From f1567f8918f4b706a82bdd4373626d6869a9cd8a Mon Sep 17 00:00:00 2001 From: Paul Xu Date: Thu, 18 Jan 2024 12:15:37 -0500 Subject: [PATCH] Update documentation to reflect changes in hssm version --- README.md | 6 +++--- docs/index.md | 6 ++---- docs/overrides/main.html | 2 +- 3 files changed, 6 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 54872d7f..9b81bf5c 100644 --- 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 index a8c94695..aaa22554 100644 --- 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 index 81fe76d3..a52b467b 100644 --- a/docs/overrides/main.html +++ b/docs/overrides/main.html @@ -5,7 +5,7 @@ Navigate the site here! - v0.2.0b1 is released! + v0.2.0 is released! {% include ".icons/material/head-question.svg" %}