===========================================================
.___ __ __
_________________ __ __ __| _/|__|/ |_
/ ___\_` __ \__ \ | | \/ __ | | \\_ __\
/ /_/ > | \// __ \| | / /_/ | | || |
\___ /|__| (____ /____/\____ | |__||__|
/_____/ \/ \/
grep rough audit - static analysis tool
v2.8 written by @Wireghoul
=================================[justanotherhacker.com]===
r-cran-bayesfactor-0.9.12-4.2+dfsg/inst/doc/compare_lme4.Rmd-231-
r-cran-bayesfactor-0.9.12-4.2+dfsg/inst/doc/compare_lme4.Rmd:232:Notice what's missing: for the categorical parameters, we are missing `Conditionbaseheid` and `ResponseToPrimecorrect`. For the slope parameters, we are missing `ResponseToPrimecorrect:RTtoPrime`. The missing effects tell us what the reference cells are. Since the reference cell parameterization is just a linear transformation of the sum-to-0 parameterization, we can create a matrix that allows us to move from one to the other. We call this $10 \times 7$ matrix `Z`. It takes the 7 "reference-cell" parameters from `lmer` and maps them into the 10 linearly constrained parameters from `lmBF`.
r-cran-bayesfactor-0.9.12-4.2+dfsg/inst/doc/compare_lme4.Rmd-233-
##############################################
r-cran-bayesfactor-0.9.12-4.2+dfsg/inst/doc/manual.Rmd-163-
r-cran-bayesfactor-0.9.12-4.2+dfsg/inst/doc/manual.Rmd:164:We can do a Bayesian version of this analysis using the `ttestBF` function, which performs the "JZS" t test described by [Rouder, Speckman, Sun, Morey, and Iverson (2009)](#Rouderttest). In this model, the true standardized difference $latex \delta=(\mu-\mu_0)/\sigma_\epsilon$ is assumed to be 0 under the null hypothesis, and \(\text{Cauchy}(\text{scale}=r)\) under the alternative. The default \(r\) scale in `BayesFactor` for t tests is \(\sqrt{2}/2\). See `?ttestBF` for more details.
r-cran-bayesfactor-0.9.12-4.2+dfsg/inst/doc/manual.Rmd-165-
##############################################
r-cran-bayesfactor-0.9.12-4.2+dfsg/inst/doc/manual.Rmd-278-
r-cran-bayesfactor-0.9.12-4.2+dfsg/inst/doc/manual.Rmd:279:The `meta.ttestBF` function is used to perform meta-analytic $t$ tests. It requires as input a vector of $t$ statistics, and one or two vectors of sample sizes (arguments `n1` and `n2`). For a set of one-sample $t$ statistics, `n1` should be provided; for two-sample analyses, both `n1` and `n2` should be provided.
r-cran-bayesfactor-0.9.12-4.2+dfsg/inst/doc/manual.Rmd-280-
##############################################
r-cran-bayesfactor-0.9.12-4.2+dfsg/inst/doc/manual.Rmd-372-
r-cran-bayesfactor-0.9.12-4.2+dfsg/inst/doc/manual.Rmd:373:Suppose that we were unhappy with the ~`r round(extractBF(bf)$error*100,1)`% proportional error on the Bayes factor `bf`. `anovaBF` and `lmBF` use Monte Carlo integration to estimate the Bayes factors. The default number of Monte Carlo samples is 10,000 but this can be increased. We could use the `recompute` to reduce the error. The `recompute` function performs the sampling required to build the Bayes factor object again:
r-cran-bayesfactor-0.9.12-4.2+dfsg/inst/doc/manual.Rmd-374-```{r}
##############################################
r-cran-bayesfactor-0.9.12-4.2+dfsg/inst/doc/manual.Rmd-873-```
r-cran-bayesfactor-0.9.12-4.2+dfsg/inst/doc/manual.Rmd:874:The `shape:ID` interaction term is now eliminated in some models, because it does not match `"^ID$"`. Multiple terms may be provided to `neverExclude` by providing a character vector; terms which match any element in the vector will always be included in model comparisons.
r-cran-bayesfactor-0.9.12-4.2+dfsg/inst/doc/manual.Rmd-875-
##############################################
r-cran-bayesfactor-0.9.12-4.2+dfsg/inst/doc/manual.Rmd-1000-
r-cran-bayesfactor-0.9.12-4.2+dfsg/inst/doc/manual.Rmd:1001:We can demonstrate using the `puzzles` data set. Suppose we prefer a prior on the `color` main effect of $r=1$, a prior twice as wide as the default in `rscaleFixed`, $r=.5$. We set the prior scale for `color` using the `rscaleEffects` argument:
r-cran-bayesfactor-0.9.12-4.2+dfsg/inst/doc/manual.Rmd-1002-
##############################################
r-cran-bayesfactor-0.9.12-4.2+dfsg/inst/doc/manual.Rmd-1009-
r-cran-bayesfactor-0.9.12-4.2+dfsg/inst/doc/manual.Rmd:1010:The other fixed effects, `shape` and `shape:color`, retain the prior scale of $r=.5$ from `rscaleFixed`. Compare these Bayes factors to the ones with in the [mixed modeling](#mixed) section above.
