dftools-package: Fitting distribution functions such as galaxy mass functions
In obreschkow/dftools: Fitting distribution functions such as galaxy mass functions

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The package can infer the most likely P parameters of a D-dimensional distribution function (DF) model generating N objects with D-dimensional observables. For instance, if the objects are galaxies, it can fit a mass function (P=1), a mass-size distribution (P=2) or the mass-spin-morphology distribution (P=3). Fits are performed using the modified maximum likelihood (MML) formalism with the fit-and-debias algorithm, described in Obreschkow et al. (2017). Unlike most common method for statistical inference, this method accurately accounts for measurement is uncertainties and complex selection functions. In the context of astrophysical applications, such as fitting a galaxy mass function, the routines can also correct for cosmic large-scale structure, inherent to any galaxy survey. The package contains the following routines:

dffit is the core routine, used to fit model parameters to a set of observations.

dfplot, mfplot, dfplot2 are plotting functions that use the output argument of dffit as primary input argument. These functions can visualize the input data and fitted model in a single plot.

dfplotveff and dfplotveff2 are a second set of plotting functions that use the output argument of dffit as primary input argument. These functions visualize the effective volume (= weight) as a function of the observables.

dfmodel and dfswmodel produce analytical distribution functions, such as galaxy mass functions, to be fitted by dffit.

dfwrite takes the output argument of dffit and writes the best-fitting solution and additional information on the screen.

dfmockdata produces a mock survey given a generative DF, such as a galaxy mass function, and a selection function.

dfexample allows the user to run a few illustrative examples of fitting galaxies.