Metadata-Version: 2.4
Name: mssmViz
Version: 0.1.5
Summary: Plotting code to visualize models estimated with the mssm toolbox.
Author-email: Joshua Krause <jokra001@proton.me>
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.24.1
Requires-Dist: pandas>=1.5.3
Requires-Dist: scipy>=1.15.0
Requires-Dist: matplotlib>=3.8.2
Requires-Dist: mssm
Provides-Extra: dev
Requires-Dist: flake8; extra == "dev"
Requires-Dist: sphinx>=8.1.3; extra == "dev"
Requires-Dist: sphinx-rtd-theme>=3.0.2; extra == "dev"
Dynamic: license-file

# mssmViz

![Docs](https://github.com/jokra1/mssmViz/actions/workflows/documentation.yml/badge.svg?branch=main)

## Description

> [!NOTE]
> The tutorial for the ``mssm`` toolbox has moved [here](https://jokra1.github.io/mssm/tutorial.html).

Plotting functions for the Mixed Sparse Smooth Models ([mssm](https://github.com/JoKra1/mssm)) toolbox. ``mssm`` is a toolbox to estimate Generalized Additive Mixed Models (GAMMs), Generalized Additive Mixed Models of Location Scale and Shape (GAMMLSS), and even more general smooth models in the sense defined by [Wood, Pya, & Säfken (2016)](https://doi.org/10.1080/01621459.2016.1180986). **Documentation** for ``mssmViz``  is hosted [here](https://jokra1.github.io/mssmViz/index.html).

## Installation

To install ``mssm`` simply run:

```
conda create -n mssm_env python=3.11
conda activate mssm_env
pip install mssm
```

Subsequently, ``mssmViz`` can be installed by running:

```
pip install mssmViz
```

Alternatively, you can clone the repository into a folder of your choice:

```
git clone https://github.com/JoKra1/mssmViz.git
```

After navigating to the folder into which you cloned this repository, you can then install `mssmViz` plot functions
by running:

```
pip install -e .
```

The -e flag will ensure that any new changes you pull from this repository will be reflected when you use the plot functions.
