The MittagLeffleR R package

The first type Mittag-Leffler distribution is a heavy-tailed distribution, and occurs mainly as a waiting time distribution in problems with “fractional” time scales, e.g. times between earthquakes.

The second type Mittag-Leffler distribution is light-tailed, and “inverse” to the sum-stable distributions. It typically models the number of events in fractional systems and is used for time-changes of stochastic processes, e.g. anomalous diffusion processes.

## Installation

### Stable release on CRAN

You can install MittagLeffleR from CRAN via

install.packages("MittagLeffleR")
library(MittagLeffleR)

### Development version on Github

Install the devtools package first, then

# install.packages("devtools")
devtools::install_github("strakaps/MittagLeffler")
library(MittagLeffleR)

## Usage

See reference manual.

## Examples

### Fitting a Mittag-Leffler distribution

Generate a dataset first:

library(MittagLeffleR)
y = rml(n = 10000, tail = 0.9, scale = 2)

Fit the distribution:

logMomentEstimator(y, 0.95)
#>      tail     scale    tailLo    tailHi   scaleLo   scaleHi
#> 0.8998758 2.0170711 0.8995285 0.9002230 2.0151044 2.0190378

• the shape parameter 0 < ν < 1,
• the scale parameter δ > 0,
• their 95% confidence intervals.

### Calculate the probability density of an anomalous diffusion process

Standard Brownian motion with drift 1 has, at time t, has a normal probability density n(x|μ=t,σ2=t). A fractional diffusion at time t has the time-changed probability density

p(x,t) = ∫n(x|μ=u,σ2=u)h(u,t)du

where h(u,t) is a second type Mittag-Leffler probability density with scale tα. (We assume t = 1.)

library(ggplot2)
library(tidyr)
tail <- 0.65
dx <- 0.01
x <- seq(-2,5,dx)
t <- 3^(-1:2)
# cut off time so that only 1 % of probability is lost
umax <- qml(p = 0.99, tail = tail, scale = max(t), second.type = TRUE)
u <- seq(0.01,umax,dx)
H <- outer(u,t, function(u,t) {dml(x = u, tail = tail, scale = t^tail)})
N <- outer(x,u,function(x,u){dnorm(x = x, mean = u, sd = sqrt(u))})
p <- N %*% H * dx
df <- data.frame(p)
names(df) <- sapply(t, function(t){paste0("T=",round(t,2))})
df['x'] <- x
df %>%
gather(key = "time", value = "density", -x) %>%
ggplot(mapping = aes(x=x, y=density, col=time)) +
geom_line() +
labs(ggtitle("Subdiffusion with drift"))

## Vignettes

See the page strakaps.github.io/MittagLeffleR/articles/ for vignettes on

• Plots of the Mittag-Leffler distributions
• Details of Mittag-Leffler random variate generation
• Probabilities and Quantiles