=========================================================== .___ __ __ _________________ __ __ __| _/|__|/ |_ / ___\_` __ \__ \ | | \/ __ | | \\_ __\ / /_/ > | \// __ \| | / /_/ | | || | \___ /|__| (____ /____/\____ | |__||__| /_____/ \/ \/ grep rough audit - static analysis tool v2.8 written by @Wireghoul =================================[justanotherhacker.com]=== r-bioc-metagenomeseq-1.32.0/R/fitZig.R-4-#' $f_count$ fits. Maximum-likelihood estimates are approximated using the EM r-bioc-metagenomeseq-1.32.0/R/fitZig.R:5:#' algorithm where we treat mixture membership $delta_ij = 1$ if $y_ij$ is r-bioc-metagenomeseq-1.32.0/R/fitZig.R-6-#' generated from the zero point mass as latent indicator variables. The ############################################## r-bioc-metagenomeseq-1.32.0/inst/doc/fitTimeSeries.Rnw-50- r-bioc-metagenomeseq-1.32.0/inst/doc/fitTimeSeries.Rnw:51:Our goal is to identify intervals where the absolute difference between two groups $\eta_d(t)=f_1(t, \cdot)-f_2(t, \cdot)$ is large, that is, regions, $R_{t_1,t_2}$, where: r-bioc-metagenomeseq-1.32.0/inst/doc/fitTimeSeries.Rnw-52-$R_{t_1,t_2}= ############################################## r-bioc-metagenomeseq-1.32.0/inst/doc/metagenomeSeq.Rnw-704-Maximum-likelihood estimates are approximated using an EM algorithm, r-bioc-metagenomeseq-1.32.0/inst/doc/metagenomeSeq.Rnw:705:where we treat mixture membership $\Delta_{ij}=1$ if $y_{ij}$ is r-bioc-metagenomeseq-1.32.0/inst/doc/metagenomeSeq.Rnw-706-generated from the zero point mass as latent indicator variables\cite{EM}. We make use of an EM algorithm to account for the linear relationship between sparsity and depth of coverage. The user can specify within the \texttt{fitZig} function a non-default zero model that accounts for more than simply the depth of coverage (e.g. country, age, any metadata associated with sparsity, etc.). ############################################## r-bioc-metagenomeseq-1.32.0/man/fitZig.Rd-54-$f_count$ fits. Maximum-likelihood estimates are approximated using the EM r-bioc-metagenomeseq-1.32.0/man/fitZig.Rd:55:algorithm where we treat mixture membership $delta_ij = 1$ if $y_ij$ is r-bioc-metagenomeseq-1.32.0/man/fitZig.Rd-56-generated from the zero point mass as latent indicator variables. The ############################################## r-bioc-metagenomeseq-1.32.0/vignettes/fitTimeSeries.Rnw-50- r-bioc-metagenomeseq-1.32.0/vignettes/fitTimeSeries.Rnw:51:Our goal is to identify intervals where the absolute difference between two groups $\eta_d(t)=f_1(t, \cdot)-f_2(t, \cdot)$ is large, that is, regions, $R_{t_1,t_2}$, where: r-bioc-metagenomeseq-1.32.0/vignettes/fitTimeSeries.Rnw-52-$R_{t_1,t_2}= ############################################## r-bioc-metagenomeseq-1.32.0/vignettes/metagenomeSeq.Rnw-704-Maximum-likelihood estimates are approximated using an EM algorithm, r-bioc-metagenomeseq-1.32.0/vignettes/metagenomeSeq.Rnw:705:where we treat mixture membership $\Delta_{ij}=1$ if $y_{ij}$ is r-bioc-metagenomeseq-1.32.0/vignettes/metagenomeSeq.Rnw-706-generated from the zero point mass as latent indicator variables\cite{EM}. We make use of an EM algorithm to account for the linear relationship between sparsity and depth of coverage. The user can specify within the \texttt{fitZig} function a non-default zero model that accounts for more than simply the depth of coverage (e.g. country, age, any metadata associated with sparsity, etc.). ############################################## r-bioc-metagenomeseq-1.32.0/debian/tests/run-unit-test-3-oname=metagenomeSeq r-bioc-metagenomeseq-1.32.0/debian/tests/run-unit-test:4:pkg=r-bioc-`echo $oname | tr '[A-Z]' '[a-z]'` r-bioc-metagenomeseq-1.32.0/debian/tests/run-unit-test-5- r-bioc-metagenomeseq-1.32.0/debian/tests/run-unit-test-6-if [ "$AUTOPKGTEST_TMP" = "" ] ; then r-bioc-metagenomeseq-1.32.0/debian/tests/run-unit-test:7: AUTOPKGTEST_TMP=`mktemp -d /tmp/${pkg}-test.XXXXXX` r-bioc-metagenomeseq-1.32.0/debian/tests/run-unit-test-8- trap "rm -rf $AUTOPKGTEST_TMP" 0 INT QUIT ABRT PIPE TERM