Love multi-level-modeling using lme4 in R, but prefer to work in the scientific Python ecosystem? This package has got you covered! It’s a small convenience package wrapping the basic functionality of lme4 for compatibility with python. (Currently this only include linear and logistic multi-level models)

This package’s main purpose is to provide a clean interface that hides the back-and-forth code required when moving between R and Python. In other words a user can work completely in Python, never having to deal with R, but get (most) of lme4’s goodness. Behind the scenes this package simply uses rpy2 to pass objects between languages, compute what’s needed, parse everything, and convert to Python types (e.g. numpy arrays, pandas dataframes, etc).

This package can also fit standard regression models with a few extra bells and whistles compared to R’s lm() (Currently this only includes linear models)

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

Github Repo Source


This package has some extra goodies to make life a bit easier, namely:

  • For multi-level models (i.e. glmer()):

    • Automatic inclusion of p-values in model output using lmerTest
    • Automatic inclusion of confidence intervals in model output
    • Automatic conversion and calculation of odds-ratios and probabilities for logit models
    • Easy access to model fits, residuals, and random effects as pandas dataframes
    • Random effects plotting using seaborn
    • Easy post-hoc tests with multiple-comparisons correction via lsmeans
    • Easy model predictions on new data
    • Easy generation of new data from a fitted model
  • For standard linear models (i.e. lm())

    • Automatic inclusion of confidence intervals in model output
    • Easy computation of empirically bootstrapped 95% confidence intervals
    • Easy computation of heteroscedasticity or auto-correlation robust ‘sandwich estimators’ for standard errors (note: these are not the same as auto-regressive models)
    • Permutation tests on model parameters
  • Data simulation

    • Highly customizable functions for generating data useful for standard regression models and multi-level models


Requires a working installation of both Python (2.7 or 3.6) and R (>= 3.2.4).

You will also need the lme4, lmerTest, and lsmeans R packages installed.

This package will not install R or R packages for you!

  1. Method 1 - Install from PyPi (stable)
pip install pymer4
  1. Method 2 - Install from github (latest)
pip install git+

Install issues

Some users have issues installing pymer4 on recent versions of macOS. This is due to compiler issues that give rpy2 (a package dependency of pymer4) some issues during install. Here’s a fix that should work for that:

  1. Install homebrew if you don’t have it already by running the command at the link (it’s a great pacakage manager for macOS). To check if you already have it, do which brew in your Terminal. If nothing pops up you don’t have it.

  2. Fix brew permissions: sudo chown -R $(whoami) $(brew --prefix)/* (this is necessary on macOS Sierra or higher (>= macOS 10.12))

  3. Update homebrew brew update

  4. Install an updated compiler: brew install gcc, or if you have homebrew already, brew upgrade gcc

  5. Enable the new compiler for use:

    export CC="$(find `brew info gcc | grep usr | sed 's/(.*//' | awk '{printf $1"/bin"}'` -name 'x86*gcc-7')"
    export CFLAGS="-W"
  6. If the above results in any error output (it should return nothing) you might need to manually find out where the new compiler is installed. To do so use brew info gcc and cd into the directory that begins with /usr in the output of that command. From there cd into bin and look for a file that begins with x86 and ends with gcc-7. Copy the full path to that file and run the following:

    export CC= pathYouCopiedInQuotes
    export CFLAGS="-W"
  7. Finally install rpy2 using the new compiler you just installed: pip install rpy2==2.8.6

  8. Now you should be able to pip install pymer4:)