CRAN Package Check Results for Package GSparO

Last updated on 2024-04-22 19:56:14 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.0 5.91 51.22 57.13 NOTE
r-devel-linux-x86_64-debian-gcc 1.0 4.26 38.98 43.24 NOTE
r-devel-linux-x86_64-fedora-clang 1.0 69.68 NOTE
r-devel-linux-x86_64-fedora-gcc 1.0 68.00 NOTE
r-prerel-macos-arm64 1.0 25.00 NOTE
r-prerel-windows-x86_64 1.0 6.00 57.00 63.00 NOTE
r-patched-linux-x86_64 1.0 5.47 51.20 56.67 NOTE
r-release-linux-x86_64 1.0 4.96 51.04 56.00 NOTE
r-release-macos-arm64 1.0 29.00 NOTE
r-release-macos-x86_64 1.0 37.00 NOTE
r-release-windows-x86_64 1.0 6.00 64.00 70.00 NOTE
r-oldrel-macos-arm64 1.0 24.00 NOTE
r-oldrel-windows-x86_64 1.0 8.00 69.00 77.00 NOTE

Check Details

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-linux-x86_64-fedora-gcc, r-prerel-macos-arm64, r-prerel-windows-x86_64, r-patched-linux-x86_64

Version: 1.0
Check: LazyData
Result: NOTE 'LazyData' is specified without a 'data' directory Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-prerel-macos-arm64, r-prerel-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, r-oldrel-macos-arm64, r-oldrel-windows-x86_64