estimator: State-space estimation algorithms such as Kalman Filters
The goal of this library is to simplify implementation and use of state-space estimation algorithms, such as Kalman Filters. The interface for constructing models is isolated as much as possible from the specifics of a given algorithm, so swapping out a Kalman Filter for a Bayesian Particle Filter should involve a minimum of effort.
This implementation is designed to support symbolic types, such as from sbv or ivory. As a result you can generate code in another language, such as C, from a model written using this package; or run static analyses on your model.
Also included is a sophisticated sensor fusion example in Numeric.Estimator.Model.SensorFusion, which may be useful in its own right.
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- estimator-1.2.0.0.tar.gz [browse] (Cabal source package)
- Package description (as included in the package)
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Versions [RSS] | 1.0.0, 1.0.0.1, 1.1.0.0, 1.1.0.1, 1.2.0.0 |
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Dependencies | ad (>=4.2), base (>=4.6 && <5), distributive (>=0.4), lens (>=4.6), linear (>=1.16), reflection (>=1.5) [details] |
Tested with | ghc ==7.6.3, ghc ==7.8.4, ghc ==7.10.2, ghc ==8.0.1 |
License | BSD-3-Clause |
Copyright | 2014-2016 Galois, Inc. |
Author | Jamey Sharp |
Maintainer | smaccm@galois.com |
Category | Math, Numerical, Statistics |
Home page | https://github.com/GaloisInc/estimator |
Bug tracker | https://github.com/GaloisInc/estimator/issues |
Source repo | this: git clone https://github.com/GaloisInc/estimator(tag 1.2.0.0) |
Uploaded | by AdamFoltzer at 2016-07-19T19:29:48Z |
Distributions | NixOS:1.2.0.0 |
Reverse Dependencies | 1 direct, 0 indirect [details] |
Downloads | 4042 total (15 in the last 30 days) |
Rating | (no votes yet) [estimated by Bayesian average] |
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Status | Docs available [build log] Last success reported on 2016-07-19 [all 1 reports] |