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https://github.com/ejolly/pymer4

All the convenience of lme4 in python
https://github.com/ejolly/pymer4

lme4 mixed-models python

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All the convenience of lme4 in python

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# Pymer4

❗️⚠️ **[Contributors wanted](https://github.com/ejolly/pymer4/issues/61)** ⚠️❗️

Pymer4 is a statistics library for estimating various regression and multi-level models in Python. Love [lme4](https://cran.r-project.org/web/packages/lme4/index.html) 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](hhttps://rpy2.github.io/doc/latest/html/index.html/) 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 example:

```python
# Assuming you have a pandas dataframe in tidy/long format
# with DV and IV columns for dependent/outcome vars and
# independent/predictor vars

model = Lmer('DV ~ IV1 + IV2 + (IV+IV2|Group)', data=dataframe)

# Fit and print an R/statsmodels style summary
# with t/z-tests, CIs, and p-values
model.fit()

# Access model attributes
model.BIC
model.residuals

# Get fitted parameters
model.coef # population parameters
model.fixef # group/cluster estimates (BLUPs)
model.ranef # group/cluster deviates
```

## Documentation

Check out the [documentation site](http://eshinjolly.com/pymer4/) for detailed tutorial examples, API documentation, and installation instructions!

## Installation

Installing via [Anaconda](https://www.anacnda.com/products/individual) is the preferred installation method. Follow the directions [here](https://eshinjolly.com/pymer4/installation.html).

## Local build

```
conda create -n build python=3.8 pip conda-build
conda activate build
conda-build ./conda --python=3.8 -c https://conda.anaconda.org/conda-forge --output-folder ./conda/build --output ./conda/build
tarball=$(conda-build ./conda --python=3.8 --output | tail -1)
conda convert -p linux-64 -p win-64 $tarball -o ./conda/build
```

## Local upload

```
# after
# conda install anaconda-client
# anaconda login

# pre-release
anaconda upload ./conda/build/**/pymer4*.tar.bz2 -l "pre-release"

# main
anaconda upload ./conda/build/**/pymer4*.tar.bz2 -l "main"
```

## Contributing

Contributions are *always welcome*!
If you are interested in contributing feel free to check out the [open issues](https://github.com/ejolly/pymer4/issues), [development roadmap on Trello](https://trello.com/b/gGKmeAJ4), or submit pull requests for additional features or bug fixes. If you do make a pull request, please do so by forking the [development branch](https://github.com/ejolly/pymer4/tree/dev) and taking note of the [contribution guidelines](https://eshinjolly.com/pymer4/contributing.html).

## Contributors

[![](https://github.com/turbach.png?size=50)](https://github.com/turbach)
[![](https://github.com/Shotgunosine.png?size=50)](https://github.com/Shotgunosine)