishment --- \link{prl_rp} \cr
3544
#' Reward-Punishment and multiple blocks per subject --- \link{prl_rp_multipleB} \cr
3645
#' Fictitious Update with separate learning for Reward-Punishment --- \link{prl_fictitious_rp} \cr
3746
#' Fictitious Update with separate learning for Reward-Punishment w/o alpha (indecision point) --- \link{prl_fictitious_rp_woa}}
47+
#' \item{\strong{Probabilistic Selection Task}}{Q-learning with two learning rates --- \link{pst_gainloss_Q}}
3848
#' \item{\strong{Risk Aversion}}{Prospect Theory (PT) --- \link{ra_prospect} \cr
3949
#' PT without a loss aversion parameter --- \link{ra_noLA} \cr
4050
#' PT without a risk aversion parameter --- \link{ra_noRA}}
41-
#' \item{\strong{Ultimatum Game}}{Ideal Bayesian Observer --- \link{ug_bayes} \cr
42-
#' Rescorla-Wagner (delta) --- \link{ug_delta}}
43-
#' \item{\strong{Choice/Reaction time}}{Drift Diffusion Model --- \link{choiceRT_ddm} \cr
44-
#' Drift Diffusion Model for single subject --- \link{choiceRT_ddm_single} \cr
45-
#' Linear Ballistic Accumulator --- \link{choiceRT_lba} \cr
46-
#' Linear Ballistic Accumulator for single subject --- \link{choiceRT_lba_single}}
51+
#' \item{\strong{Risky Decision Task}}{Happiness model --- \link{rdt_happiness}}
4752
#' \item{\strong{Two-Step task}}{Full model (7 parameters) --- \link{ts_par7} \cr
4853
#' 6 parameter model (without eligibility trace, lambda) --- \link{ts_par6} \cr
4954
#' 4 parameter model --- \link{ts_par4}}
50-
#'
55+
#' \item{\strong{Ultimatum Game}}{Ideal Bayesian Observer --- \link{ug_bayes} \cr
56+
#' Rescorla-Wagner (delta) --- \link{ug_delta}}
57+
#'
5158
#' }
52-
#'
53-
#' @seealso
59+
#'
60+
#' @seealso
5461
#' For tutorials and further readings, visit : \url{http://rpubs.com/CCSL/hBayesDM}.
55-
#'
56-
#' @references
57-
#' Please cite as:
62+
#'
63+
#' @references
64+
#' Please cite as:
5865
#' Ahn, W.-Y., Haines, N., & Zhang, L. (2017). Revealing neuro-computational mechanisms of reinforcement learning and decision-making with the hBayesDM package. \emph{Computational Psychiatry}. 1, 24-57. https://doi.org/10.1162/CPSY_a_00002
5966
#'
6067
#' @author
61-
#' Woo-Young Ahn \email{wooyoung.ahn@@gmail.com}
68+
#' Woo-Young Ahn \email{wahn55@@snu.ac.kr}
6269
#'
6370
#' Nathaniel Haines \email{haines.175@@osu.edu}
6471
#'

0 commit comments

Comments
 (0)