**➥ Tip!** Refine or expand your search. Authors are sometimes listed as 'Smith, J. K.' instead of 'Smith, John' so it is useful to search for last names only. Note this is currently a simple phrase search.

[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:1503.006]
AMADA: Analysis of Multidimensional Astronomical DAtasets

AMADA 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.

[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.

[ascl:2208.002]
qrpca: QR-based Principal Components Analysis

qrpca uses QR-decomposition for fast principal component analysis. The software is particularly suited for large dimensional matrices. It makes use of torch for internal matrix computations and enables GPU acceleration, when available. Written in both R and python languages, qrpca provides functionalities similar to the prcomp (R) and sklearn (python) packages.