Package: SIS
Version: 0.7-6
Date: 2015-11-03
Title: Sure Independence Screening
Author: Jianqing Fan, Yang Feng, Diego Franco Saldana, Richard Samworth, Yichao Wu
Maintainer: Diego Franco Saldana <diego@stat.columbia.edu>
Depends: R (>= 3.1.1), glmnet, ncvreg, survival
Description: Variable selection techniques are essential tools for model selection and estimation in high-dimensional statistical models. Through this publicly available package, we provide a unified environment to carry out variable selection using iterative sure independence screening (SIS) and all of its variants in generalized linear models and the Cox proportional hazards model.
License: GPL-2
NeedsCompilation: no
Packaged: 2015-11-06 02:18:37 UTC; df2406
Repository: CRAN
Date/Publication: 2015-11-06 05:49:48
Package: glmvsd
Type: Package
Title: Variable Selection Deviation Measures and Instability Tests for
High-Dimensional Generalized Linear Models
Version: 1.4
Date: 2016-01-06
Author: Ying Nan <nanx0006@gmail.com>, Yanjia Yu <yuxxx748@umn.edu>, Yuhong Yang <yyang@stat.umn.edu>, Yi Yang <yi.yang6@mcgill.ca>
Maintainer: Yi Yang <yi.yang6@mcgill.ca>
Depends: stats, glmnet, ncvreg, MASS, parallel, brglm
Description: Variable selection deviation (VSD) measures and instability tests for high-dimensional model selection methods such as LASSO, SCAD and MCP, etc., to decide whether the sparse patterns identified by those methods are reliable.
License: GPL-2
URL: https://github.com/emeryyi/glmvsd
Packaged: 2016-01-06 23:47:55 UTC; yiyang
Date/Publication: 2016-01-07 13:55:37
NeedsCompilation: no
Repository: CRAN
Package: ExactPath
Type: Package
Title: Exact solution paths for regularized LASSO regressions with L_1
penalty
Version: 1.0
Date: 2013-02-05
Author: Dr. Kai Wang
Maintainer: Kai Wang <kai-wang@uiowa.edu>
Depends: R (>= 2.12), ncvreg, lars
Description: ExactPath implements an algorithm for exact LASSO
solution. Two methods are provided to print and visualize the
whole solution paths. Use ?ExactPath to see an introduction.
Packages ncvreg and lars are required so that their data sets
can be used in examples.
License: GPL (>= 2)
LazyLoad: yes
Packaged: 2013-02-05 15:17:22 UTC; kaiwang
Repository: CRAN
Date/Publication: 2013-02-06 08:27:04
Package: biglasso
Version: 1.0-1
Date: 2016-02-27
Title: Big Lasso: Extending Lasso Model Fitting to Big Data in R
Author: Yaohui Zeng [aut,cre], Patrick Breheny [ctb]
Maintainer: Yaohui Zeng <yaohui-zeng@uiowa.edu>
Description: Extend lasso and elastic-net model fitting for ultrahigh-dimensional, multi-gigabyte data sets that cannot be loaded into memory. Compared to existing lasso-fitting packages, it preserves equivalently fast computation speed but is much more memory-efficient, thus allowing for very powerful big data analysis even with only a single laptop.
License: GPL-2
Depends: bigmemory, Matrix, parallel, ncvreg
Imports: Rcpp (>= 0.12.1), methods
LinkingTo: Rcpp, RcppArmadillo, bigmemory, BH
NeedsCompilation: yes
Packaged: 2016-03-02 05:20:15 UTC; yazeng
Repository: CRAN
Date/Publication: 2016-03-02 11:10:15
providing 
.