.. mssm documentation master file, created by sphinx-quickstart on Fri Sep 27 15:37:49 2024. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. mssm documentation ================== This is the documentation of the **public** API of ``mssm``. The entire source code is available for inspection on `GitHub `_. For the purpose of **applying** the models available in this toolbox, the documentation here should however be sufficient. ``mssm`` is a Python toolbox to estimate Generalized Additive Mixed Models (GAMMs), Generalized Additive Mixed Models of Location Scale and Shape (GAMMLSS), and more general (mixed) smooth models in the sense defined by `Wood, Pya, & Säfken (2016) `_. mssm is an excellent choice for the modeling of multi-level time-series data, often estimating additive models with separate smooths for thousands of levels in a couple of minutes. Use the side-bar on the left to navigate through the document tree (just click on ``src`` and it will expand). Supported Models ---------------- Currently, ``mssm`` supports Gaussian (G)AMMs (:func:`mssm.src.python.exp_fam.Gaussian`), Gamma GAMMs (:func:`mssm.src.python.exp_fam.Gamma`), Inverse Gaussian GAMMs (:func:`mssm.src.python.exp_fam.InvGauss`), and Binomial GAMMs (:func:`mssm.src.python.exp_fam.Binomial`). For GAMMLSS models, Gaussian models (:func:`mssm.src.python.exp_fam.GAUMLSS`), Gamma models (:func:`mssm.src.python.exp_fam.GAMMALS`), and Multionimal models (:func:`mssm.src.python.exp_fam.MULNOMLSS`) are supported. If you are missing a family, don't worry - all you need to do is to implement the :func:`mssm.src.python.exp_fam.Family` template class for GAMM models and the :func:`mssm.src.python.exp_fam.GAMLSSFamily` template class for GAMMLSS models. To implement a member of the most general kind of smooth model, you will need to implement only the :func:`mssm.src.python.exp_fam.GENSMOOTHFamily` template class - ``mssm`` even supports completely derivative-free estimation. You can check the :class:`mssm.models.GSMM` documentation for an example! Supported Terms ---------------- If you want to know which (smooth, parametric, & random) terms are supported by these models, you should take a look at the :class:`mssm.src.python.formula.Formula` class and the terms implemented in :py:mod:`mssm.src.python.terms`. Tutorial & Model Visualization ------------------------------ To get started with ``mssm`` the tutorial and visualization code available as part of the `mssmViz `_ package might also be helpful. ``mssmViz`` for example offers functions to visualize predictions & residuals for GAMM & GAMMLSS. .. toctree:: :maxdepth: 2 :caption: Contents: modules