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