Exact Observation Weights for the Kalman Filter and Smoother
Overview
Computes exact observation weights for the Kalman filter and smoother, following Koopman and Harvey (2003) <www.sciencedirect.com/science/article/pii/S0165188902000611>. The package provides tools for analyzing linear Gaussian state-space models, allowing users to quantify the contribution of individual observations to filtered and smoothed state estimates. These weights can be used for interpretation, decomposition, and diagnostic analysis in time series models, including applications such as dynamic factor models. See the README for examples.
- Added KFAS routines that do not require the invertibility of the prediction error variance matrices
- Improved documentation and Readme
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-04
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 2
- Examples that run
- 100%1 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 50%
Downloads
Repository
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Releases over time
3 development-tooling and community-health practices detected across 3 families in the upstream repository
Checks run against github.com/timginker/wex on 2026-10-21.
Dependencies
Installing wex adds 2 packages (2 with compiled code, 564 KB of source tarballs). Heaviest: KFAS 471 KB, FKF 93 KB.
Code & Tests
Datasets
People & History
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- RR 4.6.0 released · 2026-04-24
- 0.1.1Latest
- 0.1.02025-05-09
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-05-09
- Total releases
- 2 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 19 KB / 1 file
- Download size
- 57 KB
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