update docs/readme

CONTRIBUTORS.rst

@@ -2,8 +2,13 @@ The following people have contributed to HDDM:
 
 * Thomas Wiecki
 * Imri Sofer
+* Alexander Fengler
+* Lakshmi Govindarajan
+* Krishn Bera
 * Michael J. Frank
 * Guido Biele
 * Øystein Sandvik
 * Mads Pedersen
-* Alex Fengler
+
+
+

LICENSE

@@ -1,4 +1,4 @@
-Copyright (c) 2021, Thomas V. Wiecki, Imri Sofer, Mads Lund Pederson, Alexander Fengler, Michael J. Frank, Brown University 
+Copyright (c) 2021, Thomas V. Wiecki, Imri Sofer, Mads Lund Pederson, Alexander Fengler, Lakshmi Govindarajan, Krishn Bera, Michael J. Frank, Brown University 
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without

README.rst

@@ -2,8 +2,8 @@
 Introduction
 ************
 
-:Author: Thomas V. Wiecki, Imri Sofer, Mads L. Pedersen, Alexander Fengler, Michael J. Frank
-:Contact: [email protected], [email protected], [email protected], [email protected], [email protected]
+:Author: Thomas V. Wiecki, Imri Sofer, Mads L. Pedersen, Alexander Fengler, Lakshmi Govindarajan, Krishn Bera, Michael J. Frank
+:Contact: [email protected], [email protected], [email protected], [email protected], [email protected], [email protected]
 :Web site: https://hddm.readthedocs.io
 :Github: http://github.com/hddm-devs/hddm
 :Mailing list: https://groups.google.com/group/hddm-users/
@@ -105,6 +105,17 @@ Features
              Note also that the usage of **indirect betas** as well as **indirect regressors** may affect the speed of sampling in general.
              Both translate into more computational work at the stage of regression likelihood evaluation.
 
+* HDDM 0.9.6 brings a host of new 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.
+             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.
+
 
 Comparison to other packages
 ============================
@@ -211,6 +222,11 @@ 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_:
+
+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
+
 Published papers using HDDM
 ===========================
 
@@ -245,5 +261,6 @@ Join our low-traffic `mailing list`_.
 .. _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
 .. _published papers: https://scholar.google.com/scholar?oi=bibs&hl=en&cites=17737314623978403194
 .. _thread: https://groups.google.com/forum/#!topic/hddm-users/bdQXewfUzLs

docs/source/conf.py

@@ -70,16 +70,16 @@
 
 # General information about the project.
 project = u'HDDM'
-copyright = u'2021, Thomas V. Wiecki, Mads Lund Pedersen, Alexander Fengler, Michael J. Frank, Brown University'
+copyright = u'2022, Thomas V. Wiecki, Mads Lund Pedersen, Alexander Fengler, Krishn Bera, Michael J. Frank, Brown University'
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the
 # built documents.
 #
 # The short X.Y version.
-version = '0.9.5'
+version = '0.9.6'
 # The full version, including alpha/beta/rc tags.
-release = '0.9.5'
+release = '0.9.6'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

hddm/__init__.py

@@ -2,7 +2,7 @@
 
 __docformat__ = "restructuredtext"
 
-__version__ = "0.9.5"
+__version__ = "0.9.6"
 
