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grep rough audit - static analysis tool
v2.8 written by @Wireghoul
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r-cran-bridgesampling-1.0-0/vignettes/bridgesampling_example_jags.Rmd-17-## Model and Data
r-cran-bridgesampling-1.0-0/vignettes/bridgesampling_example_jags.Rmd:18:The model that we will use assumes that each of the $n$ observations $y_i$ (where $i$ indexes the observation, $i = 1,2,...,n$) is normally distributed with corresponding mean $\theta_i$ and a common known variance $\sigma^2$: $y_i \sim \mathcal{N}(\theta_i, \sigma^2)$. Each $\theta_i$ is drawn from a normal group-level distribution with mean $\mu$ and variance $\tau^2$: $\theta_i \sim \mathcal{N}(\mu, \tau^2)$. For the group-level mean $\mu$, we use a normal prior distribution of the form $\mathcal{N}(\mu_0, \tau^2_0)$. For the group-level variance $\tau^2$, we use an inverse-gamma prior of the form $\text{Inv-Gamma}(\alpha, \beta)$. We will use `JAGS` to fit the model which parametrizes the normal distribution in terms of the precision (i.e., one over the variance). Consequently, we implement this inverse-gamma prior on $\tau^2$ by placing a gamma prior of the form $\text{Gamma}(\alpha, \beta)$ on the precision; we call this precision parameter `invTau2` in the code.
r-cran-bridgesampling-1.0-0/vignettes/bridgesampling_example_jags.Rmd-19-
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r-cran-bridgesampling-1.0-0/vignettes/bridgesampling_example_jags.Rmd-119-
r-cran-bridgesampling-1.0-0/vignettes/bridgesampling_example_jags.Rmd:120:For example, in the null model, there are three "`~`" signs. Starting at the data-level, we need to evaluate the log of the normal density with mean $\theta_i$ and variance $\sigma^2$ for all $y_i$ and then sum the resulting log density values. Next, we move one step up in the model and evaluate the log of the group-level density for all $\theta_i$. Hence, we evaluate the log of the normal density for $\theta_i$ with mean $\mu = 0$ and variance $\tau^2$ (remember that `JAGS` parametrizes the normal distribution in terms of the precision `invTau2` = $1/\tau^2$; in contrast, `R` parametrizes it in terms of the standard deviation) and sum the resulting log density values. The result of this summation is added to the result of the previous summation for the data-level normal distribution. Finally, we need to evaluate the log of the prior density for `invTau2`. This means that we compute the log density of the gamma distribution with parameters $\alpha$ and $\beta$ for the sampled `invTau2` value and add the resulting log density value to the result of summing the data-level and group-level log densities. The unnormalized log posterior for the alternative model can be obtained in a similar fashion. The resulting functions look as follows:
r-cran-bridgesampling-1.0-0/vignettes/bridgesampling_example_jags.Rmd-121-```{r,eval=FALSE}
##############################################
r-cran-bridgesampling-1.0-0/vignettes/bridgesampling_example_nimble.Rmd-17-## Model and Data
r-cran-bridgesampling-1.0-0/vignettes/bridgesampling_example_nimble.Rmd:18:The model that we will use assumes that each of the $n$ observations $y_i$ (where $i$ indexes the observation, $i = 1,2,...,n$) is normally distributed with corresponding mean $\theta_i$ and a common known variance $\sigma^2$: $y_i \sim \mathcal{N}(\theta_i, \sigma^2)$. Each $\theta_i$ is drawn from a normal group-level distribution with mean $\mu$ and variance $\tau^2$: $\theta_i \sim \mathcal{N}(\mu, \tau^2)$. For the group-level mean $\mu$, we use a normal prior distribution of the form $\mathcal{N}(\mu_0, \tau^2_0)$. For the group-level variance $\tau^2$, we use an inverse-gamma prior of the form $\text{Inv-Gamma}(\alpha, \beta)$.
r-cran-bridgesampling-1.0-0/vignettes/bridgesampling_example_nimble.Rmd-19-
##############################################
r-cran-bridgesampling-1.0-0/vignettes/bridgesampling_example_stan.Rmd-17-## Model and Data
r-cran-bridgesampling-1.0-0/vignettes/bridgesampling_example_stan.Rmd:18:The model that we will use assumes that each of the $n$ observations $y_i$ (where $i$ indexes the observation, $i = 1,2,...,n$) is normally distributed with corresponding mean $\theta_i$ and a common known variance $\sigma^2$: $y_i \sim \mathcal{N}(\theta_i, \sigma^2)$. Each $\theta_i$ is drawn from a normal group-level distribution with mean $\mu$ and variance $\tau^2$: $\theta_i \sim \mathcal{N}(\mu, \tau^2)$. For the group-level mean $\mu$, we use a normal prior distribution of the form $\mathcal{N}(\mu_0, \tau^2_0)$. For the group-level variance $\tau^2$, we use an inverse-gamma prior of the form $\text{Inv-Gamma}(\alpha, \beta)$.
