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              grep rough audit - static analysis tool
                  v2.8 written by @Wireghoul
=================================[justanotherhacker.com]===
r-cran-kedd-1.0.3/inst/doc/kedd.R-117-###################################################
r-cran-kedd-1.0.3/inst/doc/kedd.R:118:kernels <- eval(formals(h.mlcv.default)$kernel)
r-cran-kedd-1.0.3/inst/doc/kedd.R-119-hmlcv <- numeric()
##############################################
r-cran-kedd-1.0.3/inst/doc/kedd.Rnw-171-the kernel function $K$ and the smoothing parameter or bandwidth $h$. The choice of $K$ is a problem of less importance, because $K$ is not very sensitive to the shape of estimator, and different functions that produce good results can be used. In practice, the choice of an efficient method for the computation of $h$, for an observed data sample is a crucial problem, because of the effect of the bandwidth on the shape of the corresponding estimator. If the bandwidth is small, we will obtain an under smoothed estimator, with high variability. On the contrary, if the value of $h$ is big, the resulting estimator will be over smooth and farther from the function that we are trying to estimate.\\
r-cran-kedd-1.0.3/inst/doc/kedd.Rnw:172:An example is drawn in Figure \ref{Sec2:fig1} where we show in left four different kernel (Gaussian, biweight, triweight and tricube) estimators of the first derivative of a bimodal (separated) Gaussian density (Equation \ref{Sec2:eq3}), and a given value of $h=0.6$. On the right, using the Gaussian kernel and four different values for the bandwidth.
r-cran-kedd-1.0.3/inst/doc/kedd.Rnw-173-\setkeys{Gin}{width=0.45\textwidth}
##############################################
r-cran-kedd-1.0.3/inst/doc/kedd.Rnw-352-<<results=hide,print=FALSE>>=
r-cran-kedd-1.0.3/inst/doc/kedd.Rnw:353:kernels <- eval(formals(h.mlcv.default)$kernel)
r-cran-kedd-1.0.3/inst/doc/kedd.Rnw-354-hmlcv <- numeric()
##############################################
r-cran-kedd-1.0.3/demo/kedd.R-73-
r-cran-kedd-1.0.3/demo/kedd.R:74:kernels <- eval(formals(dkde.default)$kernel)
r-cran-kedd-1.0.3/demo/kedd.R-75-dev.new()
##############################################
r-cran-kedd-1.0.3/demo/kedd.R-87-
r-cran-kedd-1.0.3/demo/kedd.R:88:kernels <- eval(formals(dkde.default)$kernel)[-3]
r-cran-kedd-1.0.3/demo/kedd.R-89-dev.new()
##############################################
r-cran-kedd-1.0.3/vignettes/kedd.Rnw-171-the kernel function $K$ and the smoothing parameter or bandwidth $h$. The choice of $K$ is a problem of less importance, because $K$ is not very sensitive to the shape of estimator, and different functions that produce good results can be used. In practice, the choice of an efficient method for the computation of $h$, for an observed data sample is a crucial problem, because of the effect of the bandwidth on the shape of the corresponding estimator. If the bandwidth is small, we will obtain an under smoothed estimator, with high variability. On the contrary, if the value of $h$ is big, the resulting estimator will be over smooth and farther from the function that we are trying to estimate.\\
r-cran-kedd-1.0.3/vignettes/kedd.Rnw:172:An example is drawn in Figure \ref{Sec2:fig1} where we show in left four different kernel (Gaussian, biweight, triweight and tricube) estimators of the first derivative of a bimodal (separated) Gaussian density (Equation \ref{Sec2:eq3}), and a given value of $h=0.6$. On the right, using the Gaussian kernel and four different values for the bandwidth.
r-cran-kedd-1.0.3/vignettes/kedd.Rnw-173-\setkeys{Gin}{width=0.45\textwidth}
##############################################
r-cran-kedd-1.0.3/vignettes/kedd.Rnw-352-<<results=hide,print=FALSE>>=
r-cran-kedd-1.0.3/vignettes/kedd.Rnw:353:kernels <- eval(formals(h.mlcv.default)$kernel)
r-cran-kedd-1.0.3/vignettes/kedd.Rnw-354-hmlcv <- numeric()
##############################################
r-cran-kedd-1.0.3/man/dkde.Rd-180-
r-cran-kedd-1.0.3/man/dkde.Rd:181:kernels <- eval(formals(dkde.default)$kernel)
r-cran-kedd-1.0.3/man/dkde.Rd-182-dev.new()
##############################################
r-cran-kedd-1.0.3/man/dkde.Rd-192-
r-cran-kedd-1.0.3/man/dkde.Rd:193:kernels <- eval(formals(dkde.default)$kernel)[-3]
r-cran-kedd-1.0.3/man/dkde.Rd-194-dev.new()
##############################################
r-cran-kedd-1.0.3/man/kernel.conv.Rd-72-\examples{
r-cran-kedd-1.0.3/man/kernel.conv.Rd:73:kernels <- eval(formals(kernel.conv.default)$kernel)
r-cran-kedd-1.0.3/man/kernel.conv.Rd-74-kernels
##############################################
r-cran-kedd-1.0.3/man/kernel.fun.Rd-74-\examples{
r-cran-kedd-1.0.3/man/kernel.fun.Rd:75:kernels <- eval(formals(kernel.fun.default)$kernel)
r-cran-kedd-1.0.3/man/kernel.fun.Rd-76-kernels