There are a couple of metrics that I would look at as the first pass:
- Version number - usually an indicator of the maturity.
- Presence & completeness of Metadata on the PyPi page.
- Information on the PyPi page
- Information on the project home page
In your example the search for Kalman Filter returns:
- Kalman 0.1.3 16 Kalman Filters
- filterpy 1.0.0 14 Kalman filtering and optimal estimation library
- pykalman 0.9.5 12 An implementation of the Kalman Filter, Kalman Smoother, and EM algorithm in Python
- adskalman 0.3.6 8 Kalman filtering routine
- ikalman 0.2.0 8 Python bindings for the ikalman c library
- calman 0.0.1 4 Stay calm with kalman filters
- KF 0.1.3 4 Fund performance tracker
- PyBayes 0.3 4 Python library for recursive Bayesian estimation (Bayesian filtering)
- pydlm 0.1.1.9 4 A python library for the Bayesian dynamic linear model for time series modeling
- control 0.7.0 2 Python control systems library
- impyute 0.0.4 2 Library of the different imputation algorithms; methods for dealing with ambiguity and handling missing data.
- norma 0.1.1 2
- pyda 1.0 2 pyda is a general object-oriented data assimilation package
- scikit-kinematics 0.6.0 2 Python utilites for movements in 3d space
- scikits.statsmodels 0.3.1 2 Statistical computations and models for use with SciPy
- starman 1.0.0 2 A library which implements algorithms of use when trying to track the true state of one or more systems over time in the presence of noisy observations.
- testbeam_analysis 0.0.1 2 A light weight test beam analysis in Python and C++.
- tracktotrip 0.4.6 2 Track processing library
Of these I would first look at filterpy and pykalman.
This has good documentation on the pypi page, complete metadata, a documentation site on pythonhosted and checking out the GitHub page has had 339 commits by 11 contributors, 22 releases, an active issue tracker with more closed than open tickets.
No documentation on the PyPi page, minimal metadata, a link to some documentation but not to the source, on locating the source I notice 40 commits from 5 contributors, more open tickets than closed and the installation instructions using easy_install rather than pip all of which lead me to doubts about the maturity of the project.
A quick scan of the others show that there is reasonably complete information on the PyPi pages of PyBayes of scikits.statsmodels so they may also be worth a look. Some the others top level summary seem to indicate that they may mention the term but are specialised in areas that are not needed.
I would then read in more depth about filterpy to see if it met my needs.