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grep rough audit - static analysis tool
v2.8 written by @Wireghoul
=================================[justanotherhacker.com]===
r-cran-bradleyterry2-1.1-2/vignettes/BradleyTerry.Rnw-100-\end{equation}
r-cran-bradleyterry2-1.1-2/vignettes/BradleyTerry.Rnw:101:where $\lambda_i=\log\alpha_i$ for all $i$. Thus, assuming independence of all
r-cran-bradleyterry2-1.1-2/vignettes/BradleyTerry.Rnw-102-contests, the parameters $\{\lambda_i\}$ can be estimated by
##############################################
r-cran-bradleyterry2-1.1-2/vignettes/BradleyTerry.Rnw-375-\end{equation}
r-cran-bradleyterry2-1.1-2/vignettes/BradleyTerry.Rnw:376:where $z=1$ if $i$ has the supposed advantage and $z=-1$ if $j$ has it. (If the
r-cran-bradleyterry2-1.1-2/vignettes/BradleyTerry.Rnw-377-`advantage' is in fact a disadvantage, $\delta$ will be negative.) The scores
##############################################
r-cran-bradleyterry2-1.1-2/R/CEMS.R-14-#' @docType data
r-cran-bradleyterry2-1.1-2/R/CEMS.R:15:#' @format A list containing three data frames, `CEMS$preferences`,
r-cran-bradleyterry2-1.1-2/R/CEMS.R:16:#' `CEMS$students` and `CEMS$schools`.
r-cran-bradleyterry2-1.1-2/R/CEMS.R-17-#'
r-cran-bradleyterry2-1.1-2/R/CEMS.R:18:#' The `CEMS$preferences` data frame has `303 * 15 = 4505`
r-cran-bradleyterry2-1.1-2/R/CEMS.R-19-#' observations (15 possible comparisons, for each of 303 students) on the
##############################################
r-cran-bradleyterry2-1.1-2/R/CEMS.R-36-#'
r-cran-bradleyterry2-1.1-2/R/CEMS.R:37:#' The `CEMS$students` data frame has 303 observations (one for each
r-cran-bradleyterry2-1.1-2/R/CEMS.R-38-#' student) on the following 8 variables: \describe{
##############################################
r-cran-bradleyterry2-1.1-2/R/CEMS.R-58-#'
r-cran-bradleyterry2-1.1-2/R/CEMS.R:59:#' The `CEMS$schools` data frame has 6 observations (one for each
r-cran-bradleyterry2-1.1-2/R/CEMS.R-60-#' management school) on the following 7 variables: \describe{
##############################################
r-cran-bradleyterry2-1.1-2/R/flatlizards.R-24-#' @format This dataset is a list containing two data frames:
r-cran-bradleyterry2-1.1-2/R/flatlizards.R:25:#' `flatlizards$contests` and `flatlizards$predictors`.
r-cran-bradleyterry2-1.1-2/R/flatlizards.R-26-#'
r-cran-bradleyterry2-1.1-2/R/flatlizards.R:27:#' The `flatlizards$contests` data frame has 100 observations on the
r-cran-bradleyterry2-1.1-2/R/flatlizards.R-28-#' following 2 variables: \describe{
##############################################
r-cran-bradleyterry2-1.1-2/R/flatlizards.R-33-#'
r-cran-bradleyterry2-1.1-2/R/flatlizards.R:34:#' The `flatlizards$predictors` data frame has 77 observations (one for
r-cran-bradleyterry2-1.1-2/R/flatlizards.R-35-#' each of the 77 lizards) on the following 18 variables: \describe{
##############################################
r-cran-bradleyterry2-1.1-2/R/BTabilities.R-8-#' abilities are computed from the terms of the fitted model that involve
r-cran-bradleyterry2-1.1-2/R/BTabilities.R:9:#' player covariates only (those indexed by `model$id` in the model
r-cran-bradleyterry2-1.1-2/R/BTabilities.R-10-#' formula). Thus parameters in any other terms are assumed to be zero. If one
##############################################
r-cran-bradleyterry2-1.1-2/R/sound.fields.R-12-#' @docType data
r-cran-bradleyterry2-1.1-2/R/sound.fields.R:13:#' @format A list containing two data frames, `sound.fields$comparisons`,
r-cran-bradleyterry2-1.1-2/R/sound.fields.R:14:#' and `sound.fields$design`.
