Metadata-Version: 2.0
Name: modest
Version: 0.1a11
Summary: A modular estimation library
Home-page: https://modular-estimator.readthedocs.io/en/latest/index.html
Author: Joel Runnels
Author-email: runne010@umn.edu
License: MIT
Platform: UNKNOWN
Requires-Dist: Pint
Requires-Dist: PyYAML
Requires-Dist: astropy
Requires-Dist: datetime
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: pyquaternion
Requires-Dist: requests
Requires-Dist: scipy
Requires-Dist: skyfield

Modular Estimator (modest) is a package designed to help facilitate the implementation of a variety of estimation algorithms with a minimum amount of "boiler-plate" code.  Modular estimator is designed around modularity, meaning that individual pieces of the estimation algorithm are build separately.  This allows for a high degree of flexibility in the configuration of the estimator, as well as for rigorous testing of sub-components in a controlled environment.

Some things the modest package offers include:

- A framework for designing estimators in a modular fashion with easily interchangeable sub-components
- A variety of built-in estimation algorithms, including an extended Kalman filter (EKF), a maximum likelihood (ML) estimator , and a joint probabilistic data association filter (JPDAF)
- The ability to easily compare performance between different estimation algorithms

Please note that modest is currently still in the "alpha" development phase: this means that there are large portions of the code which are still somewhat undocumented/untested.  Bug reports and suggestions for feature inclusion are welcomed!