 from . import simulators
 from . import likelihoods

hddm/examples/demo_HDDMnnRL/demo_HDDMnnRL.ipynb

@@ -4,9 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Tutorial for analyzing instrumental learning data with the HDDMnnRL module\n",
-    "\n",
-    "This is a tutorial for using the HDDMrl module to simultaneously estimate reinforcement learning parameters and decision parameters within a fully hierarchical bayesian estimation framework, including steps for sampling, assessing convergence, model fit, parameter recovery, and posterior predictive checks (model validation). The module uses the reinforcement learning drift diffusion model (RLDDM), a reinforcement learning model that replaces the standard “softmax” choice function with a drift diffusion process. The softmax and drift diffusion process is equivalent for capturing choice proportions, but the DDM also takes RT distributions into account; options are provided to also only fit RL parameters without RT. The RLDDM estimates trial-by-trial drift rate as a scaled difference in expected rewards (expected reward for upper bound alternative minus expected reward for lower bound alternative). Expected rewards are updated with a delta learning rule using either a single learning rate or with separate learning rates for positive and negative prediction errors. The model also includes the standard DDM-parameters. The RLDDM is described in detail in [Pedersen, Frank & Biele (2017).](http://ski.clps.brown.edu/papers/PedersenEtAl_RLDDM.pdf) (Note this approach differs from Frank et al (2015) who used HDDM to fit instrumental learning but did not allow for simultaneous estimation of learning parameters). "
+    "# Tutorial for analyzing instrumental learning data with the HDDMnnRL module"
    ]
   },
   {
@@ -20,6 +18,24 @@
     "import pandas as pd"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.11.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "import arviz as az\n",
+    "print(az.__version__)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

setup.py

@@ -18,16 +18,16 @@
 
 setup(
     name='HDDM',
-    version='0.9.5',
-    author='Thomas V. Wiecki, Imri Sofer, Michael J. Frank, Mads Lund Pedersen, Alexander Fengler, Lakshmi Govindarajan',
+    version='0.9.6',
+    author='Thomas V. Wiecki, Imri Sofer, Michael J. Frank, Mads Lund Pedersen, Alexander Fengler, Lakshmi Govindarajan, Krishn Bera',
     author_email='[email protected]',
     url='http://github.com/hddm-devs/hddm',
     packages=['hddm', 'hddm.tests', 'hddm.models', 'hddm.examples', 'hddm.torch', 'hddm.torch_models', 'hddm.simulators'], # 'hddm.cnn', 'hddm.cnn_models', 'hddm.keras_models',
     package_data={'hddm':['examples/*.csv', 'examples/*.conf', 'torch_models/*', 'simulators/*']}, # 'cnn_models/*/*'  'keras_models/*.h5',
     scripts=['scripts/hddm_demo.py'],
     description='HDDM is a python module that implements Hierarchical Bayesian estimation of Drift Diffusion Models.',
-    install_requires=['NumPy >=1.6.0', 'SciPy >= 0.6.0', 'cython >= 0.29.0', 'pandas >= 0.12.0', 'patsy', 'seaborn >= 0.11.0', 'statsmodels >= 0.12.0', 'tqdm >= 4.1.0', 'scikit-learn >= 0.24', 'cloudpickle >= 2.0.0', 'kabuki >= 0.6.0', 'PyMC >= 2.3.3, < 3.0.0'],
-    setup_requires=['NumPy >=1.6.0', 'SciPy >= 0.6.0',  'cython >= 0.29.0', 'pandas >= 0.12.0', 'patsy', 'seaborn >= 0.11.0', 'statsmodels >= 0.12.0', 'tqdm >= 4.1.0', 'scikit-learn >= 0.24', 'cloudpickle >= 2.0.0', 'kabuki >= 0.6.0', 'PyMC >= 2.3.3, < 3.0.0'],
+    install_requires=['NumPy >=1.6.0', 'SciPy >= 0.6.0', 'cython >= 0.29.0', 'pandas >= 0.12.0', 'patsy', 'seaborn >= 0.11.0', 'statsmodels >= 0.12.0', 'tqdm >= 4.1.0', 'scikit-learn >= 0.24', 'cloudpickle >= 2.0.0', 'kabuki >= 0.6.0', 'PyMC >= 2.3.3, < 3.0.0', 'arviz >= 0.11'],
+    setup_requires=['NumPy >=1.6.0', 'SciPy >= 0.6.0',  'cython >= 0.29.0', 'pandas >= 0.12.0', 'patsy', 'seaborn >= 0.11.0', 'statsmodels >= 0.12.0', 'tqdm >= 4.1.0', 'scikit-learn >= 0.24', 'cloudpickle >= 2.0.0', 'kabuki >= 0.6.0', 'PyMC >= 2.3.3, < 3.0.0', 'arviz >= 0.11'],
     include_dirs = [np.get_include()],
     classifiers=[
                 'Development Status :: 5 - Production/Stable',