Toolbox for estimating Generalized Additive Mixed Models (GAMMs), Generalized Additive Mixed Models of Location Scale and Shape (GAMMLSS), and more general smooth models.
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). 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. 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).
Plotting code to visualize and validate mssm models is provided in this repository together with a tutorial for mssm!
The easiest option is to install from pypi via pip.
pipThe latest release of mssm can be installed from pypi. So to install mssm simply run:
conda create -n mssm_env python=3.10
conda activate mssm_env
pip install mssm
pip install matplotlib # Only needed for tutorials
The fourth line, installing matplotlib is only necessary if you want to run the tutorial. 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.
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 .
Contributions are welcome! Feel free to open issues or make pull-requests to main.
FAQs
Toolbox for estimating Generalized Additive Mixed Models (GAMMs), Generalized Additive Mixed Models of Location Scale and Shape (GAMMLSS), and more general smooth models.
We found that mssm demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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