Pymer4

https://github.com/ejolly/pymer4/actions/workflows/CI.yml/badge.svg https://badge.fury.io/py/pymer4.svg https://anaconda.org/ejolly/pymer4/badges/version.svg https://anaconda.org/ejolly/pymer4/badges/platforms.svg https://pepy.tech/badge/pymer4 http://joss.theoj.org/papers/10.21105/joss.00862/status.svg https://zenodo.org/badge/90598701.svg https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8-blue
https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat

pymer4 is a statistics library for estimating various regression and multi-level models in Python. Love lme4 in R, but prefer to work in the scientific Python ecosystem? This package has got you covered!

pymer4 provides a clean interface that hides the back-and-forth code required when moving between R and Python. In other words, you can work completely in Python, never having to deal with R, but get (most) of lme4’s goodness. This is accomplished using rpy2 to interface between langauges.

Additionally pymer4 can fit various additional regression models with some bells, such as robust standard errors, and two-stage regression (summary statistics) models. See the features page for more information.

TL;DR This package is your new simple Pythonic drop-in replacement for lm() or glmer() in R.

For an example of what’s possible check out the tutorials or this blog post comparing different modeling strategies for clustered/repeated-measures data.

Publications

pymer4 has been used to analyze data is several publications including but not limited to:

  • Jolly, E., Sadhukha, S., & Chang, L.J. (in press). Custom-molded headcases have limited efficacy in reducing head motion during naturalistic fMRI expreiments. NeuroImage.

  • Sharon, G., Cruz, N. J., Kang, D. W., et al. (2019). Human gut microbiota from autism spectrum disorder promote behavioral symptoms in mice. Cell, 177(6), 1600-1618.

  • Urbach, T. P., DeLong, K. A., Chan, W. H., & Kutas, M. (2020). An exploratory data analysis of word form prediction during word-by-word reading. Proceedings of the National Academy of Sciences, 117(34), 20483-20494.

  • Chen, P. H. A., Cheong, J. H., Jolly, E., Elhence, H., Wager, T. D., & Chang, L. J. (2019). Socially transmitted placebo effects. Nature Human Behaviour, 3(12), 1295-1305.

Citing

If you use pymer4 in your own work, please cite:

Jolly, (2018). Pymer4: Connecting R and Python for Linear Mixed Modeling. Journal of Open Source Software, 3(31), 862, https://doi.org/10.21105/joss.00862