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grep rough audit - static analysis tool
v2.8 written by @Wireghoul
=================================[justanotherhacker.com]===
r-cran-fitdistrplus-1.1-1/vignettes/Optimalgo.Rmd-80-
r-cran-fitdistrplus-1.1-1/vignettes/Optimalgo.Rmd:81:To determine $W_k$, first it must verify the secant equation $H_k y_k =s_k$ or $y_k=W_k s_k$ where $y_k = g_{k+1}-g_k$ and $s_k=x_{k+1}-x_k$. To define the $n(n-1)$ terms, we generally impose a symmetry and a minimum distance conditions. We say we have a rank 2 update if $H_k = H_{k-1} + a u u^T + b v v^T$ and a rank 1 update if $H_k = H_{k-1} + a u u^T $. Rank $n$ update is justified by the spectral decomposition theorem.
r-cran-fitdistrplus-1.1-1/vignettes/Optimalgo.Rmd-82-
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r-cran-fitdistrplus-1.1-1/vignettes/Optimalgo.Rmd-297-where $\Gamma$ denotes the beta function, see the NIST Handbook of mathematical functions http://dlmf.nist.gov/.
r-cran-fitdistrplus-1.1-1/vignettes/Optimalgo.Rmd:298:There exists an alternative representation where $\mu=m (1-p)/p$ or equivalently $p=m/(m+\mu)$.
r-cran-fitdistrplus-1.1-1/vignettes/Optimalgo.Rmd-299-Thus, the log-likelihood for a set of observations $(x_1,\dots,x_n)$ is
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r-cran-fitdistrplus-1.1-1/vignettes/paper2JSS.Rnw-588- \hline
r-cran-fitdistrplus-1.1-1/vignettes/paper2JSS.Rnw:589: where $F_i\stackrel{\triangle}{=} F(x_i)$
r-cran-fitdistrplus-1.1-1/vignettes/paper2JSS.Rnw-590- \end{tabular}
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r-cran-fitdistrplus-1.1-1/vignettes/paper2JSS.Rnw-741- \hline
r-cran-fitdistrplus-1.1-1/vignettes/paper2JSS.Rnw:742: where $F_i\stackrel{\triangle}{=} F(x_{i})$; & $\overline F_i\stackrel{\triangle}{=}1-F(x_{i})$
r-cran-fitdistrplus-1.1-1/vignettes/paper2JSS.Rnw-743- \end{tabular}
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r-cran-fitdistrplus-1.1-1/inst/doc/Optimalgo.Rmd-80-
r-cran-fitdistrplus-1.1-1/inst/doc/Optimalgo.Rmd:81:To determine $W_k$, first it must verify the secant equation $H_k y_k =s_k$ or $y_k=W_k s_k$ where $y_k = g_{k+1}-g_k$ and $s_k=x_{k+1}-x_k$. To define the $n(n-1)$ terms, we generally impose a symmetry and a minimum distance conditions. We say we have a rank 2 update if $H_k = H_{k-1} + a u u^T + b v v^T$ and a rank 1 update if $H_k = H_{k-1} + a u u^T $. Rank $n$ update is justified by the spectral decomposition theorem.
r-cran-fitdistrplus-1.1-1/inst/doc/Optimalgo.Rmd-82-
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r-cran-fitdistrplus-1.1-1/inst/doc/Optimalgo.Rmd-297-where $\Gamma$ denotes the beta function, see the NIST Handbook of mathematical functions http://dlmf.nist.gov/.
r-cran-fitdistrplus-1.1-1/inst/doc/Optimalgo.Rmd:298:There exists an alternative representation where $\mu=m (1-p)/p$ or equivalently $p=m/(m+\mu)$.
r-cran-fitdistrplus-1.1-1/inst/doc/Optimalgo.Rmd-299-Thus, the log-likelihood for a set of observations $(x_1,\dots,x_n)$ is
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r-cran-fitdistrplus-1.1-1/inst/doc/paper2JSS.Rnw-588- \hline
r-cran-fitdistrplus-1.1-1/inst/doc/paper2JSS.Rnw:589: where $F_i\stackrel{\triangle}{=} F(x_i)$
r-cran-fitdistrplus-1.1-1/inst/doc/paper2JSS.Rnw-590- \end{tabular}
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r-cran-fitdistrplus-1.1-1/inst/doc/paper2JSS.Rnw-741- \hline
r-cran-fitdistrplus-1.1-1/inst/doc/paper2JSS.Rnw:742: where $F_i\stackrel{\triangle}{=} F(x_{i})$; & $\overline F_i\stackrel{\triangle}{=}1-F(x_{i})$
r-cran-fitdistrplus-1.1-1/inst/doc/paper2JSS.Rnw-743- \end{tabular}
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r-cran-fitdistrplus-1.1-1/inst/doc/Optimalgo.html-383-In practice, other methods are preferred (at least to ensure positive definiteness). The method approximates the Hessian by a matrix <span class="math inline">\(H_k\)</span> as a function of <span class="math inline">\(H_{k-1}\)</span>, <span class="math inline">\(x_k\)</span>, <span class="math inline">\(f(x_k)\)</span> and then <span class="math inline">\(d_k\)</span> solves the system <span class="math inline">\(H_k d = - g(x_k)\)</span>. Some implementation may also directly approximate the inverse of the Hessian <span class="math inline">\(W_k\)</span> in order to compute <span class="math inline">\(d_k = -W_k g(x_k)\)</span>. Using the Sherman-Morrison-Woodbury formula, we can switch between <span class="math inline">\(W_k\)</span> and <span class="math inline">\(H_k\)</span>.</p>
r-cran-fitdistrplus-1.1-1/inst/doc/Optimalgo.html:384:<p>To determine <span class="math inline">\(W_k\)</span>, first it must verify the secant equation <span class="math inline">\(H_k y_k =s_k\)</span> or <span class="math inline">\(y_k=W_k s_k\)</span> where <span class="math inline">\(y_k = g_{k+1}-g_k\)</span> and <span class="math inline">\(s_k=x_{k+1}-x_k\)</span>. To define the <span class="math inline">\(n(n-1)\)</span> terms, we generally impose a symmetry and a minimum distance conditions. We say we have a rank 2 update if <span class="math inline">\(H_k = H_{k-1} + a u u^T + b v v^T\)</span> and a rank 1 update if $H_k = H_{k-1} + a u u^T $. Rank <span class="math inline">\(n\)</span> update is justified by the spectral decomposition theorem.</p>
r-cran-fitdistrplus-1.1-1/inst/doc/Optimalgo.html-385-<p>There are two rank-2 updates which are symmetric and preserve positive definiteness</p>
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r-cran-fitdistrplus-1.1-1/debian/tests/run-unit-test-6-if [ "$AUTOPKGTEST_TMP" = "" ] ; then
r-cran-fitdistrplus-1.1-1/debian/tests/run-unit-test:7: AUTOPKGTEST_TMP=`mktemp -d /tmp/${debname}-test.XXXXXX`
r-cran-fitdistrplus-1.1-1/debian/tests/run-unit-test-8- trap "rm -rf $AUTOPKGTEST_TMP" 0 INT QUIT ABRT PIPE TERM