Imports: KernSmooth, xtable, coda, bit, mvtnorm, lattice
Bayesian Vector Autoregression (VAR) models and Markov-switching
Bayesian VAR (MSBVAR).  Functions for reduced
methods for the generating posterior inferences for these models,
from Wishart and singular multivariate normal densities.  Current
version includes functionality to build and evaluate models with

Title: Multivariate Synthetic Control Method Using Time Series
Authors@R: c(person("Martin","Becker", role = c("aut","cre"),
Imports: stats, utils, optimx, lpSolve, parallel, DEoptim, rgenoud,
Description: 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
Much effort has been taken to make the implementation as stable as possible
(including edge cases) without losing computational efficiency.

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

Rmalschains, NMOF, nloptr, hydroPSO, pso, LowRankQP, kernlab,

Imports: stats, utils, parallel, lpSolve, ggplot2, lpSolveAPI, Rglpk,
Three generalizations of the synthetic control method (which has already an
economic predictors, and third, a well-defined cross-validation approach can be used.

Description: 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
A detailed description of the main algorithms is given in
Author: Martin Becker [aut, cre] (<https://orcid.org/0000-0003-2336-9751>),

Package: MSCquartets
Title: Analyzing Gene Tree Quartets under the Multi-Species Coalescent
Version: 1.0.5
Authors@R: c(person("Elizabeth", "Allman", email ="e.allman@alaska.edu", role = "aut"),
person("Hector", "Banos", email = "hbanos@gatech.edu", role = "aut"),
person("Jonathan", "Mitchell", email = "jonathanmitchell88@gmail.com", role = "aut"),
person("John", "Rhodes", email = "j.rhodes@alaska.edu", role = c("aut", "cre")) )
Description: Methods for analyzing and using quartets displayed on a collection of gene trees,
primarily to make inferences about the species tree or network under the multi-species
coalescent model. These include quartet hypothesis tests for the model, as developed by
Mitchell et al. (2019) <doi:10.1214/19-EJS1576>, the species tree inference routines
based on quartet distances of Rhodes (2019) <doi:10.1109/TCBB.2019.2917204> and
Yourdkhani and Rhodes (2019), and the NANUQ algorithm for inference of level-1
species networks of Allman et al. (2019) <arXiv:1905.07050>.
License: MIT + file LICENSE
Imports: RandomFieldsUtils, zipfR, graphics, stats, Rdpack
RdMacros: Rdpack
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-12-15 19:21:29 UTC; johnrhodes
Author: Elizabeth Allman [aut],
Hector Banos [aut],
Jonathan Mitchell [aut],
John Rhodes [aut, cre]
Maintainer: John Rhodes <j.rhodes@alaska.edu>
Depends: R (>= 3.2.0), ape (>= 5.0), phangorn
Repository: CRAN
Date/Publication: 2019-12-16 11:50:13 UTC