Three generalizations of the synthetic control method (which has already an implementation in package 'Synth') are implemented: first, 'MSCMT' allows for using multiple outcome variables, second, time series can be supplied as economic predictors, and third, a well-defined cross-validation approach can be used. Much effort has been taken to make the implementation as stable as possible (including edge cases) without losing computational efficiency. A detailed description of the main algorithms is given in Becker and KlöÃner (2018) <doi:10.1016/j.ecosta.2017.08.002>.
Version: 1.4.0 Depends: R (≥ 3.2.0) Imports: stats, utils, parallel, lpSolve, ggplot2, lpSolveAPI, Rglpk, Rdpack Suggests: Synth, DEoptim, rgenoud, DEoptimR, GenSA, GA, soma, cmaes, Rmalschains, NMOF, nloptr, pso, LowRankQP, kernlab, reshape, knitr, rmarkdown Published: 2024-03-19 DOI: 10.32614/CRAN.package.MSCMT Author: Martin Becker [aut, cre], Stefan KlöÃner [aut], Karline Soetaert [com], Jack Dongarra [cph], R.J. Hanson [cph], K.H. Haskell [cph], Cleve Moler [cph], LAPACK authors [cph] Maintainer: Martin Becker <martin.becker at mx.uni-saarland.de> License: GPL-2 | GPL-3 [expanded from: GPL] Copyright: inst/COPYRIGHTSRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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