From 25ba782dd5cb22dbe3017ee8beb01be5fa7592ed Mon Sep 17 00:00:00 2001 From: krishnbera Date: Fri, 20 May 2022 23:30:32 -0400 Subject: [PATCH] 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.