Comprehensive Automatized Evaluation of Distribution Models for Count Data
Compare fits
countfitteR Graphical User Interface
countfitteR - a framework for fitting count distributions in R
Make a decision based on the BIC value
Fit counts to distributions
plot_fitcmp
Process counts
Select the most appropriate model
Summary of estimates
Validate data
Zero-inflated negative binomial distrbution
Zero-inflated Poisson distrbution
A large number of measurements generate count data. This is a statistical data type that only assumes non-negative integer values and is generated by counting. Typically, counting data can be found in biomedical applications, such as the analysis of DNA double-strand breaks. The number of DNA double-strand breaks can be counted in individual cells using various bioanalytical methods. For diagnostic applications, it is relevant to record the distribution of the number data in order to determine their biomedical significance (Roediger, S. et al., 2018. Journal of Laboratory and Precision Medicine. <doi:10.21037/jlpm.2018.04.10>). The software offers functions for a comprehensive automated evaluation of distribution models of count data. In addition to programmatic interaction, a graphical user interface (web server) is included, which enables fast and interactive data-scientific analyses. The user is supported in selecting the most suitable counting distribution for his own data set.
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