r-cran-bradleyterry2-1.1-2/R/sound.fields.R-15-#'
r-cran-bradleyterry2-1.1-2/R/sound.fields.R:16:#' The `sound.fields$comparisons` data frame has 84 observations on the
r-cran-bradleyterry2-1.1-2/R/sound.fields.R-17-#' following 8 variables: \describe{
##############################################
r-cran-bradleyterry2-1.1-2/R/sound.fields.R-34-#'
r-cran-bradleyterry2-1.1-2/R/sound.fields.R:35:#' The `sound.fields$design` data frame has 8 observations (one for each
r-cran-bradleyterry2-1.1-2/R/sound.fields.R-36-#' of the sound fields compared in the experiment) on the following 3
##############################################
r-cran-bradleyterry2-1.1-2/R/glmmPQL.R-149- if (!is.null(modelCall$subset))
r-cran-bradleyterry2-1.1-2/R/glmmPQL.R:150: Z <- random[eval(modelCall$subset, data, parent.frame()),]
r-cran-bradleyterry2-1.1-2/R/glmmPQL.R-151- else Z <- random
##############################################
r-cran-bradleyterry2-1.1-2/R/springall.R-14-#' @docType data
r-cran-bradleyterry2-1.1-2/R/springall.R:15:#' @format A list containing two data frames, `springall$contests` and
r-cran-bradleyterry2-1.1-2/R/springall.R:16:#' `springall$predictors`.
r-cran-bradleyterry2-1.1-2/R/springall.R-17-#'
r-cran-bradleyterry2-1.1-2/R/springall.R:18:#' The `springall$contests` data frame has 36 observations (one for each
r-cran-bradleyterry2-1.1-2/R/springall.R-19-#' possible pairwise comparison of the 9 treatments) on the following 7
##############################################
r-cran-bradleyterry2-1.1-2/R/chameleons.R-13-#' @docType data
r-cran-bradleyterry2-1.1-2/R/chameleons.R:14:#' @format A list containing three data frames: `chameleons$winner`,
r-cran-bradleyterry2-1.1-2/R/chameleons.R:15:#' `chameleons$loser` and `chameleons$predictors`.
r-cran-bradleyterry2-1.1-2/R/chameleons.R-16-#'
r-cran-bradleyterry2-1.1-2/R/chameleons.R:17:#' The `chameleons$winner` and `chameleons$loser` data frames each
r-cran-bradleyterry2-1.1-2/R/chameleons.R-18-#' have 106 observations (one per contest) on the following 4 variables:
##############################################
r-cran-bradleyterry2-1.1-2/R/chameleons.R-29-#'
r-cran-bradleyterry2-1.1-2/R/chameleons.R:30:#' The `chameleons$predictors` data frame has 35 observations, one for
r-cran-bradleyterry2-1.1-2/R/chameleons.R-31-#' each male involved in the contests, on the following 7 variables:
##############################################
r-cran-bradleyterry2-1.1-2/R/predict.BTglmmPQL.R-92- if (!is.null(object$call$offset))
r-cran-bradleyterry2-1.1-2/R/predict.BTglmmPQL.R:93: offset <- offset + eval(object$call$offset, newdata)
r-cran-bradleyterry2-1.1-2/R/predict.BTglmmPQL.R-94- }
##############################################
r-cran-bradleyterry2-1.1-2/inst/doc/BradleyTerry.Rnw-100-\end{equation}
r-cran-bradleyterry2-1.1-2/inst/doc/BradleyTerry.Rnw:101:where $\lambda_i=\log\alpha_i$ for all $i$. Thus, assuming independence of all
r-cran-bradleyterry2-1.1-2/inst/doc/BradleyTerry.Rnw-102-contests, the parameters $\{\lambda_i\}$ can be estimated by
##############################################
r-cran-bradleyterry2-1.1-2/inst/doc/BradleyTerry.Rnw-375-\end{equation}
r-cran-bradleyterry2-1.1-2/inst/doc/BradleyTerry.Rnw:376:where $z=1$ if $i$ has the supposed advantage and $z=-1$ if $j$ has it. (If the
r-cran-bradleyterry2-1.1-2/inst/doc/BradleyTerry.Rnw-377-`advantage' is in fact a disadvantage, $\delta$ will be negative.) The scores
##############################################
r-cran-bradleyterry2-1.1-2/debian/tests/run-unit-test-6-if [ "$AUTOPKGTEST_TMP" = "" ] ; then
r-cran-bradleyterry2-1.1-2/debian/tests/run-unit-test:7: AUTOPKGTEST_TMP=`mktemp -d /tmp/${debname}-test.XXXXXX`
r-cran-bradleyterry2-1.1-2/debian/tests/run-unit-test-8- trap "rm -rf $AUTOPKGTEST_TMP" 0 INT QUIT ABRT PIPE TERM