# fipp

The goal of fipp is to provide tools to carry out sanity checks on
mixture models when used for Model-Based Clustering.

Specifically, it deals with characterizing implicit quantities
obtained from prior distributions of either of the following three
models: Dirichlet Process Mixtures (DPM), Static Mixture of Finite
Mixtures (Static MFM) and Dynamic Mixture of Finite Mixtures (Dynamic
MFM).

## Installation

You can install the released version of fipp from CRAN with:

## Example:
the number of filled mixture components (in other words data
clusters)

One of the functions in the package allows the user to obtain the
number of filled mixture components. Note that it shouldnâ€™t be confused
with the number of mixture components. The former quantity is equal or
less than the latter where equality holds when at least one data point
is associated to any of the mixture components in the model. For
details, please refer to the vignette provided in the next section.

Here, we demonstrate how one can obtain the prior distribution of
filled mixture components for the DPM under a specific setting (the
concentration parameter (= 1) and when the sample size (N = 100)).

```
library(fipp)
## DPM w/ alpha = 1, N = 100, evaluate up to 30
pmfDPM <- nClusters(Kplus = 1:30, type = "DPM", alpha = 1, N = 100)
barplot(pmfDPM(),
main = expression("DPM (" * alpha == 1 * ") with N = 100"),
xlab = "number of filled components", ylab = "probability")
```

## Details

For more detailed description regarding the functionality of the
package, please refer to the vignette below:

fipp
Crash Course