wex: Compute the Exact Observation Weights for the Kalman Filter and Smoother

Computes the exact observation weights for the Kalman filter and smoother, based on the method described in Koopman and Harvey (2003) <www.sciencedirect.com/science/article/pii/S0165188902000611>. The package supports in-depth exploration of state-space models, enabling researchers and practitioners to extract meaningful insights from time series data. This functionality is especially valuable in dynamic factor models, where the computed weights can be used to decompose the contributions of individual variables to the latent factors. See the README file for examples.

Version:	0.1.0
Depends:	R (≥ 3.5.0)
Imports:	FKF
Published:	2025-05-09
DOI:	10.32614/CRAN.package.wex
Author:	Tim Ginker [aut, cre, cph]
Maintainer:	Tim Ginker <timginker at gmail.com>
BugReports:	https://github.com/timginker/wex/issues
License:	MIT + file LICENSE
URL:	https://github.com/timginker/wex
NeedsCompilation:	no
Materials:	README, NEWS
In views:	TimeSeries
CRAN checks:	wex results

Documentation:

Reference manual:

wex.html , wex.pdf

Downloads:

Package source:	wex_0.1.0.tar.gz
Windows binaries:	r-devel: wex_0.1.0.zip, r-release: wex_0.1.0.zip, r-oldrel: wex_0.1.0.zip
macOS binaries:	r-release (arm64): wex_0.1.0.tgz, r-oldrel (arm64): wex_0.1.0.tgz, r-release (x86_64): wex_0.1.0.tgz, r-oldrel (x86_64): wex_0.1.0.tgz

Reverse dependencies:

Reverse imports:

cforecast

Linking:

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