c(person("Ruida", "Zhong", email = "zrd2017@163.com", role = c("aut", "cre")),
person("Xiaohong", "Chen", email = "eescxh@mail.sysu.edu.cn", role = c("aut", "ctb")),
person("Zhaoli", "Wang", email = "wangzhl@scut.edu.cn", role = c("aut", "ctb")),
person("Chengguang", "Lai", email = "laichg@scut.edu.cn", role = c("aut", "ctb")),
person("Joseph J", "Hamman", role = c("ctb"), comment = "Contributor and maintainer of VIC source code"),
person("Keith", "Cherkauer", role = c("ctb"))
)
Ruida Zhong [aut, cre],
Xiaohong Chen [aut, ctb],
Zhaoli Wang [aut, ctb],
Chengguang Lai [aut, ctb],
Joseph J Hamman [ctb] (Contributor and maintainer of VIC source code),
Keith Cherkauer [ctb]

VIF Regression: A Fast Regression Algorithm For Large Data
This package implements a fast regression algorithm for
building linear model for large data as defined in the paper
"VIF-Regression: A Fast Regression Algorithm for Large Data
(2009)" by Dongyu Lin, Dean P. Foster and Lyle H. Ungar.

This package implements a fast regression algorithm for
building linear model for large data as defined in the paper
"VIF-Regression: A Fast Regression Algorithm for Large Data
(2011), Journal of the American Statistical Association, Vol.
106, No. 493: 232-247" by Dongyu Lin, Dean P. Foster, and Lyle
H. Ungar.

Detecting Change-Points via VIFCP Method
Xiaoping shi, Xiangsheng Wang, Dongwei Wei, Yuehua Wu
Contains a function to support the paper 'A sequential multiple change-point detection
procedure via VIF regression' (in print).

Xiaoping Shi, Xiangsheng Wang, Dongwei Wei, Yuehua Wu
Contains a function to support the paper 'A sequential multiple change-point detection
procedure via VIF regression' (Accepted by Computational Statistics).

Contains a function to support the following paper:
Xiaoping Shi, Xiang-Sheng Wang, Dongwei Wei, Yuehua Wu (2016), <DOI:10.1007/s00180-015-0587-5>,
A sequential multiple change-point detection procedure via VIF regression,
Computational Statistics, 31(2): 671-691.

Variational Bayesian Inference for Genome-Wide Regression
c(person("Akio", "Onogi", role = c("aut", "cre", "cph"), email = "onogiakio@gmail.com"), person("R Core Team", role = "ctb", comment = "Provide Rdynload.h and Boolean.h"), person("Hiroyoshi", "Iwata", role = "cph"), person("Takuji", "Nishimura", role = "ctb", comment = "Developer of Mersenne twister in header1.h"), person("Makoto", "Matsumoto", role = "ctb", comment = "Developer of Mersenne twister in header1.h"), person("STRUCTURE software contributors", role = "ctb", comment="Provide snorm and RNormal functions in header2.h"), person("Alan", "Miller", role = "ctb", comment = "Program mylgamma function in header2.h"), person("Peter", "Beerli", role = "ctb", comment = "Translate mylgamma function in header2.h"))
Conducts linear regression using variational Bayesian inference, particularly optimized for genome-wide association mapping and whole-genome prediction which use a number of DNA markers as the explanatory variables. Provides seven regression models which select the important variables (i.e., the variables related to response variables) among the given explanatory variables in different ways (i.e., model structures).
Akio Onogi [aut, cre, cph],
R Core Team [ctb] (Provide Rdynload.h and Boolean.h),
Hiroyoshi Iwata [cph],
Takuji Nishimura [ctb] (Developer of Mersenne twister in header1.h),
Makoto Matsumoto [ctb] (Developer of Mersenne twister in header1.h),
STRUCTURE software contributors [ctb] (Provide snorm and RNormal
functions in header2.h),
Alan Miller [ctb] (Program mylgamma function in header2.h),
Peter Beerli [ctb] (Translate mylgamma function in header2.h)