- using R version 4.4.0 alpha (2024-03-31 r86238)
- using platform: aarch64-apple-darwin20
- R was compiled by
Apple clang version 14.0.0 (clang-1400.0.29.202)
GNU Fortran (GCC) 12.2.0
- running under: macOS Ventura 13.4
- 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 ... [3s/3s] OK
See the install log for details.
- checking installed package size ... OK
- checking package directory ... OK
- checking DESCRIPTION meta-information ... OK
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- checking Rd files ... [0s/0s] 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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'LazyData' is specified without a 'data' directory
- checking examples ... [2s/2s] OK
- checking PDF version of manual ... [5s/5s] OK
- DONE
Status: 2 NOTEs
- using check arguments '--no-clean-on-error '