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CausalImpact: Inferring Causal Effects using Bayesian Structural Time-Series Models

Implements a Bayesian approach to causal impact estimation in time series, as described in Brodersen et al. (2015) <doi:10.1214/14-AOAS788>. See the package documentation on GitHub <https://google.github.io/CausalImpact/> to get started.

Version: 1.3.0
Depends: bsts (≥ 0.9.0)
Imports: assertthat (≥ 0.2.0), Boom, ggplot2, zoo
Suggests: covr, knitr, rmarkdown, testthat
Published: 2022-11-09
Author: Kay H. Brodersen, Alain Hauser
Maintainer: Alain Hauser <alhauser at google.com>
License: Apache License 2.0 | file LICENSE
Copyright: Copyright (C) 2014-2022 Google, Inc.
URL: https://google.github.io/CausalImpact/
NeedsCompilation: no
Citation: CausalImpact citation info
Materials: README
In views: Bayesian, CausalInference
CRAN checks: CausalImpact results

Documentation:

Reference manual: CausalImpact.pdf
Vignettes: CausalImpact

Downloads:

Package source: CausalImpact_1.3.0.tar.gz
Windows binaries: r-devel: CausalImpact_1.3.0.zip, r-release: CausalImpact_1.3.0.zip, r-oldrel: CausalImpact_1.3.0.zip
macOS binaries: r-release (arm64): CausalImpact_1.3.0.tgz, r-oldrel (arm64): CausalImpact_1.3.0.tgz, r-release (x86_64): CausalImpact_1.3.0.tgz, r-oldrel (x86_64): CausalImpact_1.3.0.tgz
Old sources: CausalImpact archive

Reverse dependencies:

Reverse imports: MarketMatching, SPORTSCausal

Linking:

Please use the canonical form https://CRAN.R-project.org/package=CausalImpact to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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