What’s New

What’s New#

You can checkout the development roadmap on Github

0.9.3 (Sep ‘25)#

New#

All model classes gain the following new private attributes for convenience:

._init_kwargs to track initialization arguments passed to lm(), lmer(), etc in R
._REML to track whether REML or ML estimation was used (lmer models only)
._report the string printed by the .report() method (which returns None)

Fixes#

#153 all models now properly support R keyword arguments at initialization time, which is most useful for controlling REML vs ML estimation in lmer4 models, i.e. lmer('y~x + (1|x)', data=data, REML=False)

0.9.2 (May ‘25)#

New#

skmer() class that acts as a scikit-learn Estimator. See the api for usage examples
Models have additional .result_fit_stats from performance::model_performance(), including variance partitioning of multi-level models
Added tutorial on installing pymer4 with pixi as an alternative to conda
Added tutorial on similarities-and-differences in coming from R

0.9.1 (May ‘25)#

Fixes#

make compare() more robust by automatically refitting all models if any are not fit or if any have been fit via bootstrapping

Development#

move expand_grid() to tidystats.plutils
Move all dependencies from pip to conda-forge anticipating conda-forge availability

0.9.0 (May ‘25)#

This is release is a major overhaul of pymer4 that involved a near complete re-write of internals to faciliate future maintenance and integrate more advanced functionality from additional R libraries. This is a backwards incompatible release with numerous dependency and API breaking changes. You can check out the migration guide for more details. The 0.8.x version line is still available on github for community contributions/maintenance if desired.

As of this version, pymer4 is now only installable via conda following the instructions here using the ejolly channel. We expect to move to the conda-forge channel soon.

Summary of changes#

Adoption of polars as dataframe “backend”
New consistent API that supports lm, glm, lmer and glmer models
Full support for factors and marginal-estimates/comparisons for all model types
Overhauled docs and tutorials with several included datasets for demo and teaching purposes
Much more extensive testing
Replaced bespoke code with “battle-tested” implementations of helper and utility functions in different R libaries (e.g. broom, insight, parameters)
Switched setup.py and requirements.txt/requirements-dev.txt to pyproject.toml
Switched over to Pixi.sh for development-tooling and Github Actions
Switched documentation from sphinx to jupyterbook
Switched project linting from black to ruff
Exclusively create noarch builds for conda
- Currently implemented as a pixi task using conda build, with plans to switch to pixi build when ready

Encompassed Fixes#

Planned features#

These following features are planned for upcoming versions in 0.9.x line

support for lmerControl options
simulation and power modules

0.8.2#

Fixes#

Issue in LogisticRegression API name change

0.8.1#

Compatibility Updates#

This version includes a noarch build that should be installable on arm-based macOS platforms (e.g. M1, M2, etc)
This version drops support for Python 3.7 and adds support for 3.9-3.11

Breaking changes#

This version also uses joblib for model saving and loading and drops supported hdf5 files previously handled with the deepdish library as it is no longer actively maintained. This means that 0.8.1 will not be able to load modelssaved with earlier versions of pymer4!

Fixes#

0.8.0#

NOTE#

there was no 0.7.9 release as there were enough major changes to warrant a new minor release version
this version unpins the maximum versions of rpy2 and pandas
if there are install issues with the conda release accompanying this version you should be able to successfully install into a conda environment using pip with the following: conda install 'r-lmerTest' 'r-emmeans' rpy2 -c conda-forge followed by pip install pymer4

Bug fixes#

fixed as issue where Lmer with family='binomial' was not converting logits into probabilities correctly
fixes #79
fixes #88
fixes #113
fixes #114
generally more robust conversion of R types to pandas

New features#

Lm models now support family='binomial' and uses the LogisticRegression class from scikit-learn with no regularization for estimation. Estimates and errors have been verified against the glm implementation in R
new lrt function for estimating likelihood-ratio tests between Lmer models thanks to@dramanica. This replicates the functionality of anova() in R for lmer models.
new .confint() method for Lmer models thanks to @dramanica. This allows computing confidence intervals on 1 or more paramters of an already fit model including random effects which are not computed by default when calling .fit()

