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We present icarogw 2.0, a pure CPU/GPU python code developed to inferastrophysical and cosmological population properties of noisy, heterogeneous,and incomplete observations. icarogw 2.0 is mainly developed for compact binarycoalescence (CBC) population inference with gravitational wave (GW)observations. The code contains several models for masses, spins, and redshiftof CBC distributions, and is able to infer population distributions as well asthe cosmological parameters and possible general relativity deviations atcosmological scales. We present the theoretical and computational foundationsof icarogw 2.0, and we describe how the code can be employed for population andcosmological inference using (i) only GWs, (ii) GWs and galaxy surveys and(iii) GWs with electromagnetic counterparts. We discuss the code performance onGraphical Processing Units (GPUs), finding a gain in computation time of abouttwo orders of magnitudes when more than 100 GW events are involved for theanalysis. We validate the code by re-analyzing GW population and cosmologicalstudies, finding very good agreement with previous publications.

ICAROGW: A python package for inference of astrophysical population properties of noisy, heterogeneous and incomplete observations