CausalMBSTS: MBSTS Models for Causal Inference and Forecasting

Infers the causal effect of an intervention on a multivariate response through the use of Multivariate Bayesian Structural Time Series models (MBSTS) as described in Menchetti & Bojinov (2020) <arXiv:2006.12269>. The package also includes functions for model building and forecasting.

Version: 0.1.1
Depends: KFAS, R (≥ 3.5.0)
Imports: CholWishart, forecast, MASS, Matrix, MixMatrix
Suggests: testthat, knitr, rmarkdown
Published: 2021-10-05
Author: Iavor Bojinov [aut], Fiammetta Menchetti [aut, cre], Victoria L. Prince [ctb], Ista Zahn [ctb]
Maintainer: Fiammetta Menchetti <fiammetta.menchetti at gmail.com>
BugReports: https://github.com/FMenchetti/CausalMBSTS/issues
License: GPL (≥ 3)
NeedsCompilation: no
Materials: NEWS
CRAN checks: CausalMBSTS results

Downloads:

Reference manual: CausalMBSTS.pdf
Vignettes: Working example of causal inference with CausalMBSTS package
Package source: CausalMBSTS_0.1.1.tar.gz
Windows binaries: r-devel: CausalMBSTS_0.1.1.zip, r-release: CausalMBSTS_0.1.1.zip, r-oldrel: CausalMBSTS_0.1.1.zip
macOS binaries: r-release (arm64): CausalMBSTS_0.1.0.tgz, r-release (x86_64): CausalMBSTS_0.1.1.tgz, r-oldrel: CausalMBSTS_0.1.1.tgz
Old sources: CausalMBSTS archive

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