fit a multvariate-t mixture model in r

To fit a multivariate-t mixture model in R, we can use the package "mixtools". Specifically, the function "normalmixEM" can be used to fit a mixture of normal distributions, and the argument "component" can be set to "t" to fit a mixture of t-distributions.

Here's an example code:

main.r
library(mixtools)
data <- mtcars[, 1:6] # subset of mtcars data
fit <- normalmixEM(data, k = 3, maxit = 1000, epsilon = 1e-3, component = "t")
summary(fit)
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In this example, the "data" object contains the input data that we want to fit a mixture model to (here, a subset of the built-in "mtcars" dataset). The argument "k" specifies the number of mixture components to fit, "maxit" specifies the maximum number of iterations for the EM algorithm, "epsilon" is the convergence criterion for the log-likelihood, and "component" is set to "t" to fit a mixture of t-distributions.

The "fit" object contains the resulting mixture model, and we can use the "summary" function to obtain a summary of the model. This summary provides estimates of the mixing proportions, means, and covariance matrices for each mixture component, as well as the degrees of freedom for each t-distribution.