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) <doi:10.48550/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
DOI:	10.32614/CRAN.package.CausalMBSTS
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
In views:	CausalInference
CRAN checks:	CausalMBSTS results

Documentation:

Reference manual:	CausalMBSTS.html , CausalMBSTS.pdf
Vignettes:	Working example of causal inference with CausalMBSTS package (source, R code)

Downloads:

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_0.1.1.tgz, r-oldrel (arm64): CausalMBSTS_0.1.1.tgz, r-release (x86_64): CausalMBSTS_0.1.1.tgz, r-oldrel (x86_64): CausalMBSTS_0.1.1.tgz
Old sources:	CausalMBSTS archive

Linking:

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