{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"pykalman provides Kalman filtering, smoothing, and expectation-maximization algorithms for state estimation in time series and sequential data, supporting linear and nonlinear dynamical systems.","skillfed_tags":["state-estimation","time-series","signal-processing"],"use_cases":["Estimate position and velocity from noisy GPS or sensor measurements in tracking applications.","Smooth noisy time-series data (e.g., stock prices, sensor readings) using forward-backward filtering.","Learn optimal filter parameters automatically from observed data using the EM algorithm.","Handle nonlinear state transitions and observation models with the Unscented Kalman Filter.","Perform online state estimation by updating filter state incrementally as new measurements arrive."],"what_it_does":"pykalman is a Python library for Kalman filtering and smoothing that solves the problem of estimating hidden state in linear and nonlinear dynamical systems from noisy observations. It implements the classical Kalman Filter for linear systems, the Unscented Kalman Filter for nonlinear dynamics, and an EM algorithm for automatic parameter learning. The library also supports numerically robust square-root filter variants and can handle missing measurements via masked arrays.\n\nDevelopers use pykalman for tracking, signal smoothing, and state estimation tasks in time series analysis. It depends on numpy for array operations, scipy for numerical routines, scikit-base for base classes, and packaging for version handling. The package is actively maintained, supports Python 3.10 through 3.14, and has been in development since 2012.","worth_installing":"Yes. pykalman is worth installing for any project requiring Kalman filtering or state estimation. It has low install friction, active maintenance, no known vulnerabilities, permissive BSD licensing for PyPI users, and a stable API with examples and documentation. The dependency footprint (numpy, scipy, scikit-base, packaging) is standard in scientific Python. Use it unless you need specialized variants (e.g., particle filters, adaptive Kalman filters) not provided here."},"id":"pykalman","links":{"html":"https://skillfed.io/packages/pykalman","md":"https://skillfed.io/packages/pykalman.md","pypi":"https://pypi.org/project/pykalman/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-01-31","license_spdx":null,"license_treatment":"permissive","name":"pykalman","python_support":"supports_current","summary":"An implementation of the Kalman Filter, Kalman Smoother, and EM algorithm in Python"},"popularity":{"monthly_downloads":264522,"position":8336,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.11.2"}