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FitDynMix

The goal of FitDynMix is to estimate a dynamic lognormal - Generalized Pareto mixture via the Approximate Maximum Likelihood and the Cross-Entropy methods.

Installation

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

# install.packages("devtools")
devtools::install_github("marco-bee/FitDynMix")

Example

This is a basic example of estimation via AMLE:

library(FitDynMix)
## basic example code
k <- 5000
epsilon <- .02
bootreps <- 2
res = AMLEfit(Metro2019, epsilon, k, bootreps, 1e-04, 'exp')

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Install

install.packages('FitDynMix')

Monthly Downloads

487

Version

1.0.2

License

MIT + file LICENSE

Issues

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Maintainer

Marco Bee

Last Published

January 20th, 2026

Functions in FitDynMix (1.0.2)

nConst_MC

Monte carlo approximation of the normalizing constant of a Lognormal-GPD dynamic mixture
MLEBoot

Bootstrap standard errors of MLEs
MLEfit

Estimating a dynamic mixture via MLE
dynloglik

Log-likelihood of a Lognormal-GPD dynamic mixture
cvm_stat_M

Computing the Cramér - von Mises distance between two samples
ddyn

Density of a Lognormal-GPD dynamic mixture
CENoisyFitBoot

Cross-Entropy estimation and bootstrap standard errors
CENoisyFit

Cross-Entropy estimation
AMLEmode

Approximating the mode of a multivariate empirical distribution
AMLEfit

Estimating a dynamic mixture via AMLE
Metro2019

Population estimate of the US metropolitan areas
dynloglikMC

Log-likelihood of a Lognormal-GPD dynamic mixture
rDynMix

Simulating a dynamic lognormal-Pareto mixture