MixFit.jl is a Julia package for fitting finite mixture models, and for nonparametric density estimation via finite mixture models (Wang & Chee, 2012). Models are fitted using random-swap expectation maximization (Zhao, Hautamäki, Kärkkäinen, & Fränti, 2012), which avoids the problem of local optimums by randomly replacing components and using the final maximum. By default, MixFit.jl supports normal, gamma, lognormal, and gumbel mixtures, but it is easy to use your own density function instead. If you use this, it would be nice if you cited me, but I'm putting it under public domain so you can do whatever you want with it.
julia> using MixFit
julia> x = [randn(500); (randn(500).+3).*2]
1000-element Array{Float64,1}:
-0.3872542393168341
0.44218146773613404
-1.140006685489404
-0.11093365262262657
0.917287805330094
1.3997276699755827
⋮
3.860428712486353
4.924743765884938
4.767237225983121
3.6111782228201013
5.498455397469906
julia> densfit(x, criterion = AIC3)
----------------------------------
Log-likelihood: -2408.562390297034
AIC: 4827.124780594068
AIC3: 4832.124780594068
BIC: 4851.6635569889795
Component 1:
α: 0.5093992
μ: 0.07551261
σ: 1.0396444
Component 2:
α: 0.49060085
μ: 6.004384
σ: 2.0322912
----------------------------------
MixModel(Float32[0.5093992, 0.49060085], Float32[0.07551261, 6.004384], Float32[1.0396444, 2.0322912], MixFit.dnorm)
julia>