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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

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# famish

The goal of `famish` is to refine a **fam**ily 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](https://probaverse.com/) 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()`](https://famish.netlify.app/reference/fit_dst.md), 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()`](https://famish.netlify.app/reference/fit_dst_family_wrappers.md)
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:

``` r

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
…

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