docs

README.md

@@ -4,29 +4,29 @@
 [![docs-main](https://img.shields.io/readthedocs/tsml-eval/latest?logo=readthedocs&label=docs%20%28latest%29)](https://tsml-eval.readthedocs.io/en/latest/)
 [![codecov](https://img.shields.io/codecov/c/github/time-series-machine-learning/tsml-eval?label=codecov&logo=codecov)](https://codecov.io/gh/time-series-machine-learning/tsml-eval)
 [![pypi](https://img.shields.io/pypi/v/tsml-eval?logo=pypi&color=blue)](https://pypi.org/project/tsml-eval/)
+[![!conda](https://img.shields.io/conda/vn/conda-forge/tsml-eval?logo=anaconda&color=blue)](https://anaconda.org/conda-forge/tsml-eval)
 [![python-versions](https://img.shields.io/pypi/pyversions/tsml-eval?logo=python)](https://www.python.org/)
 [![black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 [![license](https://img.shields.io/badge/license-BSD%203--Clause-green?logo=style)](https://github.com/time-series-machine-learning/tsml-eval/blob/main/LICENSE)
 
 # tsml-eval
 
-tsml-eval contains benchmarking and evaluation tools for time series machine learning
+`tsml-eval` contains benchmarking and evaluation tools for time series machine learning
 algorithms.
 
-The current release of tsml-eval is v0.1.1.
+The current release of `tsml-eval` is v0.1.1.
 
-Installation
-------------
+## Installation
 
-tsml-eval is available on PyPI and can be installed via pip:
+`tsml-eval` is available on PyPI and can be installed via pip:
 
-    pip install tsml-eval
+```console
+pip install tsml-eval
+```
 
-More information available on out documentation webpage:
-https://tsml-eval.readthedocs.io/en/stable/
+More information available on our [documentation](https://tsml-eval.readthedocs.io/en/stable/installation.html).
 
-Acknowledgements
-----------------
+## Acknowledgements
 
 This work is supported by the UK Engineering and Physical Sciences Research Council
-(EPSRC) EP/W030756/1
+(EPSRC) EP/W030756/2

docs/installation.md

@@ -1,72 +1,93 @@
 # Installation
 
+The following contains information on installing `tsml-eval` for users and developers
+with write access. Those who wish to contribute to `tsml-eval` without write access
+will need to create a fork, see the [aeon](https://www.aeon-toolkit.org/en/stable/developer_guide/dev_installation.html)
+and [sklearn](https://scikit-learn.org/stable/developers/contributing.html#how-to-contribute)
+documentation on contributing and developer installation for guidance.
+
+We recommend setting up a fresh virtual environment or the conda equivalent before
+installing `tsml-eval`. See the [aeon guide](https://www.aeon-toolkit.org/en/stable/installation.html#using-a-pip-venv)
+for setup information.
+
 ## Install from PyPi
 
-The easiest way to install ``tsml-eval`` is using ``pip``:
+The easiest way to install `tsml-eval` is using `pip`:
 
-```{code-block} console
+```console
 pip install tsml-eval
 ```
 
 Some estimators require additional dependencies. You can install these individually as
-required, or install all of them using the ``all_extras`` extra dependency set:
+required, or install all of them using the `all_extras` extra dependency set:
 
-```{code-block} console
+```console
 pip install tsml-eval[all_extras]
 ```
 
 All extra dependency sets can be found in the [pyproject.toml](https://github.com/time-series-machine-learning/tsml-eval/blob/main/pyproject.toml)
-file ``[project.optional-dependencies]`` options.
+file `[project.optional-dependencies]` options.
 
 To install a specific [release](https://github.com/time-series-machine-learning/tsml-eval/releases)
 version, specify the version number when installing:
 
-```{code-block} console
+```console
 pip install tsml-eval==0.1.0
 ```
 
-```{code-block} console
+```console
 pip install tsml-eval[all_extras]==0.1.0
 ```
 
+## Install from conda-forge
+
+`tsml-eval` is also available on [conda-forge](https://anaconda.org/conda-forge/tsml-eval).
+
+```console
+conda create -n tsml-env -c conda-forge tsml-eval
+conda activate tsml-env
+```
+
+Currently for conda installations, optional dependencies must be installed separately.
+
 ## Install fixed dependency versions for a publication
 
-``tsml-eval`` [publications](publications.md) contain a ``requirements.txt`` file that
-lists the versions of all  dependencies used to generate results at the time of the
-release.
+`tsml-eval` [publications](publications.md) contain a `static_publication_reqs.txt`
+file that lists the versions of all dependencies used to generate results at the time
+of the release.
 
 To install the dependencies using this file, run:
 
-```{code-block} console
-pip install -r requirements.txt
+```console
+pip install -r static_publication_reqs.txt
 ```
 
-## Install latest in-development version from GitHub
+## Install the latest in-development version from GitHub
 
-The latest development version of ``tsml-eval`` can be installed directly from GitHub
-using ``pip``:
+The latest development version of `tsml-eval` can be installed directly from GitHub
+using `pip`:
 
-```{code-block} console
+```console
 pip install git+https://github.com/time-series-machine-learning/tsml-eval.git@main
 ```
 
-## Install for developers
+## Install for developers with write access
 
-To install ``tsml-eval`` for development, first clone the GitHub repository:
+To install `tsml-eval` for development, first clone the GitHub repository:
 
-```{code-block} console
+```console
 git clone https://github.com/time-series-machine-learning/tsml-eval.git
 ```
 
 Then install the package in editable mode with developer dependencies:
 
-```{code-block} console
+```console
 pip install --editable .[dev]
 ```
 
 We recommend setting up pre-commit hooks to automatically format code and check for
 common issues before committing:
 
-```{code-block} console
+```console
 pre-commit install
 ```

docs/publications.md

@@ -5,10 +5,19 @@ the package.
 
 ## 2023
 
+### Classification
+
+[Bake off redux: a review and experimental evaluation of recent time series classification algorithms](./publications/2023/tsc_bakeoff/tsc_bakeoff_2023.ipynb)
+
+[Extracting Features from Random Subseries: A Hybrid Pipeline for Time Series Classification and Extrinsic Regression](./publications/2023/rist_pipeline/rist_pipeline.ipynb)
+
+### Clustering
+
 [A Review and Evaluation of Elastic Distance Functions for Time Series Clustering](./publications/2023/distance_based_clustering/distance_based_clustering.ipynb)
 
-[Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression](./publications/2023/tser_archive_expansion/tser_archive_expansion.ipynb)
+### Regression
 
-[Bake off redux: a review and experimental evaluation of recent time series classification algorithms](./publications/2023/tsc_bakeoff/tsc_bakeoff_2023.ipynb)
+[Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression](./publications/2023/tser_archive_expansion/tser_archive_expansion.ipynb)
 
 [Extracting Features from Random Subseries: A Hybrid Pipeline for Time Series Classification and Extrinsic Regression](./publications/2023/rist_pipeline/rist_pipeline.ipynb)
+