Generates random values from a univariate and multivariate continuous distribution by using kernel density estimation based on a sample. Duong (2017)
The R package for Simulation with Kernel Density Estimation
https://cran.r-project.org/package=simukde
## 1-dimensional datadata(faithful)hist(faithful$eruptions)res <- simukde::simulate_kde(x = faithful$eruptions, n = 1000)hist(res$random.values) ## Simulation with the best fitting instrumental distributiondata(faithful)par(mfrow = c(1, 3))hist(faithful$eruptions)fit <- simukde::find_best_fit(x = faithful$eruptions, positive = TRUE)res <- simukde::simulate_kde( x = faithful$eruptions, n = 1000, distr = fit$distribution, parallel = FALSE)hist(res$random.values)par(mfrow = c(1, 1)) ## 2-dimensional datadata(faithful)res <- simukde::simulate_kde(x = faithful, n = 100)plot(res$kde, display = "filled.contour2")points(x = res$random.values, cex = 0.25, pch = 16, col = "green")points(x = faithful, cex = 0.25, pch = 16, col = "black")
From CRAN
install.packages("simukde")
From the repository on GitHub
install.packages("devtools")devtools::install_github("galaamn/simukde")
MAKHGAL Ganbold and BAYARBAATAR Amgalan, National University of Mongolia, 2018
Funding: This package has been done within the framework of the project Statistics and Optimization Based Methods for Identification of Cancer-Activated Biological Processes (P2017-2519) supported by the Asia Research Center, Mongolia and Korea Foundation for Advanced Studies, Korea.
The funders had no role in study design, analysis, decision to publish, or preparation of the package.
NEWS.md
file to track changes to the package.ByteCompile
was added into DESCRIPTION
file with the value true.distr
.MASS
.distr
.find_best_fit
for finding the best fitting instrumental univariate continuous distribution easily.datasets
.simulate_kde
.find_best_fit
.