Simulation with Kernel Density Estimation.

simukde

The R package for Simulation with Kernel Density Estimation

Features

1. Generates random values from a univariate and multivariate continuous distribution by using kernel density estimation based on a sample.
2. Finds the best fitting distribution from supported univariate continuous distributions for given data.

CRAN Page

https://cran.r-project.org/package=simukde

Example

## 1-dimensional data
data(faithful)
hist(faithful$eruptions)
res <- simukde::simulate_kde(x = faithful$eruptions, n = 1000)
hist(res$random.values)

## Simulation with the best fitting instrumental distribution
data(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 data
data(faithful)
res <- simukde::simulate_kde(x = faithful, n = 100)
plot(res$kde, display = "filled.contour")
points(x = res$random.values, cex = 0.25, pch = 16, col = "green")
points(x = faithful, cex = 0.25, pch = 16, col = "black")

Installation

From CRAN

install.packages("simukde")

From the repository on GitHub

install.packages("devtools")
devtools::install_github("galaamn/simukde")

Author

MAKHGAL Ganbold and BAYARBAATAR Amgalan, National University of Mongolia

Copyright

© 2018 Makhgal Ganbold and BAYARBAATAR Amgalan

Acknowledgment

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.