Functions for the Nakagami Distribution

Density, distribution function, quantile function and random generation for the Nakagami distribution of Nakagami (1960) .


nakagami

R buildstatus Project Status: Active – The project has reached a stable, usablestate and is being activelydeveloped. CRAN_Status_Badge

Overview

An R-package for the Nakagami distribution.

Installation

Use the following command from inside R:

# install.packages("devtools")
devtools::install_github("JonasMoss/nakagami")

Usage

The density function is dnaka, the probability distribution is pnaka, the quantile function is qnaka and random deviate generator is rnaka. Use them just like the *gamma functions in the stats package.

set.seed(313)
x = seq(0, 3, by = 0.01)
hist(nakagami::rnaka(10^5, shape = 4, scale = 2), freq = FALSE, breaks = "FD")
lines(x, nakagami::dnaka(x, shape = 4, scale = 2), type = "l", lwd = 2)

Parameterization

If X has a Nakagami distribution with parameters shape and scale, then X^2 has a Gamma distribution with the same shape and rate = shape / scale. The Nakagami scale is therefore E[X^2], rather than the Gamma scale parameter.

How to Contribute or Get Help

If you encounter a bug, have a feature request or need some help, open a Github issue.

This project follows a Contributor Code of Conduct.

References

  • Nakagami, N. 1960. “The m-Distribution, a General Formula of Intensity of Rapid Fading.” In Statistical Methods in Radio Wave Propagation: Proceedings of a Symposium Held at the University of California, June 18–20, 1958, edited by William C. Hoffman, 3–36. Permagon Press. https://doi.org/10.1016/B978-0-08-009306-2.50005-4.

Reference manual

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install.packages("nakagami")

1.2.0 by Jonas Moss, 19 hours ago


https://github.com/JonasMoss/nakagami


Report a bug at https://github.com/JonasMoss/nakagami/issues


Browse source code at https://github.com/cran/nakagami


Authors: Jonas Moss [aut, cre]


Documentation:   PDF Manual  


MIT + file LICENSE license


Suggests testthat, knitr, rmarkdown


Imported by univariateML.


See at CRAN