RegCombin: Partially Linear Regression under Data Combination

We implement linear regression when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked, based on D'Haultfoeuille, Gaillac, Maurel (2022) <doi:10.3386/w29953>. The package allows for common regressors observed in both datasets, and for various shape constraints on the effect of covariates on the outcome of interest. It also provides the tools to perform a test of point identification. See the associated vignette <https://github.com/cgaillac/RegCombin/blob/master/RegCombin_vignette.pdf> for theory and code examples.

Version:	0.4.1
Imports:	dplyr, kableExtra, snowfall, RationalExp, Hmisc, geometry, pracma
Suggests:	knitr, rmarkdown
Published:	2023-10-16
DOI:	10.32614/CRAN.package.RegCombin
Author:	Xavier D'Haultfoeuille [aut], Christophe Gaillac [aut, cre], Arnaud Maurel [aut]
Maintainer:	Christophe Gaillac <christophe.gaillac at economics.ox.ac.uk>
License:	GPL-3
NeedsCompilation:	no
CRAN checks:	RegCombin results

Documentation:

Reference manual:

RegCombin.pdf

Downloads:

Package source:	RegCombin_0.4.1.tar.gz
Windows binaries:	r-devel: RegCombin_0.4.1.zip, r-release: RegCombin_0.4.1.zip, r-oldrel: RegCombin_0.4.1.zip
macOS binaries:	r-release (arm64): RegCombin_0.4.1.tgz, r-oldrel (arm64): RegCombin_0.4.1.tgz, r-release (x86_64): RegCombin_0.4.1.tgz, r-oldrel (x86_64): RegCombin_0.4.1.tgz
Old sources:	RegCombin archive

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