fitdistrplus: An R Package for Fitting Distributions

By M. Delignette-Muller, Christophe Dutang. 2015

2,207 citations recorded by the source corpus.

The package fitdistrplus provides functions for fitting univariate distributions to different types of data (continuous censored or non-censored data and discrete data) and allowing different estimation methods (maximum likelihood, moment matching, quantile matching and maximum goodness-of-fit estimation). Outputs of fitdist and fitdistcens functions are S3 objects, for which specific methods are provided, including summary, plot and quantile. This package also provides various functions to compare the fit of several distributions to the same data set and can handle to bootstrap parameter estimates. Detailed examples are given in food risk assessment, ecotoxicology and insurance contexts.

Subfields: Food Science, Health, Toxicology and Mutagenesis, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2eb55e5f1fae4ac89994d9e4769a96002431068c

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Citations in the corpus

Increased probability of compound long-duration dry and hot events in Europe during summer (1950–2013) (2019, methodology): “Marginal distributions and copulas were fitted through a maximum likelihood estimator using the fitdistrplus (Delignette-Muller and Dutang 2015), ismev (Heffernan and Stephenson 2018) and VineCopula (Schepsmeier et al 2017) R packages.”
SPEI-Based Approach to Agricultural Drought Monitoring in Vojvodina Region (2019, methodology): “The distributions were fit to empirical data by maximum likelihood estimation using the “fitdistrplus” R package [92].”
Deep mutational scanning of SARS-CoV-2 receptor binding domain reveals constraints on folding and ACE2 binding (2020, positive): “Calculating variant phenotypes for expression For each library variant, we estimated mean expression based on its distribution of cell counts across FITC sort bins and the known censored fluorescence boundaries of each sort bin using a maximum likelihood approach (Peterman and Levine, 2016), enacted”
A Psychometric Network Perspective on the Validity and Validation of Personality Trait Questionnaires (2019, methodology): “Next, we fit a distribution to these non-zero values using the fitdistrplus package (Delignette-Muller and Dutang, 2015) in R.”
Splatter: simulation of single-cell RNA sequencing data (2017, methodology): “The parameters that control the mean expression of each gene ( α and β ) are estimated by fitting a gamma distribution to the winsorized means of the library size normalized counts using the fitdistrplus package [35].”
Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses (2018, methodology): “Thus, we proceed by fitting a gamma distribution to the step lengths and a von Mises distribution to the turn angles using maximum likelihood (Agostinelli & Lund, 2017; Delignette-Muller & Dutang, 2015), and use these distributions to generate and pair 9 random steps with each observed step.”