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              grep rough audit - static analysis tool
                  v2.8 written by @Wireghoul
=================================[justanotherhacker.com]===
r-cran-brms-2.14.4/vignettes/brms_threading.Rmd-252-varying intercept model with $`r N`$ data observation which are grouped into
r-cran-brms-2.14.4/vignettes/brms_threading.Rmd:253:$`r G`$ groups. Each data item has $`r P`$ continuous covariates. The
r-cran-brms-2.14.4/vignettes/brms_threading.Rmd-254-simulation code for the fake data can be found in the appendix and it's first
##############################################
r-cran-brms-2.14.4/vignettes/brms_customfamilies.Rmd-61-model, which will serve as our baseline model. For observed number of events $y$
r-cran-brms-2.14.4/vignettes/brms_customfamilies.Rmd:62:(`incidence` in our case) and total number of trials $T$ (`size`), the
r-cran-brms-2.14.4/vignettes/brms_customfamilies.Rmd-63-probability mass function of the binomial distribution is defined as
##############################################
r-cran-brms-2.14.4/vignettes/brms_customfamilies.Rmd-252-[^phi]: The presented post-processing functions need to be adjusted if you
r-cran-brms-2.14.4/vignettes/brms_customfamilies.Rmd:253:predict `phi` in your model as well by writing `phi <- prep$dpars$phi[, i]`.
r-cran-brms-2.14.4/vignettes/brms_customfamilies.Rmd-254-If you want to support `pointwise` evaluation as well, please write
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r-cran-brms-2.14.4/vignettes/brms_families.Rmd-208-$$
r-cran-brms-2.14.4/vignettes/brms_families.Rmd:209:where $\rho_p$ is given by $\rho_p(x) = x (p - I_{x < 0})$ and $I_A$ is the
r-cran-brms-2.14.4/vignettes/brms_families.Rmd-210-indicator function of set $A$. The parameter $\sigma$ is a positive scale
##############################################
r-cran-brms-2.14.4/R/data-predictor.R-133-      if (new) {
r-cran-brms-2.14.4/R/data-predictor.R:134:        # prepare rasm for use with new data
r-cran-brms-2.14.4/R/data-predictor.R:135:        rasm <- s2rPred(sm, data)
r-cran-brms-2.14.4/R/data-predictor.R-136-      } else {
r-cran-brms-2.14.4/R/data-predictor.R:137:        rasm <- mgcv::smooth2random(sm, names(data), type = 2)
r-cran-brms-2.14.4/R/data-predictor.R-138-      }
##############################################
r-cran-brms-2.14.4/R/predictor.R-55-  # evaluate non-linear predictor
r-cran-brms-2.14.4/R/predictor.R:56:  eta <- try(eval(prep$nlform, args), silent = TRUE)
r-cran-brms-2.14.4/R/predictor.R-57-  if (is(eta, "try-error")) {
##############################################
r-cran-brms-2.14.4/NEWS.md-256-* Model fit criteria computed via `add_criterion` are now 
r-cran-brms-2.14.4/NEWS.md:257:stored in the `brmsfit$criteria` slot.
r-cran-brms-2.14.4/NEWS.md-258-* Deprecate `resp_cat` in favor of `resp_thres`.
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r-cran-brms-2.14.4/inst/doc/brms_threading.Rmd-252-varying intercept model with $`r N`$ data observation which are grouped into
r-cran-brms-2.14.4/inst/doc/brms_threading.Rmd:253:$`r G`$ groups. Each data item has $`r P`$ continuous covariates. The
r-cran-brms-2.14.4/inst/doc/brms_threading.Rmd-254-simulation code for the fake data can be found in the appendix and it's first
##############################################
r-cran-brms-2.14.4/inst/doc/brms_customfamilies.Rmd-61-model, which will serve as our baseline model. For observed number of events $y$
r-cran-brms-2.14.4/inst/doc/brms_customfamilies.Rmd:62:(`incidence` in our case) and total number of trials $T$ (`size`), the
r-cran-brms-2.14.4/inst/doc/brms_customfamilies.Rmd-63-probability mass function of the binomial distribution is defined as
##############################################
r-cran-brms-2.14.4/inst/doc/brms_customfamilies.Rmd-252-[^phi]: The presented post-processing functions need to be adjusted if you
r-cran-brms-2.14.4/inst/doc/brms_customfamilies.Rmd:253:predict `phi` in your model as well by writing `phi <- prep$dpars$phi[, i]`.
r-cran-brms-2.14.4/inst/doc/brms_customfamilies.Rmd-254-If you want to support `pointwise` evaluation as well, please write
##############################################
r-cran-brms-2.14.4/inst/doc/brms_families.Rmd-208-$$
r-cran-brms-2.14.4/inst/doc/brms_families.Rmd:209:where $\rho_p$ is given by $\rho_p(x) = x (p - I_{x < 0})$ and $I_A$ is the
r-cran-brms-2.14.4/inst/doc/brms_families.Rmd-210-indicator function of set $A$. The parameter $\sigma$ is a positive scale
##############################################
r-cran-brms-2.14.4/debian/tests/run-unit-test-6-if [ "$AUTOPKGTEST_TMP" = "" ] ; then
r-cran-brms-2.14.4/debian/tests/run-unit-test:7:    AUTOPKGTEST_TMP=`mktemp -d /tmp/${debname}-test.XXXXXX`
r-cran-brms-2.14.4/debian/tests/run-unit-test-8-    trap "rm -rf $AUTOPKGTEST_TMP" 0 INT QUIT ABRT PIPE TERM