Release 0.2.0

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "metromc"
-version = "0.1.0"
+version = "0.2.0"
 edition = "2021"
 authors = ["Emil Koutanov"]
 license = "MIT"

README.md

@@ -1,15 +1,24 @@
 `metromc`
 ===
-Markov chain Monte Carlo sampling using the _Independence Metropolis-Hastings_ algorithm with uniform transition kernel.
+Markov chain Monte Carlo (MCMC) sampling using the _Independence Metropolis-Hastings_ algorithm with uniform transition kernel.
 
 [![Crates.io](https://img.shields.io/crates/v/metromc?style=flat-square&logo=rust)](https://crates.io/crates/metromc)
 [![docs.rs](https://img.shields.io/badge/docs.rs-metromc-blue?style=flat-square&logo=docs.rs)](https://docs.rs/metromc)
 [![Build Status](https://img.shields.io/github/actions/workflow/status/obsidiandynamics/metromc/master.yml?branch=master&style=flat-square&logo=github)](https://github.com/obsidiandynamics/metromc/actions/workflows/master.yml)
 
 Uses the [tinyrand](https://github.com/obsidiandynamics/tinyrand) RNG to sample at a rate of ~50M samples/sec.
 
+Supports the following distributions:
+
+* [Gaussian/Normal](https://en.wikipedia.org/wiki/Normal_distribution)
+* [Gamma](https://en.wikipedia.org/wiki/Gamma_distribution)
+* [Pareto](https://en.wikipedia.org/wiki/Pareto_distribution)
+* [Beta](https://en.wikipedia.org/wiki/Beta_distribution)
+
+It is easy to add more univariate distributions by supplying an implementation of a PDF or wrapping one from the excellent [statrs](https://crates.io/crates/statrs) crate.
+
 # Example
-Draw samples from the Gaussian distribution.
+Draw samples from the Gaussian distribution using MCMC.
 
 ```rust
 use std::ops::RangeInclusive;