Kern_meth

the smoothing bandwidth to be used. The kernels are scaled such that this is the standard deviation of the smoothing kernel.
bw=c('Irt','scott','Ucv','Bcv','SJ') or manual, see details <code>bw.nrd0</code>

a character string giving the smoothing kernel to be used. This must be one of "gaussian", "rectangular", "triangular", "epanechnikov", "biweight", "cosine" or "optcosine"

kernel density estimates. Its default method does so with the given kernel and bandwidth for univariate observations.

Environment R

Parametric Estimation

Statistical Analysis

The package Sim.DiffProc is an object created in the R
        language for simulation and modeling of stochastic differential
        equations (SDEs), and statistical analysis of diffusion
        processes solution of SDEs. This package contains many objects
        (code/function), for example a numerical methods to find the
        solutions to SDEs (one, two and three dimensional), which
        simulates a flows trajectories, with good accuracy. Many
        theoretical problems on the SDEs have become the object of
        practical research, as statistical analysis and simulation of
        solution of SDEs, enabled many searchers in different domains
        to use these equations to modeling and to analyse practical
        problems, in financial and actuarial modeling and other areas
        of application, for example modelling and simulate of
        dispersion in shallow water using the attractive center
        (Boukhetala K, 1996),and the stochastic calculus are applied to
        the random oscillators problem in physics.  We hope that the
        package presented here and the updated survey on the subject
        might be of help for practitioners, postgraduate and PhD
        students, and researchers in the field who might want to
        implement new methods and ideas using R as a statistical
        environment.

Kern_meth: Kernel Density of Random Variable X

Description

Usage

Arguments

Value

Details

See Also

Examples