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UnBBayes is a probabilistic network framework written in Java. It has both a GUI and an API with inference, sampling, learning and evaluation. It supports Bayesian networks, influence diagrams, MSBN, OOBN, HBN, MEBN/PR-OWL, PRM, structure, parameter and incremental learning. Please, visit our wiki (https://sourceforge.net/p/unbbayes/wiki/Home/) for more information. Check out the license section (https://sourceforge.net/p/unbbayes/wiki/License/) for our licensing policy.

***WARNING*** This software was migrated to: https://github.com/popgenmethods/spectralHMM Support and updates will only be available at this new address. This software implements the algorithms described in the following paper: Steinrücken, M., Bhaskar, A. and Song, Y.S. A novel spectral method for inferring general diploid selection from time series genetic data. Annals of Applied Statistics, Vol. 8, No. 4 (2014) 2203-2222

Realtime bigdata tool at the bit level based on immutable AVL forest which can be run in memory or, in future versions, as a merkle forest like a blockchain. Main object is a sparse bit string (Bits) that efficiently scales up to 2^63 bits normally compressed as forest has duplicated substrings. Bits objects support reading bit, byte, short, int, or long (Java primitives) at any bit index in 64 bit range. Example: instead of building a class to hold a header and then data, represent all of that as Bits, subranges of them, and ints for sizes of its parts. Expansion ability for other kinds of compression, since Bits is a Java interface. Main functions on bits are substring, concat, number of 0 or 1 bits, and number of bits (size). ...

This project is a prototype of a framework for Social Network Analysis created at INRIA. It provides methods for fetching, preparing, clustering and analyzing data from online Social Networks. Currently it has support only for the Twitter network.