R: Overview of the 'fitdistrplus' package

fitdistrplus-package {fitdistrplus} | R Documentation

## Overview of the fitdistrplus package

### Description

The idea of this package emerged in 2008 from a collaboration between JB Denis, R Pouillot and ML Delignette who at this time worked in the area of quantitative risk assessment. The implementation of this package was a part of a more general project named "Risk assessment with R" gathering different packages and hosted in R-forge.

The fitdistrplus package was first written by ML Delignette-Muller and made available in CRAN on 2009 and presented at the 2009 useR conference in Rennes. A few months after, C Dutang joined the project by starting to participate to the implementation of the fitdistrplus package. The package has also been presented at 2011 useR conference, 2eme rencontres R in 2013.

Three vignettes are available within the package:

- a general overview of the package published in the Journal of Statistical Software,

- a html document answering the most Frequently Asked Questions,

- a html document presenting a benchmark of optimization algorithms when finding parameters.

The fitdistrplus package is a general package that aims at helping the fit of univariate parametric distributions to censored or non-censored data. The two main functions are `fitdist` for fit on non-censored data and `fitdistcens` for fit on censored data.

The choice of candidate distributions to fit may be helped using functions `descdist` and `plotdist` for non-censored data and `plotdistcens` for censored data).

Using functions `fitdist` and `fitdistcens`, different methods can be used to estimate the distribution parameters:

- maximum likelihood estimation by default (`mledist`),

- moment matching estimation (`mmedist`),

- quantile matching estimation (`qmedist`),

- maximum goodness-of-fit estimation (`mgedist`).

For classical distributions initial values are automatically calculated if not provided by the user. Graphical functions `plotdist` and `plotdistcens` can be used to help a manual calibration of initial values for parameters of non-classical distributions. Function `prefit` is proposed to help the definition of good starting values in the special case of constrained parameters. In the case where maximum likelihood is chosen as the estimation method, function `llplot` enables to visualize loglikelihood contours.

The goodness-of-fit of fitted distributions (a single fit or multiple fits) can be explored using different graphical functions (`cdfcomp`, `denscomp`, `qqcomp` and `ppcomp` for non-censored data and `cdfcompcens` for censored data). Goodness-of-fit statistics are also provided for non-censored data using function `gofstat`.

Bootstrap is proposed to quantify the uncertainty on parameter estimates (functions `bootdist` and `bootdistcens`) and also to quantify the uncertainty on CDF or quantiles estimated from the fitted distribution (`quantile` and `CIcdfplot`).

### Author(s)

Marie-Laure Delignette-Muller and Christophe Dutang.

[Package fitdistrplus version 1.0-9 Index]