c(person("Patrick", "Brandt", role=c("aut", "cre"),
email="pbrandt@utdallas.edu"),
person("W. Ryan", "Davis", role=c("ctb")))
Patrick Brandt [aut, cre],
W. Ryan Davis [ctb]

Multivariate Synthetic Control Method Using Time Series
c(person("Martin","Becker", role = c("aut","cre"),
email = "martin.becker@mx.uni-saarland.de"),
person("Stefan","Kloessner", role = c("aut"),
email = "S.Kloessner@mx.uni-saarland.de"),
person("Karline","Soetaert", role = c("com"),
email = "karline.soetaert@nioz.nl"),
person("LAPACK authors", role = "cph"))
stats, utils, optimx, lpSolve, parallel, DEoptim, rgenoud,
ggplot2
Multivariate Synthetic Control Method Using Time Series.
Two 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.
Much effort has been taken to make the implementation as stable as possible
(including edge cases) without losing computational efficiency.
Martin Becker [aut, cre],
Stefan Kloessner [aut],
Karline Soetaert [com],
LAPACK authors [cph]

stats, utils, parallel, lpSolve, ggplot2
Synth, DEoptim, rgenoud, DEoptimR, GenSA, GA, soma, cmaes,
Rmalschains, NMOF, nloptr, hydroPSO, pso, optimx, knitr,
rmarkdown

Synth, DEoptim, rgenoud, DEoptimR, GenSA, GA, soma, cmaes,
Rmalschains, NMOF, nloptr, hydroPSO, pso, LowRankQP, kernlab,
reshape, knitr, rmarkdown

c(person("Martin","Becker", role = c("aut","cre"),
email = "martin.becker@mx.uni-saarland.de"),
person("Stefan","Klößner", role = c("aut"),
email = "S.Kloessner@mx.uni-saarland.de"),
person("Karline","Soetaert", role = c("com"),
email = "karline.soetaert@nioz.nl"),
person("LAPACK authors", role = "cph"))
stats, utils, parallel, lpSolve, ggplot2, lpSolveAPI, Rglpk,
Rdpack
Multivariate Synthetic Control Method Using Time Series.
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.
Martin Becker [aut, cre],
Stefan Klößner [aut],
Karline Soetaert [com],
LAPACK authors [cph]