DarkSirensStat statistically measures modified gravitational wave (GW) propagation and the Hubble parameter. The package implements a hierarchical Bayesian framework for constraining the Hubble parameter and modified GW propagation with dark sirens and galaxy catalogs. The package downloads the needed data; which include the GLADE galaxy catalog, O2 and O3 skymaps from the LVC official data releases, and O2 and O3 strain sensitivities. The default options are for running inference for H0 on the O3 BBH events, with flat prior between 20 and 140, mask completeness with 9 masks, interpolation between multiplicative and homogeneous completion, B-band luminosity weights, and a completeness threshold of 50%. The selection effects are computed with MC.

GWFAST forecasts the signal-to-noise ratios and parameter estimation capabilities of networks of gravitational-wave detectors, based on the Fisher information matrix approximation. It is designed for applications to third-generation gravitational-wave detectors. It is based on Automatic Differentiation, which makes use of the library JAX (ascl:2111.002). This allows efficient parallelization and numerical accuracy. The code includes a module for parallel computation on clusters.

MGCosmoPop implements a hierarchical Bayesian inference method for constraining the background cosmological history, in particular the Hubble constant, together with modified gravitational-wave propagation and binary black holes population models (mass, redshift and spin distributions) with gravitational-wave data. It includes support for loading and analyzing data from the GWTC-3 catalog as well as for generating injections to evaluate selection effects, and features a module to run in parallel on clusters.

EF-TIGRE (Effective Field Theory of Interacting dark energy with Gravitational REdshift) constrains interacting Dark Energy/Dark Matter models in the Effective Field Theory framework through Large Scale Structures observables. In particular, the observables include the effect of gravitational redshift, a distortion of time from galaxy clustering. This generates a dipole in the correlation function which is detectable with two distinct populations of galaxies, thus making it possible to break degeneracies among parameters of the EFT description.

BaCoN (BAyesian COsmological Network) trains and tests Bayesian Convolutional Neural Networks in order to classify dark matter power spectra as being representative of different cosmologies, as well as to compute the classification confidence. It supports the following theories: LCDM, wCDM, f(R), DGP, and a randomly generated class. Additional cosmologies can be easily added.