In a post last year, I unveiled synloc, my Python package designed for sequential and local estimation of distributions to generate synthetic data from a sample. The algorithm that powers synloc is flexible enough to integrate with both parametric and nonparametric distributions, and can be easily installed via PyPI with the command pip install synloc.
It recently came to my attention that the synloc package has achieved a milestone: it has been downloaded over 2000 times! This accomplishment is especially noteworthy given the package is still in its pre-alpha stage and requires further refinement.
The spike in downloads was an unexpected but pleasant surprise. I strongly believe that the algorithmic innovations in synloc have helped it carve out its unique place in the landscape of Python packages, particularly due to its superiority over the synthpop package in R.
Notably, synloc excels in its ability to naturally trim outliers in datasets, a feature that enhances the robustness of any analysis. Furthermore, I have demonstrated its proficiency in replicating nonlinear and multi-modal distributions in my paper.