Add Read the Docs configuration and update README for documentation setup

Author: bmusangu

.readthedocs.yml

@@ -0,0 +1,17 @@
+# .readthedocs.yml
+version: 2
+
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.13"
+
+python:
+  install:
+    - method: pip
+      path: .
+      extra_requirements:
+        - docs
+
+sphinx:
+  configuration: docs/conf.py

README.md

@@ -1,28 +1,35 @@
 # BlockInvGPFA
 
 [![Automated Tests](https://github.com/CausalityInMotion/BlockInverseGPFA/actions/workflows/tests.yml/badge.svg)](https://github.com/CausalityInMotion/BlockInverseGPFA/actions/workflows/tests.yml)
+[![Documentation Status](https://readthedocs.org/projects/blockinversegpfa/badge/?version=latest)](https://blockinversegpfa.readthedocs.io/en/latest/)
+
+This package provides a Python implementation of **Gaussian Process Factor Analysis (GPFA)** that incorporates a novel approach to efficiently handle variable-length time series data. Building on the original method by [Byron Yu et al. (2009)](https://papers.nips.cc/paper/2009/hash/6c1b887a379c4f0c2c621f305d15f6b0-Abstract.html), this implementation introduces a **block-matrix–based inversion strategy** that reuses kernel computations across trials of different lengths.
+
+The package also includes **scikit-learn–compatible API** for integration into existing ML workflows such as:
+- A **Modular Preprocessing** — Separates data preprocessing from model logic via a dedicated `EventTimesToCounts` transformer.
+  - Accepts standard array-like inputs (not `Neo` objects), simplifying integration with other tools.
+  - Follows scikit-learn's transformer–estimator interface for clean, reusable workflows.
+- A new **variance-explained metric** to evaluate GPFA model fits.
+
+This implementation is adapted from [Elephant](https://elephant.readthedocs.io/en/latest/reference/gpfa.html)’s GPFA codebase with substantial modifications to improve performance, modularity, and usability in Python-based pipelines.
 
-This package is an implementation of Gaussian Process Factor Analysis (GPFA) by Byron Yu et al.,
-(2009) in python. The code is based on [Elephant](https://elephant.readthedocs.io/en/latest/reference/gpfa.html)'s
-python implementation plus additional modules and functional implementations.
 
 ## Usage
 
 ### Installation
 -----------------
 
-- Clone the project locally using 
+Install directly from GitHub using `pip`:
 
 ```bash
-$ git clone https://github.com/CausalityInMotion/GPFA_for_sklearn
+$ pip install git+https://github.com/CausalityInMotion/BlockInverseGPFA.git
 ```
-
-Ensure you are in the working directory of the project.
-
-Then install the project's required packages uisng
+Or clone the repo and install locally:
 
 ```bash
-$ pip install -r requirements.txt
+$ git clone https://github.com/CausalityInMotion/BlockInverseGPFA.git
+$ cd BlockInverseGPFA
+$ pip install . # or pip install .[test,docs] to include optional dependencies
 ```
 
 You are now set to use the package.
@@ -38,41 +45,46 @@ Building the documentation requires the following packages:
  - [numpydoc](https://numpydoc.readthedocs.io/)
  - [Jupyter Notebook Tools for Sphinx](https://nbsphinx.readthedocs.io/)
 
-You can install the required packages either using the `docs/requirements.txt` file,
+You can install the required documentation dependencies by:
 ```bash
-$ pip install -r docs/requirements.txt
+$ pip install .[docs]
 ```
 or manually by calling
 ```bash
 $ pip install sphinx sphinx-rtd-theme numpydoc nbsphinx
 ```
 
-Finally, to view the documentation, run
+Finally, to view the documentation locally, run
 
 ```bash
 $ cd docs
 $ make html
 $ open _build/html/index.html
 ```
+or view them online:
+[BlockInvGPFA docs](https://blockinversegpfa.readthedocs.io/en/latest/)
+
+A detailed walkthrough of the package — including how to fit the model to real neural data — is available in the Jupyter notebook example: (link)
 
 -----------
 ### Tests
 -----------
 
-To run the unittests in the [test](test) folder, use the following command in your command line/terminal:
+To run the full test suite in the [test](test) folder, use:
 
 ```bash
-$ python -m unittest test.gpfa
-$ python -m unittest test.preprocessing
+$ pip install .[test]
+$ pytest test/
 ```
+Tests are automatically run via [GitHub Actions](https://github.com/CausalityInMotion/BlockInverseGPFA/actions/new) on every push and pull request.
 
 ## License
 Modified BSD License based on Elephant, see [LICENSE.txt](LICENSE.txt) for details.
 
 
 ## Copyright
 
-:copyright: 2021 Brooks M. Musangu and Jan Drugowitsch
+:copyright: 2021-2025 Brooks M. Musangu and Jan Drugowitsch
 
 ## Acknowledgments