• using R version 4.4.2 (2024-10-31 ucrt)
• using platform: x86_64-w64-mingw32
• R was compiled by     gcc.exe (GCC) 13.3.0     GNU Fortran (GCC) 13.3.0
• running under: Windows Server 2022 x64 (build 20348)
• using session charset: UTF-8
• checking for file 'GSparO/DESCRIPTION' ... OK
• this is package 'GSparO' version '1.0'
• package encoding: UTF-8
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• checking whether package 'GSparO' can be installed ... OK See the install log for details.
• checking installed package size ... OK
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• checking DESCRIPTION meta-information ... OK
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• checking R code for possible problems ... [3s] OK
• checking Rd files ... [0s] 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)].        | ^
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• checking examples ... [7s] OK
• checking PDF version of manual ... [20s] OK
• checking HTML version of manual ... [1s] OK
• DONE

Status: 2 NOTEs