Corrfunc is a suite of high-performance clustering routines. The code can compute a variety of spatial correlation functions on Cartesian geometry as well Landy-Szalay calculations for spatial and angular correlation functions on a spherical geometry and is useful for, for example, exploring the galaxy-halo connection. The code is written in C and can be used on the command-line, through the supplied python extensions, or the C API.

DARK SAGE is a semi-analytic model of galaxy formation that focuses on detailing the structure and evolution of galaxies' discs. The code-base, written in C, is an extension of SAGE (ascl:1601.006) and maintains the modularity of SAGE. DARK SAGE runs on any N-body simulation with trees organized in a supported format and containing a minimum set of basic halo properties.

TesseRACt computes concentrations of simulated dark matter halos from volume information for particles generated using Voronoi tesselation. This technique is advantageous as it is non-parametric, does not assume spherical symmetry, and allows for the presence of substructure. TesseRACt accepts data in a number of formats, including Gadget-2 (ascl:0003.001), Gasoline (ascl:1710.019), and ASCII, and computes concentrations using particles volumes, traditional fitting to an NFW profile, and non-parametric techniques that assume spherical symmetry.

pycorr wraps two-point counter engines such as Corrfunc (ascl:1703.003) to estimate the correlation function. It supports theta (angular), s, s-mu, rp-pi binning schemes, analytical two-point counts with periodic boundary conditions, and inverse bitwise weights (in any integer format) and (angular) upweighting. It also provides MPI parallelization and jackknife estimate of the correlation function covariance matrix.