Update documentation to reflect changes in hssm version

lnccbrown · Jan 18, 2024 · f1567f8 · f1567f8
1 parent b17dd73
commit f1567f8
Show file tree

Hide file tree

Showing 3 changed files with 6 additions and 8 deletions.
diff --git a/README.md b/README.md
@@ -31,13 +31,13 @@ HSSM is a Python toolbox that provides a seamless combination of state-of-the-ar
 
 ## Installation
 
-`hssm` is available through PyPI. You can install it with Pip via:
+`hssm` is available through PyPI. You can install it with pip via:
 
 ```
 pip install hssm
 ```
 
-You can also install the bleeding edge version of `hssm` directly from this repo:
+You can also install the bleeding-edge version of `hssm` directly from this repo:
 
 ```
 pip install git+https://github.com/lnccbrown/HSSM.git
@@ -51,7 +51,7 @@ for more detailed instructions.
 [here](https://github.com/lnccbrown/HSSM/discussions). We recommend leveraging an
 environment manager with Python 3.10~3.11 to prevent any problems with dependencies
 during the installation process. Please note that hssm is tested for python 3.10,
-3.11. As of HSSM v0.1.6, support for Python 3.9 is dropped. Use other python
+3.11. As of HSSM v0.2.0, support for Python 3.9 is dropped. Use other python
 versions with caution.
 
 ## Example

diff --git a/docs/index.md b/docs/index.md
@@ -15,7 +15,6 @@
 
 **HSSM** (Hierarchical Sequential Sampling Modeling) is a modern Python toolbox that provides state-of-the-art likelihood approximation methods within the Python Bayesian ecosystem. It facilitates hierarchical model building and inference via fast and robust MCMC samplers. User-friendly, extensible, and flexible, HSSM can rigorously estimate the impact of neural and other trial-by-trial covariates through parameter-wise mixed-effects models for a large variety of cognitive process models.
 
-
 HSSM is a [BRAINSTORM](https://ccbs.carney.brown.edu/brainstorm) project in collaboration with the [Center for Computation and Visualization (CCV)](https://ccv.brown.edu/) and the [Center for Computational Brain Science](https://ccbs.carney.brown.edu/) within the [Carney Institute at Brown University](https://www.brown.edu/carney/).
 
 ## Features
@@ -30,7 +29,7 @@ HSSM is a [BRAINSTORM](https://ccbs.carney.brown.edu/brainstorm) project in coll
 
 ## Installation
 
-`hssm` is available through PyPI. You can install it with Pip via:
+`hssm` is available through PyPI. You can install it with pip via:
 
 ```bash
 pip install hssm
@@ -50,10 +49,9 @@ For more detailed guidance, please check out our [installation guide](getting_st
     [here](https://github.com/lnccbrown/HSSM/discussions). We recommend leveraging an
     environment manager with Python 3.10~3.11 to prevent any problems with dependencies
     during the installation process. Please note that hssm is tested for python 3.10,
-    3.11. As of HSSM v0.1.6, support for Python 3.9 is dropped. Use other python
+    3.11. As of HSSM v0.2.0, support for Python 3.9 is dropped. Use other python
     versions with caution.
 
-
 ### Setting global float type
 
 Using the analytical DDM (Drift Diffusion Model) likelihood in PyMC without forcing float type to `"float32"` in PyTensor may result in warning messages during sampling, which is a known bug in PyMC v5.6.0 and earlier versions. We can use `hssm.set_floatX("float32")` to get around this for now.

diff --git a/docs/overrides/main.html b/docs/overrides/main.html
@@ -5,7 +5,7 @@
   </span>
   Navigate the site here!
 </span>
-<span class="right-margin"> v0.2.0b1 is released! </span>
+<span class="right-margin"> v0.2.0 is released! </span>
 <span>
   <span class="twemoji">
     {% include ".icons/material/head-question.svg" %}