**bmixture**

The `R`

package **bmixture** provides
statistical tools for Bayesian estimation for the mixture of
distributions. The package implemented the improvements in the Bayesian
literature, including Mohammadi
et al. (2013) and Mohammadi
and Salehi-Rad (2012). Besides, the package contains several
functions for simulation and visualization, as well as a real dataset
taken from the literature.

## Installation

You can install the latest version from CRAN using:

`install.packages( "bmixture" )`

## Example
1: Finite mixture of Normal distributions using real world data

Here is a simple example to see the performance of the package for
the Finite mixture of Normal distributions for the `galaxy`

dataset:

```
data( galaxy )
# Runing bdmcmc algorithm for the galaxy dataset
mcmc_sample = bmixnorm( data = galaxy )
summary( mcmc_sample )
plot( mcmc_sample )
print( mcmc_sample )
```

## Example
2: Finite mixture of Normal distributions using simulatoin data

Here is a simple example to see the performance of the package for
the Finite mixture of Normal distributions using simulation data. First,
we simulate data from the mixture of Normal with 3 components as
follow:

```
n = 500
mean = c( 0 , 10 , 3 )
sd = c( 1 , 1 , 1 )
weight = c( 0.3, 0.5, 0.2 )
data = rmixnorm( n = n, weight = weight, mean = mean, sd = sd )
# plot for simulation data
hist( data, prob = TRUE, nclass = 30, col = "gray" )
x = seq( -20, 20, 0.05 )
densmixnorm = dmixnorm( x, weight, mean, sd )
lines( x, densmixnorm, lwd = 2 )
```

Now, we run the ‘bdmcmc’ algorithm for the above simulation data
set

```
bmixnorm.obj = bmixnorm( data, k = 3, iter = 1000 )
summary( bmixnorm.obj )
```