0.9.2

v0.9.2

prep for release

0.9.1

Release notes

0.9.0

This release involves a backward incompatible rewrite of the entire library with a new API. For a full list of changes please see: https://eshinjolly.com/pymer4/pages/new.html

The previous version of pymer4 is available on the 0.8.x branch for continued community contributions if desired.

Encompassed fixes

• #61
• #71
• #99
• #102
• #124
• #130
• #132
• #134
• #139

0.8.2

Fixes

• Fixes issue in LogisticRegression API name change

v0.8.1

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for model saving and loading and drops supported hdf5 files previously handled with the deepdish library as it is no longer actively maintained.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 models saved with earlier versions of pymer4!

Fixes

• #119
• #122
• #125

v0.8.0

This is a minor release that adds supporting for logistic Lm models, likelihood ratio tests for Lmer models, fixes numerous bugs.

See the full changelog here

If you have trouble installing from conda or a pre-built conda package is not available you can install using pip by first creating a new conda environment:

conda create -n pymer4 python=3.8 'r-lmerTest' 'r-emmeans' rpy2 -c conda-forge
conda activate pymer4
pip install -r requirements.txt
pip install . 

v0.7.8

• Maintenance release that pins rpy2 >= 3.4.5,< 3.5.1 due to R -> 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

v0.7.7

• 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
• Default installations via conda-forge will use openblas instead of mkl

v0.7.6

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. Thanks to Mario Leaonardo Salinas for discovering this issue

Behind-the-scenes

• All conda packages for this release make use of the Intel MKL libraries which may result in slight estimate differences and fit times. While this was likely already happening before, it has been made explicit in this release, but is subject to change in the future.
• All conda packages also install R<4.1.1 which has some new functionality regarding namespaces that are not yet compatible with pymer4

v0.7.5

This release is identical to 0.7.4 and only exists because of a naming conflict that prevented uploading Python 3.9 support to Anaconda cloud. It also entails infrastructure changes as Travis CI has been deprecated in favor of Github Actions