person(given = "Xueqin", family = "Wang", email = "wangxq20@ustc.edu.cn", role = c("aut")))
Description: An implementation of best subset selection in generalized linear model and Cox proportional hazard model via the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S. and Wang, X. (2020) <doi:10.18637/jss.v094.i04>The algorithm formulates coefficient parameters and residuals as primal and dual variables and utilizes efficient active set selection strategies based on the complementarity of the primal and dual variables.

Title: Best Subset Selection /Ridge Regression in Linear, Logistic,
person(given = "Liyuan", family = "Hu", email = "1249096170@qq.com", role = c("aut")),
person(given = "Kangkang", family = "Jiang", email = "jiangkk3@mail2.sysu.edu.cn" ,role = c("aut")),
person(given = "Yanhang", family = "Zhang", email = "zhangyh98@ruc.edu.cn", role = c("aut")),
person(given = "Jin", family = "Zhu", email = "zhuj37@mail2.sysu.edu.cn", role = c("aut")),
Description: An implementation of best subset selection in generalized linear model and Cox proportional hazard model via the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S. and Wang, X. (2020) <doi:10.18637/jss.v094.i04>. The algorithm formulates coefficient parameters and residuals as primal and dual variables and utilizes efficient active set selection strategies based on the complementarity of the primal and dual variables.
Imports: Rcpp (>= 1.0.3), Matrix(>= 1.2-6), MASS, pheatmap, survival

Title: JAR Dependency for MCMC Using 'BEAST'
person("Marc A.", "Suchard", email = "msuchard@ucla.edu", role = c("aut","cre","cph")),
person("Andrew", "Rambaut", role = "cph"),
person("Alexei J.", "Drummond", role = "cph")
Description: Provides JAR to perform Markov chain Monte Carlo (MCMC) inference using
the popular Bayesian Evolutionary Analysis by Sampling Trees 'BEAST' software library
of Suchard et al (2018) <doi:10.1093/ve/vey016>. 'BEAST' supports auto-tuning
Metropolis-Hastings, slice, Hamiltonian Monte Carlo and Sequential Monte Carlo
sampling for a large variety of composable standard and phylogenetic statistical
models using high performance computing.  By placing the 'BEAST' JAR in this package,
we offer an efficient distribution system for 'BEAST' use by other R packages using
Author: Marc A. Suchard [aut, cre, cph],
Maintainer: Marc A. Suchard <msuchard@ucla.edu>