Removed some duplicated source code and updated workflows / README / gitmodules

Author: andrewherren

.github/workflows/docs.yml

@@ -23,6 +23,7 @@ jobs:
     - name: Checkout repository
       uses: actions/checkout@v4
       with:
+        repository: 'StochasticTree/stochtree'
         submodules: 'recursive'
 
     - name: Setup Python 3.10

.gitmodules

@@ -1,7 +0,0 @@
-[submodule "pybind11"]
-	path = pybind11
-	url = https://github.com/pybind/pybind11
-	branch = stable
-[submodule "src/stochtree-cpp"]
-	path = src/stochtree-cpp
-	url = https://github.com/StochasticTree/stochtree-cpp/

CMakeLists.txt

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2024 StochasticTree
+Copyright (c) 2024 stochtree authors
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

LICENSE

@@ -1,52 +1,5 @@
-# StochasticTree Python Package
+# StochasticTree Python Package Documentation
 
-## Getting started
-
-The python package can be installed from source. Before you begin, make sure you have [conda](https://www.anaconda.com/download) installed.
-Clone the repo recursively (including git submodules) by running 
-
-```{bash}
-git clone --recursive https://github.com/StochasticTree/stochtree-python.git
-```
-
-### Conda-based installation
-
-Conda provides a straightforward experience in managing python dependencies, avoiding version conflicts / ABI issues / etc.
-
-To build stochtree using a `conda` based workflow, first create and activate a conda environment with the requisite dependencies
-
-```{bash}
-conda create -n stochtree-dev -c conda-forge python=3.10 numpy scipy pytest pandas pybind11 scikit-learn matplotlib seaborn
-conda activate stochtree-dev
-pip install jupyterlab
-```
-
-Then, navigate to the main `stochtree-python` project folder (i.e. `cd /path/to/stochtree-python`) and install the package locally via pip
-
-```{bash}
-pip install .
-```
-
-### Pip-based installation
-
-If you would rather avoid installing and setting up conda, you can alternatively setup the dependencies and install `stochtree` using only `pip` (caveat: this has not been extensively tested 
-across platforms and python versions).
-
-First, navigate to the main `stochtree-python` project folder (i.e. `cd /path/to/stochtree-python`) and create and activate a virtual environment as a subfolder of the repo
-
-```{bash}
-python -m venv venv
-source venv/bin/activate
-```
-
-Install all of the package (and demo notebook) dependencies
-
-```{bash}
-pip install numpy scipy pytest pandas scikit-learn pybind11 matplotlib seaborn jupyterlab
-```
-
-Then install stochtree via
-
-```{bash}
-pip install .
-```
+This is documentation repository, which builds and publishes 
+docs for the [stochtree python package](https://stochastictree.github.io/stochtree-python/). 
+The source code for the python (and R and C++) is available at https://github.com/StochasticTree/stochtree