Multivariate Synthetic Control Method Using Time Series R package MSCMT version 1.4.3 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 Klößner (2018) <doi:10.1016/j.ecosta.2017.08.002>. Software Depends: R (>= 3.2.0) Imports: stats, utils, parallel, lpSolve, ggplot2, lpSolveAPI, Rglpk, Rdpack, rlang Suggests: Synth, DEoptim, rgenoud, DEoptimR, GenSA, GA, soma, cmaes, Rmalschains, NMOF, nloptr, pso, LowRankQP, kernlab, reshape, knitr, rmarkdown Martin Becker <martin.becker@mx.uni-saarland.de> Comprehensive R Archive Network (CRAN) Martin Becker [aut, cre] (ORCID: <https://orcid.org/0000-0003-2336-9751>), Stefan Klößner [aut], Karline Soetaert [com], Jack Dongarra [cph], R.J. Hanson [cph], K.H. Haskell [cph], Cleve Moler [cph], LAPACK authors [cph] GPL 2026-05-26 application/tgz https://CRAN.R-project.org/package=MSCMT doi:10.32614/CRAN.package.MSCMT