• 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 ‘lcpm/DESCRIPTION’ ... OK
• checking extension type ... Package
• this is package ‘lcpm’ version ‘0.1.1’
• package encoding: UTF-8
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• checking whether package ‘lcpm’ 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 ... [10s/12s] OK
• checking Rd files ... [0s/1s] NOTE checkRd: (-1) lcpm.Rd:52: Lost braces; missing escapes or markup?     52 | \code{lcpm} provides the maximum likelihood estimate for ordinal outcomes (J>2 categories) and a Generalized Linear Model (GLM) with the log link without the assumption of proportionality. That is, lcpm determines the MLE for log[P(y <= j)]= cut_j + X beta_j subject to [cut_{j-1} + X beta_{j-1} <= cut_j + X beta_j] and [cut_j + X beta_j <=0]. This implementation uses \code{\link{constrOptim}} to determine the MLE and so the results account for the restricted parameter space.        | ^ checkRd: (-1) lcpm.Rd:52: Lost braces; missing escapes or markup?     52 | \code{lcpm} provides the maximum likelihood estimate for ordinal outcomes (J>2 categories) and a Generalized Linear Model (GLM) with the log link without the assumption of proportionality. That is, lcpm determines the MLE for log[P(y <= j)]= cut_j + X beta_j subject to [cut_{j-1} + X beta_{j-1} <= cut_j + X beta_j] and [cut_j + X beta_j <=0]. This implementation uses \code{\link{constrOptim}} to determine the MLE and so the results account for the restricted parameter space.        | ^ checkRd: (-1) ppm.Rd:51: Lost braces; missing escapes or markup?     51 | \code{ppm} provides the maximum likelihood estimate for ordinal outcomes (J>2 categories) and a Generalized Linear Model with the log link with the assumption of proportionality. That is, ppm determines the MLE for log[P(y <= j)]= cut_j + X beta subject to [cut_{j-1} <= cut_j ] and [cut_j + X beta <=0]. This implementation uses \code{\link{constrOptim}} to determine the MLE and so the results should correctly account for the restricted parameter space. A proposed test for proportionality is included in \code{\link{lcpm}}.        | ^
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• checking for non-standard things in the check directory ... OK
• DONE

Status: 2 NOTEs