Phitter
| Phitter main interface | |
| Repository | GitHub Repository |
|---|---|
| Written in | Python |
| Engine | |
| Operating system | Cross-platform (Windows, macOS, Linux) |
| Platform | Web application |
| Available in |
|
| Type | Statistical software |
| License | MIT License |
| Website | Phitter |
Search Phitter on Amazon.
Phitter is an open-source Python library designed to streamline the process of fitting and analyzing probability distributions for applications in statistics, data science, operations research, and machine learning. It provides a comprehensive catalog of over 80 continuous and discrete distributions, multiple goodness-of-fit measures (Chi-Square, Kolmogorov-Smirnov, and Anderson-Darling), interactive visualizations for exploratory data analysis and model validation, and detailed modeling guides with spreadsheet implementations. By reducing the complexity of distribution fitting, Phitter helps researchers and practitioners identify distributions that best model their data.[1][2][3]
Features
Phitter supports fitting over 80 continuous and discrete probability distributions and includes the following features:
- Documentations, spreadsheets and python support for continuous and discrete distributions[4]
- Web-based interface and Python library[5]
- Goodness-of-fit tests: Chi-square, Kolmogorov–Smirnov, Anderson–Darling[6]
- Interactive visualizations: PDF overlays, CDF plots, Q–Q plots[7]
- Automated modeling reports with formulas and parameter estimates
- Simulation tools for stochastic processes and queueing systems (e.g., FIFO, LIFO)
- Parallel processing for large datasets
- Open-source under the MIT License
Python package
The Python library Phitter provides an intuitive interface for fitting both continuous and discrete probability distributions to empirical data. For each distribution, it performs three goodness-of-fit tests: Chi-square, Kolmogorov–Smirnov test, and Anderson–Darling test.
Phitter estimates distribution parameters primarily through the method of moments, solving the system of parametric equations where possible. This estimation approach offers significant computational efficiency gains. Additional performance optimization is achieved through parallel processing of the fitting workflow.
Users can evaluate results using interactive visualizations including:
- Histograms with fitted distribution curves
- Empirical Cumulative Distribution Function (ECDF) plots
- Q–Q plots for distribution comparison
Probability distributions documented in Phitter
Continuous distributions
- Alpha distribution
- Arcsine distribution
- ARGUS distribution
- Beta distribution
- Beta prime distribution
- Bradford distribution
- Burr distribution
- Cauchy distribution
- Chi-square distribution
- Dagum distribution
- Erlang distribution
- Exponential distribution
- F-distribution
- Fatigue-life (Birnbaum–Saunders) distribution
- Folded normal distribution
- Fréchet distribution
- Gamma distribution
- Generalized extreme value distribution
- Generalized gamma distribution
- Generalized logistic distribution
- Generalized normal distribution
- Generalized Pareto distribution
- Gumbel (right-skewed) distribution
- Half-normal distribution
- Hyperbolic secant distribution
- Inverse-gamma distribution
- Inverse Gaussian distribution
- Johnson SB distribution
- Johnson SU distribution
- Kumaraswamy distribution
- Laplace distribution
- Lévy distribution
- Log-gamma distribution
- Logistic distribution
- Log-logistic distribution
- Log-normal distribution
- Maxwell distribution
- Moyal distribution
- Nakagami distribution
- Noncentral chi-squared distribution
- Noncentral F-distribution
- Noncentral t-distribution
- Normal distribution
- Pareto (first-kind) distribution
- Pareto (second-kind) / Lomax distribution
- PERT distribution
- Power function distribution
- Rayleigh distribution
- Reciprocal distribution
- Rice distribution
- Semicircular distribution
- Trapezoidal distribution
- Triangular distribution
- Student's t-distribution
- Continuous uniform distribution
- Weibull distribution
Discrete distributions
- Bernoulli distribution
- Binomial distribution
- Geometric distribution
- Hypergeometric distribution
- Logarithmic (series) distribution
- Negative binomial distribution
- Poisson distribution
- Discrete uniform distribution
See also
References
- ↑ "Phitter: A library designed to streamline the process of fitting and analyzing probability distributions". Journal of Open Source Software. 10 (110): 7625. 2025. doi:10.21105/joss.07625.
- ↑ "Univariate Distribution Relationships". Professor Leemis Univariate Distribution Relationships. Retrieved 2025-06-16.
- ↑ "Phitter – A Python library for statistical distribution fitting". Reddit. 3 January 2025. Retrieved 2025-06-16.
- ↑ "Playground continuous and discrete distributions". Phitter. Retrieved 2025-06-16.
- ↑ "Phitter Documentation". Phitter Docs. Retrieved 2025-06-16.
- ↑ "How to Use Goodness-of-Fit Tests to Validate Your Distribution Choice in Phitter". Statology. 27 February 2025. Retrieved 2025-06-16.
- ↑ "How to Use ECDF Analysis to Validate Distribution Fits in Phitter". Statology. 28 February 2025. Retrieved 2025-06-16.
This article "Phitter" is from Wikipedia. The list of its authors can be seen in its historical and/or the page Edithistory:Phitter. Articles copied from Draft Namespace on Wikipedia could be seen on the Draft Namespace of Wikipedia and not main one.