0.7.8#

Maintenance release that pins rpy2 >= 3.4.5,< 3.5.1 due to R to Python dataframe conversion issue on recent rpy2 versions that causes a recursion error.
Pending code changes to support rpy2 >= 3.5.1 are tracked on this development branch.
Upcoming releases will drop support for rpy2 < 3.5.X
Clearer error message when making circular predictions using Lmer models

0.7.7#

This version is identical to 0.7.6 but supports R >= 4.1
Installation is also more flexible and includes instructions for using conda-forge and optimized libraries (MKL) for Intel CPUs

0.7.6#

**Bug fixes:**

:   -   fixes an issue in which a `Lmer` model fit using categorical
        predictors would be unable to use `.predict` or would return
        fitted values instead of predictions on new data. random
        effect and fixed effect index names were lost thanks to
        Mario Leaonardo Salinas for discovering this issue

0.7.5#

This version is identical to 0.7.4 and simply exists because a naming conflict that resulted in a failed released to Anaconda cloud. See release notes for 0.7.4 below

0.7.4#

**Compatibility updates:**

:   -   This version drops official support for Python 3.6 and adds
        support for Python 3.9. While 3.6 should still work for the
        most part, development support and testing against this
        version of Python will no longer continue moving forward.

**New features:**

:   -   `utils.result_to_table` function nicely formats the
        `model.coefs` output for a fitted model. The docstring also
        contains instructions on using this in conjunction with the
        [gspread-pandas](https://github.com/aiguofer/gspread-pandas)
        library for \"exporting\" model results to a google sheet

0.7.3#

**Bug fixes:**

:   -   fix issue in which random effect and fixed effect index
        names were lost thanks to
        [\@jcheong0428](https://github.com/jcheong0428) and
        [\@Shotgunosine](https://github.com/Shotgunosine) for the
        quick PRs!

0.7.2#

**Bug fixes:**

:   -   fix bug in which `boot_func` would fail iwth `y=None` and
        `paired=False`

**Compatibility updates:**

:   -   add support for `rpy2>=3.4.3` which handles model matrices
        differently
    -   pin maximum `pandas<1.2`. This is neccesary until our other
        dependency `deepdish` adds support. See [this
        issue](https://github.com/uchicago-cs/deepdish/issues/45)

0.7.1#

**Pymer4 will be on conda as of this release!**

:   -   install with
        `conda install -c ejolly -c defaults -c conda-forge pymer4`
    -   This should make installation much easier
    -   Big thanks to [Tom
        Urbach](https://turbach.github.io/toms_kutaslab_website/)
        for assisting with this!

**Bug fixes:**

:   -   design matrix now handles rfx only models properly
    -   compatibility with the latest version of pandas and rpy2 (as
        of 08/20)
    -   `Lmer.residuals` now save as numpy array rather than
        `R FloatVector`

**New features:**

:   -   `stats.tost_equivalence` now takes a `seed` argument for
        reproducibility

**Result Altering Change:**

:   -   Custom contrasts in `Lmer` models are now expected to be
        specified in *human readable* format. This should be more
        intuitive for most users and is often what users expect from
        R itself, even though that\'s not what it actually does! R
        expects custom contrasts passed to the `contrasts()`
        function to be the *inverse* of the desired contrasts. See
        [this
        vignette](https://rstudio-pubs-static.s3.amazonaws.com/65059_586f394d8eb84f84b1baaf56ffb6b47f.html)
        for more info.
    -   In `Pymer4`, specifying the following contrasts:
        `model.fit(factors = {"Col1": {'A': 1, 'B': -.5, 'C': -.5}}))`
        will estimate the difference between A and the mean of B and
        C as one would expect. Behind the scenes, `Pymer4` is
        performing the inversion operation automatically for R.

Lots of other devops changes to make testing, bug-fixing, development, future releases and overall maintenance much easier. Much of this work has been off-loaded to automated testing and deployment via Travis CI.

