wex: Exact Observation Weights for the Kalman Filter and Smoother

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.

Version:	0.1.1
Depends:	R (≥ 3.5.0)
Imports:	FKF, KFAS
Published:	2026-04-04
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_0.1.1.tar.gz
Windows binaries:	r-devel: wex_0.1.1.zip, r-release: wex_0.1.0.zip, r-oldrel: wex_0.1.0.zip
macOS binaries:	r-release (arm64): wex_0.1.1.tgz, r-oldrel (arm64): wex_0.1.0.tgz, r-release (x86_64): wex_0.1.1.tgz, r-oldrel (x86_64): wex_0.1.1.tgz
Old sources:	wex archive

Reverse dependencies:

Reverse imports:

cforecast

Linking:

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