Estimation of a Lognormal - Generalized Pareto Mixture

Estimation of a lognormal - Generalized Pareto mixture via the Expectation-Maximization algorithm. Computation of bootstrap standard errors is supported and performed via parallel computing. Functions for random number simulation and density evaluation are also available. For more details see Bee and Santi (2025) .

Overview

The goal of lognGPD is to estimate a lognormal - Generalized Pareto mixture via the EM algorithm.

Installation

You can install the development version of lognGPD from GitHub with:

# install.packages("pak")
pak::pak("marco-bee/lognGPD")

Example

This is a basic example which shows you how to simulate and estimate a lognormal-GPD mixture:

library(lognGPD)
y <- rlognGPD(100,.9,0,1,0.5,2)
x0 <- c(.7,.2,1.3,.8,1.7)
res <- EMlogngpdmix(x0, y, maxiter = 1000, nboot = 0)