r-cran-bayesfactor-0.9.12-4.2+dfsg/inst/doc/manual.Rmd-1011-
##############################################
r-cran-bayesfactor-0.9.12-4.2+dfsg/vignettes/compare_lme4.Rmd-231-
r-cran-bayesfactor-0.9.12-4.2+dfsg/vignettes/compare_lme4.Rmd:232:Notice what's missing: for the categorical parameters, we are missing `Conditionbaseheid` and `ResponseToPrimecorrect`. For the slope parameters, we are missing `ResponseToPrimecorrect:RTtoPrime`. The missing effects tell us what the reference cells are. Since the reference cell parameterization is just a linear transformation of the sum-to-0 parameterization, we can create a matrix that allows us to move from one to the other. We call this $10 \times 7$ matrix `Z`. It takes the 7 "reference-cell" parameters from `lmer` and maps them into the 10 linearly constrained parameters from `lmBF`.
r-cran-bayesfactor-0.9.12-4.2+dfsg/vignettes/compare_lme4.Rmd-233-
##############################################
r-cran-bayesfactor-0.9.12-4.2+dfsg/vignettes/manual.Rmd-163-
r-cran-bayesfactor-0.9.12-4.2+dfsg/vignettes/manual.Rmd:164:We can do a Bayesian version of this analysis using the `ttestBF` function, which performs the "JZS" t test described by [Rouder, Speckman, Sun, Morey, and Iverson (2009)](#Rouderttest). In this model, the true standardized difference $latex \delta=(\mu-\mu_0)/\sigma_\epsilon$ is assumed to be 0 under the null hypothesis, and \(\text{Cauchy}(\text{scale}=r)\) under the alternative. The default \(r\) scale in `BayesFactor` for t tests is \(\sqrt{2}/2\). See `?ttestBF` for more details.
r-cran-bayesfactor-0.9.12-4.2+dfsg/vignettes/manual.Rmd-165-
##############################################
r-cran-bayesfactor-0.9.12-4.2+dfsg/vignettes/manual.Rmd-278-
r-cran-bayesfactor-0.9.12-4.2+dfsg/vignettes/manual.Rmd:279:The `meta.ttestBF` function is used to perform meta-analytic $t$ tests. It requires as input a vector of $t$ statistics, and one or two vectors of sample sizes (arguments `n1` and `n2`). For a set of one-sample $t$ statistics, `n1` should be provided; for two-sample analyses, both `n1` and `n2` should be provided.
r-cran-bayesfactor-0.9.12-4.2+dfsg/vignettes/manual.Rmd-280-
##############################################
r-cran-bayesfactor-0.9.12-4.2+dfsg/vignettes/manual.Rmd-372-
r-cran-bayesfactor-0.9.12-4.2+dfsg/vignettes/manual.Rmd:373:Suppose that we were unhappy with the ~`r round(extractBF(bf)$error*100,1)`% proportional error on the Bayes factor `bf`. `anovaBF` and `lmBF` use Monte Carlo integration to estimate the Bayes factors. The default number of Monte Carlo samples is 10,000 but this can be increased. We could use the `recompute` to reduce the error. The `recompute` function performs the sampling required to build the Bayes factor object again:
r-cran-bayesfactor-0.9.12-4.2+dfsg/vignettes/manual.Rmd-374-```{r}
##############################################
r-cran-bayesfactor-0.9.12-4.2+dfsg/vignettes/manual.Rmd-873-```
r-cran-bayesfactor-0.9.12-4.2+dfsg/vignettes/manual.Rmd:874:The `shape:ID` interaction term is now eliminated in some models, because it does not match `"^ID$"`. Multiple terms may be provided to `neverExclude` by providing a character vector; terms which match any element in the vector will always be included in model comparisons.
r-cran-bayesfactor-0.9.12-4.2+dfsg/vignettes/manual.Rmd-875-
##############################################
r-cran-bayesfactor-0.9.12-4.2+dfsg/vignettes/manual.Rmd-1000-
r-cran-bayesfactor-0.9.12-4.2+dfsg/vignettes/manual.Rmd:1001:We can demonstrate using the `puzzles` data set. Suppose we prefer a prior on the `color` main effect of $r=1$, a prior twice as wide as the default in `rscaleFixed`, $r=.5$. We set the prior scale for `color` using the `rscaleEffects` argument:
r-cran-bayesfactor-0.9.12-4.2+dfsg/vignettes/manual.Rmd-1002-
##############################################
r-cran-bayesfactor-0.9.12-4.2+dfsg/vignettes/manual.Rmd-1009-
r-cran-bayesfactor-0.9.12-4.2+dfsg/vignettes/manual.Rmd:1010:The other fixed effects, `shape` and `shape:color`, retain the prior scale of $r=.5$ from `rscaleFixed`. Compare these Bayes factors to the ones with in the [mixed modeling](#mixed) section above.
r-cran-bayesfactor-0.9.12-4.2+dfsg/vignettes/manual.Rmd-1011-
##############################################
r-cran-bayesfactor-0.9.12-4.2+dfsg/debian/tests/run-unit-test-5-if [ "$AUTOPKGTEST_TMP" = "" ] ; then
r-cran-bayesfactor-0.9.12-4.2+dfsg/debian/tests/run-unit-test:6: AUTOPKGTEST_TMP=`mktemp -d /tmp/${pkg}-test.XXXXXX`
r-cran-bayesfactor-0.9.12-4.2+dfsg/debian/tests/run-unit-test-7-fi