From 5a9cf7c04acb4c91f9c8458d0db0fb2d473bf500 Mon Sep 17 00:00:00 2001 From: krishnbera Date: Fri, 20 May 2022 23:25:37 -0400 Subject: [PATCH 1/2] version update --- README.rst | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/README.rst b/README.rst index 519731af4..35fe8a6c7 100644 --- a/README.rst +++ b/README.rst @@ -9,7 +9,7 @@ Introduction :Mailing list: https://groups.google.com/group/hddm-users/ :Copyright: This document has been placed in the public domain. :License: HDDM is released under the BSD 2 license. -:Version: 0.9.5 +:Version: 0.9.6 .. image:: https://secure.travis-ci.org/hddm-devs/hddm.png?branch=master @@ -109,7 +109,6 @@ Features HDDM now includes use of `likelihood approximation networks`_ in conjunction with reinforcement learning models via the **HDDMnnRL** class. This allows researchers to study not only the across-trial dynamics of learning but the within-trial dynamics of choice processes, using a single model. This module greatly extends the previous functionality for fitting RL+DDM models (via HDDMrl class) by allowing fitting of a number of variants of sequential sampling models in conjuction with a learning process (RL+SSM models). - We have included a new **simulator**, which allows data generation for a host of variants of sequential sampling models in conjunction with the Rescorla-Wagner update rule on a 2-armed bandit task environment. There are some new, out-of-the-box **plots** and **utility function** in the **hddm.plotting** and **hddm.utils** modules, respectively, to facilitate posterior visualization and posterior predictive checks. @@ -217,12 +216,12 @@ And if you're a mac user, check out this `thread`_ for advice on installation. How to cite =========== -If HDDM was used in your research, please cite the publication_: +If HDDM was used in your research, please cite the `publication`__: Wiecki TV, Sofer I and Frank MJ (2013). HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python. Front. Neuroinform. 7:14. doi: 10.3389/fninf.2013.00014 -If you use the HDDMnn, HDDMnnRegressor, HDDMnnStimCoding or HDDMnnRL class, please cite the publication2_: +If you use the HDDMnn, HDDMnnRegressor, HDDMnnStimCoding or HDDMnnRL class, please cite the `publication`__: Alexander Fengler, Lakshmi N Govindarajan, Tony Chen, Michael J Frank (2021). Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience. eLife 10:e65074. doi: 10.7554/eLife.65074 @@ -260,7 +259,7 @@ Join our low-traffic `mailing list`_. .. _mailing list: https://groups.google.com/group/hddm-users/ .. _SciPy Superpack: http://fonnesbeck.github.com/ScipySuperpack/ .. _Anaconda: http://docs.continuum.io/anaconda/install.html -.. _publication: http://www.frontiersin.org/Journal/10.3389/fninf.2013.00014/abstract -.. _publication2: https://elifesciences.org/articles/65074 +.. __: http://www.frontiersin.org/Journal/10.3389/fninf.2013.00014/abstract +.. __: https://elifesciences.org/articles/65074 .. _published papers: https://scholar.google.com/scholar?oi=bibs&hl=en&cites=17737314623978403194 .. _thread: https://groups.google.com/forum/#!topic/hddm-users/bdQXewfUzLs From 25ba782dd5cb22dbe3017ee8beb01be5fa7592ed Mon Sep 17 00:00:00 2001 From: krishnbera Date: Fri, 20 May 2022 23:30:32 -0400 Subject: [PATCH 2/2] fix readme --- README.rst | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/README.rst b/README.rst index 35fe8a6c7..36a1b899b 100644 --- a/README.rst +++ b/README.rst @@ -109,8 +109,7 @@ Features HDDM now includes use of `likelihood approximation networks`_ in conjunction with reinforcement learning models via the **HDDMnnRL** class. This allows researchers to study not only the across-trial dynamics of learning but the within-trial dynamics of choice processes, using a single model. This module greatly extends the previous functionality for fitting RL+DDM models (via HDDMrl class) by allowing fitting of a number of variants of sequential sampling models in conjuction with a learning process (RL+SSM models). - We have included a new **simulator**, which allows data generation for a host of variants of sequential sampling models - in conjunction with the Rescorla-Wagner update rule on a 2-armed bandit task environment. + We have included a new **simulator**, which allows data generation for a host of variants of sequential sampling models in conjunction with the Rescorla-Wagner update rule on a 2-armed bandit task environment. There are some new, out-of-the-box **plots** and **utility function** in the **hddm.plotting** and **hddm.utils** modules, respectively, to facilitate posterior visualization and posterior predictive checks. Lastly you can also save and load **HDDMnnRL** models. Please see the **documentation** (under **HDDMnnRL Extension**) for illustrations on how to use the new features.