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              grep rough audit - static analysis tool
                  v2.8 written by @Wireghoul
=================================[justanotherhacker.com]===
r-cran-flexmix-2.3-17/vignettes/regression-examples.Rnw-161-$\mathcal{C} = \{c_s \,|\, s = 1,\ldots S\}$ of the $S$ components is
r-cran-flexmix-2.3-17/vignettes/regression-examples.Rnw:162:determined where $c_s = \{s^* = 1,\ldots,S \,|\, c(s^*) = c(s)\}$. A
r-cran-flexmix-2.3-17/vignettes/regression-examples.Rnw-163-similar splitting is possible for the variance of mixtures of Gaussian
##############################################
r-cran-flexmix-2.3-17/vignettes/bootstrapping.Rnw-163-\end{eqnarray*}
r-cran-flexmix-2.3-17/vignettes/bootstrapping.Rnw:164:where $\mu_k(\mathbf{x}) = \mathbf{x}'\bm{\alpha}_k$ and $N(\mu, \sigma^2)$
r-cran-flexmix-2.3-17/vignettes/bootstrapping.Rnw-165-is the normal distribution. 
##############################################
r-cran-flexmix-2.3-17/vignettes/bootstrapping.Rnw-371-\end{equation*}
r-cran-flexmix-2.3-17/vignettes/bootstrapping.Rnw:372:where $\lambda_k = e^{\mathbf{x}'\bm{\alpha}_k}$ for $k = 1,2$ and
r-cran-flexmix-2.3-17/vignettes/bootstrapping.Rnw-373-$P(\lambda)$ is the Poisson distribution.
##############################################
r-cran-flexmix-2.3-17/vignettes/mixture-regressions.Rnw-240-\end{align*}
r-cran-flexmix-2.3-17/vignettes/mixture-regressions.Rnw:241:where $c(k) = \{c = 1,\ldots, C: k \in K_c\}$. 
r-cran-flexmix-2.3-17/vignettes/mixture-regressions.Rnw-242-
##############################################
r-cran-flexmix-2.3-17/vignettes/mixture-regressions.Rnw-253-\end{align*}
r-cran-flexmix-2.3-17/vignettes/mixture-regressions.Rnw:254:where $v(k) = \{v = 1,\ldots,V: k \in K_v\}$. The nesting structure of
r-cran-flexmix-2.3-17/vignettes/mixture-regressions.Rnw-255-the component specific parameters is also described in
##############################################
r-cran-flexmix-2.3-17/vignettes/mixture-regressions.Rnw-444-the form twice the negative loglikelihood plus number of parameters
r-cran-flexmix-2.3-17/vignettes/mixture-regressions.Rnw:445:times $k$ where $k=2$ for the AIC and $k$ equals the logarithm of the
r-cran-flexmix-2.3-17/vignettes/mixture-regressions.Rnw-446-number of observations for the BIC. The ICL is the same as the BIC
##############################################
r-cran-flexmix-2.3-17/R/models.R-14-               list(call = sys.call(), offset = offset,
r-cran-flexmix-2.3-17/R/models.R:15:                    control = eval(formals(glm.fit)$control),            
r-cran-flexmix-2.3-17/R/models.R-16-                    method = "weighted.glm.fit"))
##############################################
r-cran-flexmix-2.3-17/R/refit.R-533-           list(call = sys.call(), offset = offset,
r-cran-flexmix-2.3-17/R/refit.R:534:                control = eval(formals(glm.fit)$control),            
r-cran-flexmix-2.3-17/R/refit.R-535-                method = "weighted.glm.fit"))
##############################################
r-cran-flexmix-2.3-17/inst/doc/regression-examples.Rnw-161-$\mathcal{C} = \{c_s \,|\, s = 1,\ldots S\}$ of the $S$ components is
r-cran-flexmix-2.3-17/inst/doc/regression-examples.Rnw:162:determined where $c_s = \{s^* = 1,\ldots,S \,|\, c(s^*) = c(s)\}$. A
r-cran-flexmix-2.3-17/inst/doc/regression-examples.Rnw-163-similar splitting is possible for the variance of mixtures of Gaussian
##############################################
r-cran-flexmix-2.3-17/inst/doc/bootstrapping.Rnw-163-\end{eqnarray*}
r-cran-flexmix-2.3-17/inst/doc/bootstrapping.Rnw:164:where $\mu_k(\mathbf{x}) = \mathbf{x}'\bm{\alpha}_k$ and $N(\mu, \sigma^2)$
r-cran-flexmix-2.3-17/inst/doc/bootstrapping.Rnw-165-is the normal distribution. 
##############################################
r-cran-flexmix-2.3-17/inst/doc/bootstrapping.Rnw-371-\end{equation*}
r-cran-flexmix-2.3-17/inst/doc/bootstrapping.Rnw:372:where $\lambda_k = e^{\mathbf{x}'\bm{\alpha}_k}$ for $k = 1,2$ and
r-cran-flexmix-2.3-17/inst/doc/bootstrapping.Rnw-373-$P(\lambda)$ is the Poisson distribution.
##############################################
r-cran-flexmix-2.3-17/inst/doc/mixture-regressions.Rnw-240-\end{align*}
r-cran-flexmix-2.3-17/inst/doc/mixture-regressions.Rnw:241:where $c(k) = \{c = 1,\ldots, C: k \in K_c\}$. 
r-cran-flexmix-2.3-17/inst/doc/mixture-regressions.Rnw-242-
##############################################
r-cran-flexmix-2.3-17/inst/doc/mixture-regressions.Rnw-253-\end{align*}
r-cran-flexmix-2.3-17/inst/doc/mixture-regressions.Rnw:254:where $v(k) = \{v = 1,\ldots,V: k \in K_v\}$. The nesting structure of
r-cran-flexmix-2.3-17/inst/doc/mixture-regressions.Rnw-255-the component specific parameters is also described in
##############################################
r-cran-flexmix-2.3-17/inst/doc/mixture-regressions.Rnw-444-the form twice the negative loglikelihood plus number of parameters
r-cran-flexmix-2.3-17/inst/doc/mixture-regressions.Rnw:445:times $k$ where $k=2$ for the AIC and $k$ equals the logarithm of the
r-cran-flexmix-2.3-17/inst/doc/mixture-regressions.Rnw-446-number of observations for the BIC. The ICL is the same as the BIC