PyNM: a Lightweight Python implementation of Normative Modeling

Python Jupyter Notebook Submitted 11 March 2022Published 08 December 2022
Review

Editor: @dfm (all papers)
Reviewers: @smkia (all reviews), @saigerutherford (all reviews)

Authors

Annabelle Harvey (0000-0002-9940-8799), Guillaume Dumas (0000-0002-2253-1844)

Citation

Harvey et al., (2022). PyNM: a Lightweight Python implementation of Normative Modeling. Journal of Open Source Software, 7(80), 4321, https://doi.org/10.21105/joss.04321

@article{Harvey2022, doi = {10.21105/joss.04321}, url = {https://doi.org/10.21105/joss.04321}, year = {2022}, publisher = {The Open Journal}, volume = {7}, number = {80}, pages = {4321}, author = {Annabelle Harvey and Guillaume Dumas}, title = {PyNM: a Lightweight Python implementation of Normative Modeling}, journal = {Journal of Open Source Software} }
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Tags

Normative Modeling Heterogeneity Heteroskedasticity Big Data Centiles LOESS Gaussian Process Stochastic Variational Gaussian Process GAMLSS Computational Psychiatry Neuroscience

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ISSN 2475-9066