Toolbox for estimating Generalized additive mixed models (GAMMs), semi-Markov-switching (sms) GAMMs, and Impulse response sms GAMMs.
Project description
mssm: Markov-switching Spline Models
Description
mssm
is a toolbox to estimate Generalized Additive Mixed Models (GAMMs) semi Markov-switching GAMMs (sMs-GAMMs) and sMs Impulse Response GAMMs (sMs-IR-GAMMs). 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).
Installation
The easiest option is to install from pypi via pip
.
- Setup a conda environment with python > 3.10
- Install mssm via
pip
The 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 tutorials. Note: pypi will only reflect releases (Basically, the state of the stable branch). Pushes to main continue to be distributed to test.pypi, so if you need the latest changes you can get them from test.pypi. In that case, you need to replace the third line in the code above with:
pip install -i https://test.pypi.org/simple/ mssm
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 .
To get started
- With GAMMs: Take a look at tutorial 1 in the tutorial folder.
- With sms-IR-GAMMs: Take a look at tutorial 2.
- With sms-GAMMs: Take a look at tutorial 3.
Contributing
Contributions are welcome! Feel free to open issues or make pull-requests to main.
Project details
Release history Release notifications | RSS feed
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Algorithm | Hash digest | |
---|---|---|
SHA256 | 39a274ba29d69bb25a4beb06923c6726a28c9e0a57cf0d91e4ceb216a3b1c6dc |
|
MD5 | 9eb95496634f5a336a5bf17ceb9a2400 |
|
BLAKE2b-256 | 1be2d73ee12401671423eca5eaf25f727a4595b69f740961184d6356b81b18ee |