• using R version 4.4.2 (2024-10-31)
• using platform: x86_64-pc-linux-gnu
• R was compiled by     gcc-14 (Debian 14.2.0-6) 14.2.0     GNU Fortran (Debian 14.2.0-6) 14.2.0
• running under: Debian GNU/Linux trixie/sid
• using session charset: UTF-8
• checking for file ‘GSparO/DESCRIPTION’ ... OK
• this is package ‘GSparO’ version ‘1.0’
• package encoding: UTF-8
• checking package namespace information ... OK
• checking package dependencies ... OK
• checking if this is a source package ... OK
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• checking whether package ‘GSparO’ can be installed ... OK See the install log for details.
• checking package directory ... OK
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• checking DESCRIPTION meta-information ... OK
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• checking R code for possible problems ... [6s/8s] OK
• checking Rd files ... [0s/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 for unstated dependencies in examples ... OK
• checking LazyData ... NOTE   'LazyData' is specified without a 'data' directory
• checking examples ... [7s/10s] OK
• checking PDF version of manual ... [4s/6s] OK
• checking HTML version of manual ... [0s/0s] OK
• checking for non-standard things in the check directory ... OK
• DONE

Status: 2 NOTEs