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famish

The goal of famish is to refine a family of distributions to match a provided dataset. In most use cases, this means fitting a distribution to data, and this is the current version functionality of famish. Importantly, famish grounds the broader probaverse suite of packages to real data.

This young version of famish currently works mostly by wrapping existing fitting functions from other packages, particularly fitdistrplus, ismev and lmom. The main function is fit_dst(), which fits a specified distribution family to data using a specified fitting method. Thin wrappers for specific distribution families are also provided, such as fit_dst_gev() for the Generalised Extreme Value distribution.

The name “famish” reflects the process of narrowing down a broad family of distributions to those that best fit your needs.

Statement of Need

Many software routines allow for the estimation of probability distributions, but there is a need to connect those estimates to the downstream operations needed for advanced statistical models. The probaverse supplies that higher-level infrastructure, and famish is the bridge that grounds probaverse-built models in real datasets or expert judgment.

Target Audience

famish supports the probaverse user base – anyone who works with probability distributions, including data scientists, analysts, researchers, and students. It serves users who need flexible fitting workflows and clear diagnostics for how well a distribution matches observed data.

It is particularly useful for risk-focused domains – hydrology, economics, actuarial science, credit risk, and similar fields – where tail behaviour and extremes determine decisions and advanced probabilistic models rely on dependable estimation tools.

Installation

Install famish from CRAN:

install.packages("famish")

Future Goals

While the current version of famish is limited in scope, it has big long-term goals, especially as the broader probaverse expands to allow for the easier creation of distribution families. Some bigger goals for famish include:

• Fitting cascades, such as first refining a family to have a specified mean (e.g., as estimated by regression), and then estimating the remaining parameters.
• Fitting a distribution to best match a supplied table of quantiles, or another reference distribution.
• Providing modern estimation methods that are more appropriate for estimation of risk and hazard analysis.

Additional features will be added as development continues. We appreciate your patience and welcome contributions! Please see the contributing guide to get started.

Example: Quick Streamflow Fit

For a complete walkthrough, including comparisons between multiple fitted families and tail-focused diagnostics, see the fitting vignette. The snippet below shows the minimal workflow.

library(distionary)
library(famish)

Sample dataset: annual streamflow maxima (cms) for 12 years.

x <- c(4.0, 2.7, 3.5, 3.2, 7.1, 3.1, 2.5, 5.0, 2.3, 4.5, 3.0, 3.8)

Fit a Generalised Extreme Value distribution via maximum likelihood.

d <- fit_dst_gev(x)
#> Loading required namespace: testthat
d
#> Generalised Extreme Value distribution (continuous) 
#> --Parameters--
#>  location     scale     shape 
#> 3.0658476 0.7426435 0.2699160

Distributions are objects understood by the distionary package, so you can use all the familiar methods to inspect and work with them. For example, calculate its mean:

mean(d)
#> [1] 3.761526

The fit_dst() function is the main fitting function in famish. Here is an example, this time fitting a Normal distribution by L-moments.

fit_dst("norm", x = x, method = "lmom")
#> Normal distribution (continuous) 
#> --Parameters--
#>     mean       sd 
#> 3.725000 1.279658

Correctness and Reliability

For those combinations of distribution families and fitting methods indicated by the functions fit_dst_*(), rigorous testing has been conducted to ensure that the estimation methods are consistent – that is, the estimated distribution parameters converge to the true parameter values as more data are drawn from the distribution being estimated.

famish in the Context of Other Packages

famish is unique as it is a bridge from existing fitting routines to the probaverse suite of packages.

• Packages lmom, ismev, and fitdistrplus are all useful for fitting distribution parameters (and are in fact wrapped by famish), but remain low-level.
• Packages like distributions3 and distributional turn distributions in objects, but lack estimation capabilities.

Acknowledgements

The creation of famish would not have been possible without the support of BGC Engineering Inc., the Politecnico di Milano, and the European Space Agency.

Citation

To cite package famish in publications use:

Coia V (2025). famish: Flexibly Tune Probability Distributions. R package version 0.2.0, https://github.com/probaverse/famish, https://famish.probaverse.com/.

Code of Conduct

Please note that the famish project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.