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[ascl:1408.018]
CosmoPhotoz: Photometric redshift estimation using generalized linear models

de Souza, Rafael S.; Elliott, Jonathan; Krone-Martins, Alberto; Ishida, Emille E. O.; Hilbe, Joseph; Cameron, Ewan

CosmoPhotoz determines photometric redshifts from galaxies utilizing their magnitudes. The method uses generalized linear models which reproduce the physical aspects of the output distribution. The code can adopt gamma or inverse gaussian families, either from a frequentist or a Bayesian perspective. A set of publicly available libraries and a web application are available. This software allows users to apply a set of GLMs to their own photometric catalogs and generates publication quality plots with no involvement from the user. The code additionally provides a Shiny application providing a simple user interface.

[ascl:1505.013]
cosmoabc: Likelihood-free inference for cosmology

Ishida, Emille E. O.; Vitenti, Sandro D. P.; Penna-Lima, Mariana; Trindade, Arlindo M.; Cisewski, Jessi; M.; de Souza, Rafael; Cameron, Ewan; Busti, Vinicius C.

Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationally too expensive. It relies on the forward simulation of mock data and comparison between observed and synthetic catalogs. cosmoabc is a Python Approximate Bayesian Computation (ABC) sampler featuring a Population Monte Carlo variation of the original ABC algorithm, which uses an adaptive importance sampling scheme. The code can be coupled to an external simulator to allow incorporation of arbitrary distance and prior functions. When coupled with the numcosmo library, it has been used to estimate posterior probability distributions over cosmological parameters based on measurements of galaxy clusters number counts without computing the likelihood function.

[ascl:1512.009]
DRACULA: Dimensionality Reduction And Clustering for Unsupervised Learning in Astronomy

Aguena, Michel; Busti, Vinicius C.; Camacho, Hugo; Sasdelli, Michele; Ishida, Emille E. O.; Vilalta, Ricardo; Trindade, Arlindo M. M.; Gieseke, Fabien; de Souza, Rafael S.; Fantaye, Yabebal T.; Mazzali, Paolo A.

DRACULA classifies objects using dimensionality reduction and clustering. The code has an easy interface and can be applied to separate several types of objects. It is based on tools developed in scikit-learn, with some usage requiring also the H2O package.