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• using platform: x86_64-w64-mingw32
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• running under: Windows Server 2022 x64 (build 20348)
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• this is package 'GSparO' version '1.0'
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• checking Rd files ... [1s] 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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• DONE

Status: 1 NOTE