0.7.0#

dropped support for versions of rpy2 < 3.0
Result Altering Change: Lm standard errors are now computed using the square-root of the adjusted mean-squared-error (np.sqrt(res.T.dot(res) / (X.shape[0] - X.shape[1]))) rather than the standard deviation of the residuals with DOF adjustment (np.std(res, axis=0, ddof=X.shape[1])). While these produce the same results if an intercept is included in the model, they differ slightly when an intercept is not included. Formerly in the no-intercept case, results from pymer4 would differ slightly from R or statsmodels. This change ensures the results are always identical in all cases.
Result Altering Change: Lm rsquared and adjusted rsquared now take into account whether an intercept is included in the model estimation and adjust accordingly. This is consistent with the behavior of R and statsmodels
Result Altering Change: hc1 is the new default robust estimator for Lm models, changed from hc0
API change: all model residuals are now saved in the model.residuals attribute and were formerly saved in the model.resid attribute. This is to maintain consistency with model.data column names.
New feature: addition of pymer4.stats module for various parametric and non-parametric statistics functions (e.g. permutation testing and bootstrapping)
New feature: addition of pymer4.io module for saving and loading models to disk
New feature: addition of Lm2 models that can perform multi-level modeling by first estimating a separate regression for each group and then performing inference on those estimates. Can perform inference on first-level semi-partial and partial correlation coefficients instead of betas too.
New feature: All model classes now have the ability to rank transform data prior to estimation, see the rank argument of their respective .fit() methods.

**New features for Lm models:**

:   -   `Lm` models can transform coefficients to partial or
        semi-partial correlation coefficients
    -   `Lm` models can also perform weight-least-squares (WLS)
        regression given the weights argument to `.fit()`, with
        optional dof correction via Satterthwaite approximation.
        This is useful for categorical (e.g. group) comparison where
        one does not want to assume equal variance between groups
        (e.g. Welch\'s t-test). This remains an experimental feature
    -   `Lm` models can compute hc1 and hc2 robust standard errors

New documentation look: the look and feel of the docs site has been completely changed which should make getting information much more accessible. Additionally, overview pages have now been turned into downloadable tutorial jupyter notebooks
All methods/functions capable of parallelization now have their default n_jobs set to 1 (i.e. no default parallelization)
Various bug fixes to all models
Automated testing on travis now pins specific r and r-package versions
Switched from lsmeans to emmeans for post-hoc tests because lsmeans is deprecated
Updated interactions with rpy2 api for compatibility with version 3 and higher
Refactored package layout for easier maintainability

0.6.0#

Dropped support for Python 2
upgraded rpy2 dependency version
Added conda installation instructions
Accepted JOSS version

0.5.0#

Lmer models now support all generalized linear model family types supported by lme4 (e.g. poisson, gamma, etc)
Lmer models now support ANOVA tables with support for auto-orthogonalizing factors using the .anova() method
Test statistic inference for Lmer models can now be performed via non-parametric permutation tests that shuffle observations within clusters
Lmer.fit(factors={}) arguments now support custom arbitrary contrasts
New forest plots for visualizing model estimates and confidence intervals via the Lmer.plot_summary() method
More comprehensive documentation with examples of new features
Submission to JOSS

0.4.0#

Added .post_hoc() method to Lmer models
Added .simulate() method to Lmer models
Several bug fixes for Python 3 compatibility

0.3.2#

addition of simulate module

0.2.2#

Official pyipi release

0.2.1#

Support for standard linear regression models
Models include support for robust standard errors, boot-strapped CIs, and permuted inference

0.2.0#

Support for categorical predictors, model predictions, and model plots

0.1.0#

Linear and Logit multi-level models

What’s New

Contents

What’s New#

0.9.3 (Sep ‘25)#

New#

Fixes#

0.9.2 (May ‘25)#

New#

0.9.1 (May ‘25)#

Fixes#

Development#

0.9.0 (May ‘25)#

Summary of changes#

Encompassed Fixes#

Planned features#

0.8.2#

Fixes#

0.8.1#

Compatibility Updates#

Breaking changes#

Fixes#

0.8.0#

NOTE#

Bug fixes#

New features#

0.7.8#

0.7.7#

0.7.6#

0.7.5#

0.7.4#

0.7.3#

0.7.2#

0.7.1#

0.7.0#

0.6.0#

0.5.0#

0.4.0#

0.3.2#

0.2.2#

0.2.1#

0.2.0#

0.1.0#