# bmixture    The `R` package bmixture provides statistical tools for Bayesian estimation in finite 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

``install.packages( "bmixture" )``
``require( "bmixture" )``

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

Here is a simple example to see the preformance 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 preformance 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 ) ``````