README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r echo=FALSE, message=FALSE}
set.seed(2020)
require(dirichletprocess)
require(ggplot2)
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "vignettes/img/"
)
```

# dirichletprocess

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The dirichletprocess package provides tools for you to build custom Dirichlet process mixture models. You can use the pre-built Normal/Weibull/Beta distributions or create your own following the instructions in the vignette. In as little as four lines of code you can be modelling your data nonparametrically.

## Installation

You can install the stable release of dirichletprocess from CRAN:

```{r, eval=FALSE}
install.packages("dirichletprocess")
```

You can also install the development build of dirichletprocess from github with:

```{r gh-installation, eval = FALSE}
# install.packages("devtools")
devtools::install_github("dm13450/dirichletprocess")
```

For a full guide to the package and its capabilities please consult the vignette:

```{r vignette, eval=FALSE}
browseVignettes(package = "dirichletprocess")
```

## Examples

### Density Estimation

Dirichlet processes can be used for nonparametric density estimation. 

```{r density, eval=FALSE}
faithfulTransformed <- faithful$waiting - mean(faithful$waiting)
faithfulTransformed <- faithfulTransformed/sd(faithful$waiting)
dp <- DirichletProcessGaussian(faithfulTransformed)
dp <- Fit(dp, 100, progressBar = FALSE)
plot(dp)
```

<img src=https://github.com/dm13450/dirichletprocess/raw/master/vignettes/img/density-1.png width=50% />


### Clustering

Dirichlet processes can also be used to cluster data based on their common distribution parameters. 

```{r clustering, eval=FALSE}
faithfulTrans <- scale(faithful)
dpCluster <-  DirichletProcessMvnormal(faithfulTrans)
dpCluster <- Fit(dpCluster, 2000, progressBar = FALSE)
plot(dpCluster)
```

<img src=https://github.com/dm13450/dirichletprocess/raw/master/vignettes/img/clustering-1.png width=50% />

For more detailed explanations and examples see the vignette.

### Tutorials

I've written a number of tutorials: 

* [Non parametric priors](https://dm13450.github.io/2019/02/22/Nonparametric-Prior.html)
* [Calculating cluster probabilities](https://dm13450.github.io/2018/11/21/Cluster-Probabilities.html)
* [Clustering](https://dm13450.github.io/2018/05/30/Clustering.html)
* [Point processes](https://dm13450.github.io/2018/03/08/dirichletprocess-pointprocess.html)
* [Custom mixtures](https://dm13450.github.io/2018/02/21/Custom-Distributions-Conjugate.html)
* [Density estimation](https://dm13450.github.io/2018/02/01/Dirichlet-Density.html)
* [Checking convergence](https://dm13450.github.io/2020/01/11/Dirichlet-Convergence.html)

and some case studies: 

* [State of the Market - Infinite State Hidden Markov Models](https://dm13450.github.io/2020/06/03/State-of-the-Market.html)
* [Palmer Penguins and an Introduction to Dirichlet Processes](https://dm13450.github.io/2020/09/28/PriorToPosterior.html)