mssmViz
Description
Plotting functions for the Mixed Sparse Smooth Models (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). Documentation for mssmViz is hosted here.
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.
1 maintainer
Metadata
1 maintainer
Release files for mssmViz 0.1.52
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| mssmviz-0.1.52.tar.gz | 1.4 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mssmviz-0.1.52-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.4 MB
Release files / mssmviz-0.1.52.tar.gz
| Download URL | mssmviz-0.1.52.tar.gz |
|---|---|
| Size | 1.4 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
bec895064688da533903ae51e62ca02976b8a0d52a3dac127c9ed644a9d8789f
|
|
BLAKE2b-256 checksum How to use checksums |
fb609d7e69324fe0336454bb6a6c130eecb6f026d16432e5457ae2a67b2c7d93
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 21, 2026.
Transparency logRelease files / mssmviz-0.1.52-py3-none-any.whl
| Download URL | mssmviz-0.1.52-py3-none-any.whl |
|---|---|
| Size | 45.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
a60d7d24b0946723a5c6d2827d0f6d0e603dcc10fa04bdcd10a4a48ca4293aa6
|
|
BLAKE2b-256 checksum How to use checksums |
0db3f358127de0da07bb55596f5e061195e6613e5592772105fc565e5136b564
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 21, 2026.
Transparency log
