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 the 2011 useR conference and at the 2eme rencontres R in 2013 (https://r2013-lyon.sciencesconf.org/).
Three vignettes are available within the package:
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
plotdist for non-censored data and
for censored data).
fitdistcens, different methods can be used to estimate the
maximum likelihood estimation by default (
moment matching estimation (
quantile matching estimation (
maximum goodness-of-fit estimation (
For classical distributions initial values are automatically calculated
if not provided by the user.
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 (
ppcomp for non-censored data and
cdfcompcens for censored data). Goodness-of-fit statistics are also
provided for non-censored data using function
Bootstrap is proposed to quantify the uncertainty on parameter estimates
and also to quantify the uncertainty on CDF or quantiles estimated
from the fitted distribution (
Marie-Laure Delignette-Muller and Christophe Dutang.
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