Last updated on 2024-12-26 07:51:07 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0 | 4.29 | 39.71 | 44.00 | NOTE | |
| r-devel-linux-x86_64-debian-gcc | 1.0 | 3.82 | 28.60 | 32.42 | NOTE | |
| r-devel-linux-x86_64-fedora-clang | 1.0 | 70.58 | NOTE | |||
| r-devel-windows-x86_64 | 1.0 | 6.00 | 57.00 | 63.00 | NOTE | |
| r-patched-linux-x86_64 | 1.0 | 4.17 | 38.94 | 43.11 | NOTE | |
| r-release-linux-x86_64 | 1.0 | 3.78 | 38.99 | 42.77 | NOTE | |
| r-release-macos-arm64 | 1.0 | 25.00 | NOTE | |||
| r-release-macos-x86_64 | 1.0 | 47.00 | NOTE | |||
| r-release-windows-x86_64 | 1.0 | 6.00 | 57.00 | 63.00 | NOTE | |
| r-oldrel-macos-arm64 | 1.0 | 29.00 | NOTE | |||
| r-oldrel-macos-x86_64 | 1.0 | 37.00 | NOTE | |||
| r-oldrel-windows-x86_64 | 1.0 | 7.00 | 64.00 | 71.00 | NOTE |
Version: 1.0
Check: Rd files
Result: NOTE
checkRd: (-1) GSparO.Rd:23: Lost braces; missing escapes or markup?
23 | Group sparse optimization (GSparO) for least squares regression by using the proximal gradient algorithm to solve the L_{2,1/2} regularization model.
| ^
checkRd: (-1) GSparO.Rd:26: Lost braces; missing escapes or markup?
26 | GSparO is group sparse optimization for least squares regression described in [Hu et al(2017)], in which the proximal gradient algorithm is implemented to solve the L_{2,1/2} regularization model. GSparO is an iterative algorithm consisting of a gradient step for the least squares regression and a proximal steps for the L_{2,1/2} penalty, which is analytically formulated in this function. Also, GSparO can solve sparse variable selection problem in absence of group structure. In particular, setting group in GSparO be a vector of ones, GSparO is reduced to the iterative half thresholding algorithm introduced in [Xu et al (2012)].
| ^
checkRd: (-1) GSparO.Rd:26: Lost braces; missing escapes or markup?
26 | GSparO is group sparse optimization for least squares regression described in [Hu et al(2017)], in which the proximal gradient algorithm is implemented to solve the L_{2,1/2} regularization model. GSparO is an iterative algorithm consisting of a gradient step for the least squares regression and a proximal steps for the L_{2,1/2} penalty, which is analytically formulated in this function. Also, GSparO can solve sparse variable selection problem in absence of group structure. In particular, setting group in GSparO be a vector of ones, GSparO is reduced to the iterative half thresholding algorithm introduced in [Xu et al (2012)].
| ^
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64
Version: 1.0
Check: LazyData
Result: NOTE
'LazyData' is specified without a 'data' directory
Flavors: r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64