GalaxyMC is a parallelized Fortran code with a Python interface to semi-analytically model, predict and analyze high-redshift (z>4) galaxy samples.
the cloud-based computing service is available HERE
lead developer: Martin Sahlén, Uppsala University. 

GalaxyMC is highly flexible and modular and can easily be adapted to semi-analytically model generic cosmological number counts and correlation functions of tracers of the matter distribution. Interest in using the code is gladly received.

Press-Schechter luminosity functionsluminosity function
global reionization signal
Time per bin, 1 CPU
Multiple CPUs
t1 ~0.01 – 0.1 s
t ~ Nbins x t1 / NCPUs
Halo mass function + star formation rate modelsluminosity function
stellar mass function
star formation rate
duty cycles
Dust extinctionStandard parameterizations or arbitrary distribution
Selection function / contaminationArbitrary distributions
Gravitational lensingWeak: magnification bias, analytical models or fitted model
Strong: cluster-lensed observations, arbitrary magnification distribution
Statistical uncertaintiesMalmquist bias: statistical scatter in luminosity function
Eddington bias: asymmetric distribution / measurement uncertainties
Full Bayesian MCMC parameter fittingIntegrated with the CosmoMC package or Python interface to use e.g. Emcee
Fisher-matrix forecast computation
Generation of mock galaxy catalogues
Combination with other astrophysical and cosmological data sets 

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