r-cran-bridgesampling-1.0-0/vignettes/bridgesampling_example_stan.Rmd-19-
##############################################
r-cran-bridgesampling-1.0-0/R/bridge_sampler.R-463- verbose = FALSE, ...) {
r-cran-bridgesampling-1.0-0/R/bridge_sampler.R:464: df <- eval(samples$call$diagnostic_file)
r-cran-bridgesampling-1.0-0/R/bridge_sampler.R-465- if (is.null(df))
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r-cran-bridgesampling-1.0-0/inst/doc/bridgesampling_example_jags.Rmd-17-## Model and Data
r-cran-bridgesampling-1.0-0/inst/doc/bridgesampling_example_jags.Rmd:18:The model that we will use assumes that each of the $n$ observations $y_i$ (where $i$ indexes the observation, $i = 1,2,...,n$) is normally distributed with corresponding mean $\theta_i$ and a common known variance $\sigma^2$: $y_i \sim \mathcal{N}(\theta_i, \sigma^2)$. Each $\theta_i$ is drawn from a normal group-level distribution with mean $\mu$ and variance $\tau^2$: $\theta_i \sim \mathcal{N}(\mu, \tau^2)$. For the group-level mean $\mu$, we use a normal prior distribution of the form $\mathcal{N}(\mu_0, \tau^2_0)$. For the group-level variance $\tau^2$, we use an inverse-gamma prior of the form $\text{Inv-Gamma}(\alpha, \beta)$. We will use `JAGS` to fit the model which parametrizes the normal distribution in terms of the precision (i.e., one over the variance). Consequently, we implement this inverse-gamma prior on $\tau^2$ by placing a gamma prior of the form $\text{Gamma}(\alpha, \beta)$ on the precision; we call this precision parameter `invTau2` in the code.
r-cran-bridgesampling-1.0-0/inst/doc/bridgesampling_example_jags.Rmd-19-
##############################################
r-cran-bridgesampling-1.0-0/inst/doc/bridgesampling_example_jags.Rmd-119-
r-cran-bridgesampling-1.0-0/inst/doc/bridgesampling_example_jags.Rmd:120:For example, in the null model, there are three "`~`" signs. Starting at the data-level, we need to evaluate the log of the normal density with mean $\theta_i$ and variance $\sigma^2$ for all $y_i$ and then sum the resulting log density values. Next, we move one step up in the model and evaluate the log of the group-level density for all $\theta_i$. Hence, we evaluate the log of the normal density for $\theta_i$ with mean $\mu = 0$ and variance $\tau^2$ (remember that `JAGS` parametrizes the normal distribution in terms of the precision `invTau2` = $1/\tau^2$; in contrast, `R` parametrizes it in terms of the standard deviation) and sum the resulting log density values. The result of this summation is added to the result of the previous summation for the data-level normal distribution. Finally, we need to evaluate the log of the prior density for `invTau2`. This means that we compute the log density of the gamma distribution with parameters $\alpha$ and $\beta$ for the sampled `invTau2` value and add the resulting log density value to the result of summing the data-level and group-level log densities. The unnormalized log posterior for the alternative model can be obtained in a similar fashion. The resulting functions look as follows:
r-cran-bridgesampling-1.0-0/inst/doc/bridgesampling_example_jags.Rmd-121-```{r,eval=FALSE}
##############################################
r-cran-bridgesampling-1.0-0/inst/doc/bridgesampling_example_nimble.Rmd-17-## Model and Data
r-cran-bridgesampling-1.0-0/inst/doc/bridgesampling_example_nimble.Rmd:18:The model that we will use assumes that each of the $n$ observations $y_i$ (where $i$ indexes the observation, $i = 1,2,...,n$) is normally distributed with corresponding mean $\theta_i$ and a common known variance $\sigma^2$: $y_i \sim \mathcal{N}(\theta_i, \sigma^2)$. Each $\theta_i$ is drawn from a normal group-level distribution with mean $\mu$ and variance $\tau^2$: $\theta_i \sim \mathcal{N}(\mu, \tau^2)$. For the group-level mean $\mu$, we use a normal prior distribution of the form $\mathcal{N}(\mu_0, \tau^2_0)$. For the group-level variance $\tau^2$, we use an inverse-gamma prior of the form $\text{Inv-Gamma}(\alpha, \beta)$.
r-cran-bridgesampling-1.0-0/inst/doc/bridgesampling_example_nimble.Rmd-19-
##############################################
r-cran-bridgesampling-1.0-0/inst/doc/bridgesampling_example_stan.Rmd-17-## Model and Data
r-cran-bridgesampling-1.0-0/inst/doc/bridgesampling_example_stan.Rmd:18:The model that we will use assumes that each of the $n$ observations $y_i$ (where $i$ indexes the observation, $i = 1,2,...,n$) is normally distributed with corresponding mean $\theta_i$ and a common known variance $\sigma^2$: $y_i \sim \mathcal{N}(\theta_i, \sigma^2)$. Each $\theta_i$ is drawn from a normal group-level distribution with mean $\mu$ and variance $\tau^2$: $\theta_i \sim \mathcal{N}(\mu, \tau^2)$. For the group-level mean $\mu$, we use a normal prior distribution of the form $\mathcal{N}(\mu_0, \tau^2_0)$. For the group-level variance $\tau^2$, we use an inverse-gamma prior of the form $\text{Inv-Gamma}(\alpha, \beta)$.
r-cran-bridgesampling-1.0-0/inst/doc/bridgesampling_example_stan.Rmd-19-
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r-cran-bridgesampling-1.0-0/debian/tests/run-unit-test-6-if [ "$AUTOPKGTEST_TMP" = "" ] ; then
r-cran-bridgesampling-1.0-0/debian/tests/run-unit-test:7: AUTOPKGTEST_TMP=`mktemp -d /tmp/${debname}-test.XXXXXX`
r-cran-bridgesampling-1.0-0/debian/tests/run-unit-test-8- trap "rm -rf $AUTOPKGTEST_TMP" 0 INT QUIT ABRT PIPE TERM