mssm 1.1.2

Toolbox for estimating Generalized Additive Mixed Models (GAMMs), Generalized Additive Mixed Models of Location Scale and Shape (GAMMLSS), and more general smooth models.

mssm: Mixed Sparse Smooth Models

Description

[!NOTE] Our preprint detailing the algorithms implemented in the mssm toolbox is now available on arXiv.

mssm is a 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). Approximate estimation (and automatic regularization) of the latter only requires users to provide the (gradient of) the log-likelihood. Furthermore, 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.

Note: The main branch is updated frequently to reflect new developments. The stable branch should reflect the latest releases. If you don't need the newest functionality, you should install from the stable branch (see below for instructions). Documentation is hosted here - together with a tutorial for mssm! Plotting code to visualize and validate mssm models is provided in this repository!

Installation

The easiest option is to install from pypi via pip. This can be achieved in two steps:

1. Setup a conda environment with python > 3.10
2. Install mssm via pip

The latest release of mssm can be installed from pypi. So to complete both steps (after installing conda - see here for instructions), simply run:

conda create -n mssm_env python=3.13
conda activate mssm_env
pip install mssm[plot] # [plot] also installs ``mssmViz``

Note: pypi will only reflect releases (Basically, the state of the stable branch). If you urgently need a feature currently only available on the main branch, consider building from source.

Building from source

You can also build directly from source. This requires conda or an installation of eigen (setup.py then expects eigen in "usr/local/include/eigen3". This will probably not work on windows - the conda strategy should.). Once you have conda installed, install eigen from conda-forge. After cloning and navigating into the downloaded repository you can then install via:

pip install .

Contributing

Contributions are welcome! Feel free to open issues or make pull-requests to main.