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{"name":"COINtoolbox","tagline":"","body":"[![DOI](https://zenodo.org/badge/7175/COINtoolbox/COINtoolbox.github.io.svg)](https://doi.org/10.5281/zenodo.16376)\r\n<img src=\"www/COIN.jpg\", class=\"inline\"/>\r\n\r\n# Methodology and software for cosmology\r\n\r\n\r\n> The COsmostatistics INitiative ([COIN](https://asaip.psu.edu/organizations/iaa/iaa-working-group-of-cosmostatistics/)), a working group built within the International Astrostatistics Association\r\n([IAA](https://asaip.psu.edu/organizations/iaa/international-astrostatistics-association-overview\r\n)), aims to create a friendly environment where hands-on collaboration between astronomers,\r\ncosmologists, statisticians and machine learning experts can flourish. COIN is designed to\r\npromote the development of a new family of tools for data exploration in cosmology. \r\n\r\n\r\n## Generalized Linear Models in Astronomy\r\n\r\nStatistical methods play a central role  to fully exploit astronomical catalogues and an  efficient  data analysis requires astronomers  to go beyond the traditional Gaussian-based models. This projects illustrates the power of generalized linear models (GLMs) for astronomical community,  from a Bayesian perspective.  Applications range from modelling star formation activity (logistic regression), globular cluster population (negative binomial regression), photometric redshifts (gamma regression), exoplanets multiplicity (Poisson regression), and so forth.\r\n\r\n### Binomial Regression\r\n\r\n* [Paper](http://adsabs.harvard.edu/abs/2014arXiv1409.7696D)\r\n\r\n* [Tutorial]()\r\n\r\n### Gamma Regression\r\n\r\n* [Paper](http://adsabs.harvard.edu/abs/2015A%26C....10...61E)\r\n\r\n* [Package](http://ascl.net/1408.018)\r\n\r\n* [Tutorial](http://cosmophotoz.readthedocs.org/en/latest/)\r\n\r\n* [Web-interface](https://cosmostatisticsinitiative.shinyapps.io/CosmoPhotoz)\r\n\r\n\r\n\r\n## Aproximate Bayesian Computation\r\n\r\n* [Package](https://pypi.python.org/pypi/CosmoABC)\r\n\r\n* [Tutorial](http://cosmoabc.readthedocs.org/en/latest/)\r\n\r\n## Analysis of Muldimensional Astronomical DAtasets (AMADA)\r\n\r\nAMADA allows an iterative exploration and information retrieval of high-dimensional data sets. This is done by performing a hierarchical clustering analysis for different choices of correlation matrices and by doing a principal components analysis in the original data. Additionally, AMADA provides a set of modern visualization data-mining diagnostics. The user can switch between them using the different tabs.\r\n\r\n* [Package](http://rafaelsdesouza.github.io/AMADA/)\r\n\r\n* [Web App](https://cosmostatisticsinitiative.shinyapps.io/AMADA/)\r\n\r\n---\r\n#### Contact: <rafael.2706@gmail.com>\r\n\r\n","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."}