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Using the ExtendedLaplace Package

David Saah & Tomasz Kozubowski

2025-05-23

Introduction

The ExtendedLaplace package provides tools for working with the Extended Laplace (EL) distribution, a generalization of the classical Laplace distribution. This distribution is characterized by four parameters: location $\mu$ , scale $\sigma > 0$ , and a uniform noise range $\delta > 0$ .

The EL distribution arises as the sum $Y = X + U$ where $X \sim \text{Laplace}(\mu, \sigma)$ and $U \sim \text{Uniform}(-\delta, \delta)$ .

Installation

To install the development version of this package from GitHub:

# install.packages("devtools")
devtools::install_github("saahdavid/ExtendedLaplace")

Functions

The package provides the following main functions:

dEL(y, mu, sigma, delta): Probability density function
pEL(y, mu, sigma, delta): Cumulative distribution function
qEL(u, mu, sigma, delta): Quantile function (inverse CDF)
rEL(n, mu, sigma, delta): Random number generation
qqplotEL(samples, mu, sigma, delta): Quantile-Quantile Plot

Examples

Density

library(ExtendedLaplace)
curve(dEL(x, mu = 0, sigma = 1, delta = 1), from = -5, to = 5, ylab = "Density", xlab = 'y')

Distribution Function

curve(pEL(x, mu = 0, sigma = 1, delta = 1), from = -5, to = 5, ylab = "CDF", xlab = 'y')

Quantiles

qEL(c(0.025, 0.5, 0.975), mu = 0, sigma = 1, delta = 1)
# [1] -3.157172  0.000000  3.157172

Simulation

samples <- rEL(10000, mu = 0, sigma = 1, delta = 1)
hist(samples, probability = TRUE, breaks = 40, main = "Simulated EL Data", xlab = 'y')
curve(dEL(x, mu = 0, sigma = 1, delta = 1), add = TRUE, col = "navy", lwd = 2)

QQ-Plot

qqplotEL(samples, mu = 0, sigma = 1, delta = 1)

Theoretical Notes

The Extended Laplace distribution has the following form:

PDF

$\begin{aligned} g(y) = \frac{1}{4\delta} \begin{cases} e^{\frac{y- \mu + \delta}{\sigma}} - e^{\frac{y- \mu - \delta}{\sigma}}, & y < \mu - \delta \\ 2 - e^{-\frac{y - \mu + \delta}{\sigma}} - e^{\frac{y - \mu - \delta}{\sigma}}, & \mu - \delta \leq y < \mu + \delta \\ e^{-\frac{y - \mu - \delta}{\sigma}} - e^{-\frac{y - \mu + \delta}{\sigma}}, & y \geq \mu + \delta \end{cases} \end{aligned}$

CDF

$\begin{aligned} G(y) = \frac{1}{4\delta} \begin{cases} \sigma e^{\frac{y- \mu + \delta}{\sigma}} - \sigma e^{\frac{y- \mu - \delta}{\sigma}} , & y < \mu - \delta \\ 2(y - \mu + \delta) + \sigma e^{-\frac{y - \mu + \delta}{\sigma}} - \sigma e^{\frac{y - \mu - \delta}{\sigma}} , & \mu-\delta \leq y < \mu+\delta\\ 4\delta + \sigma e^{-\frac{y- \mu + \delta}{\sigma}} - \sigma e^{-\frac{y- \mu - \delta}{\sigma}} , & y \geq \mu + \delta \, . \end{cases} \end{aligned}$

The quantile function

For $\sigma>0$ and $0<u<1$ , we have $Q(u)=\mu+\sigma z$ , where

$\begin{aligned} z = \begin{cases} \log [4\tau u] - \log [e^\tau - e^{-\tau}] & \mbox{for } 0<u\leq (1-e^{-2\tau})/(4\tau) \\ z^\ast & \mbox{for } (1-e^{-2\tau})/(4\tau) \leq u \leq 1 - (1-e^{-2\tau})/(4\tau)\\ - \log [4\tau (1-u)] + \log [e^{\tau} - e^{-\tau}] & \mbox{for } 1 - (1-e^{-2\tau})/(4\tau) \leq u <1, \end{cases} \end{aligned}$

where $\tau=\delta/\sigma$ and $z^\ast$ is a unique solution of the equation: $\begin{aligned} u = \frac{1}{4\tau} [ 2(z+\tau) - e^{-\tau}(e^z - e^{-z})], \quad -\tau\leq z \leq \tau. \end{aligned}$

Session Info

sessionInfo()
# R version 4.4.3 (2025-02-28)
# Platform: x86_64-apple-darwin20
# Running under: macOS Sequoia 15.5
# 
# Matrix products: default
# BLAS:   /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib 
# LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
# 
# locale:
# [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
# 
# time zone: America/Los_Angeles
# tzcode source: internal
# 
# attached base packages:
# [1] stats     graphics  grDevices utils     datasets  methods   base     
# 
# other attached packages:
# [1] ExtendedLaplace_0.1.6
# 
# loaded via a namespace (and not attached):
#  [1] digest_0.6.37     R6_2.6.1          fastmap_1.2.0     xfun_0.51        
#  [5] splines_4.4.3     cachem_1.1.0      knitr_1.50        htmltools_0.5.8.1
#  [9] rmarkdown_2.29    stats4_4.4.3      lifecycle_1.0.4   cli_3.6.4        
# [13] sass_0.4.9        jquerylib_0.1.4   VGAM_1.1-13       compiler_4.4.3   
# [17] rstudioapi_0.17.1 tools_4.4.3       evaluate_1.0.3    bslib_0.9.0      
# [21] yaml_2.3.10       rlang_1.1.5       jsonlite_1.9.1

References

Saah, D. K., & Kozubowski, T. J. (2025).
A new class of extended Laplace distributions with applications to modeling contaminated Laplace data.
Journal of Computational and Applied Mathematics.
https://doi.org/10.1016/j.cam.2025.116588

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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