Showing 206 open source projects for "bayesian"

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  • 1
    The JAGS ALCOVE module is an extension for JAGS, which provides functions to enable a bayesian analysis with the ALCOVE model. See the README in Code for usage details.
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  • 2
    CABBaGe

    CABBaGe

    Classification Algorithm Based on a Bayesian method for Genomics

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  • 3

    BisSNP

    Bisulfite-seq/NOMe-seq SNPs & cytosine methylation caller

    Now in Github: https://github.com/dnaase/Bis-tools/tree/master/Bis-SNP BisSNP is a package based on the Genome Analysis Toolkit (GATK) map-reduce framework for genotyping in bisulfite treated massively parallel sequencing (Bisulfite-seq, NOMe-seq and RRBS) on Illumina platform. It uses bayesian inference with either manually specified or automatically estimated methylation probabilities of different cytosine context(not only CpG, CHH, CHG in Bisulfite-seq, but also GCH et.al. in other bisulfite treated sequencing) to determine genotypes and methylation levels simultaneously. It works for both of single-end and paired-end reads.Specificity and sensitivity has been validate by Illumina IM SNP array. ...
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  • 4
    ...Currently, it includes the software systems KReator and MECore and the library Log4KR: - KReator is an integrated development environment (IDE) for relational probabilistic knowledge representation languages such as Bayesian Logic Programs (BLPs), Markov Logic Networks (MLNs), Relational Maximum Entropy (RME), First-Order Probabilistic Conditional Logic (FO-PCL), and others. - MECore is a shell-based system that allows the user to create propositional knowledge bases, to perform a variety of belief change operations, and to query a knowledge base with respect to the principle of optimum entropy...
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  • 5
    faif

    faif

    C++ header only library with AI and bioinformatics algorithms

    C++ header only library, small and fast; Naive Bayesian Classifier, Decision Tree Classifier (ID3), DNA/RNA nucleotide second structure predictor, timeseries management, timeseries prediction, generic Evolutionary Algorithm, generic Hill Climbing algorithm and others.
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  • 6
    PrivBayes

    PrivBayes

    Private Data Release via Bayesian Networks

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  • 7
    BATS

    BATS

    Bayesian Adaptive Trial Simulator

    A user-friendly, quick simulator for Bayesian Multi-Arm Multi-Stage Trials
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  • 8

    BayeFM-MCMC-v1.0

    Fine mapping using Bayesian MCMC

    A new fine mapping method for detecting causitive variants (clusters) using Bayesian MCMC for complex traits. It is typically suitable to the GWAS identified region, which typically confines the causitive variant in a small region, say 1Mb or even small region. If there are enough variants are genotyped or imputed with high quality. The software is able to generate a set of credible sets t refine the causitive variants.
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  • 9

    EBprotV2

    Bayesian Analysis of labeling-based Quantitative Proteomics Data

    EBprotV2 is an extension to the existing EBprot (version 1) which adopts a more flexible non-parametric mixture model. It serves as an differential expression analysis tool which takes into account the direct peptide to protein hierarchy which differs from the usual analysis which occurs at the protein-summary level. The tool is implemented in C++ which minimize computational time and also, incorporates an additional module which help users to generate comparison ratios between groups with...
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  • 10

    EBprot

    Bayesian Analysis of labeling-based Quantitative Proteomics Data

    EBprot implements a hierarchical mixture model directly representing the peptide-protein map. The model fit is used to score differential expression of proteins in a (signed) probability scale based on reproducible quantification over multiple peptides. The package takes as input a data matrix with each row containing the data for one peptide, including protein and peptide identifier and peptide ratio(s), and returns the protein-level score table with PPscore, BFDR, Posterior Odds score, and...
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  • 11
    BayesRate

    BayesRate

    Bayesian estimation of diversification rates

    BayesRate is a program to estimate speciation and extinction rates from dated phylogenies in a Bayesian framework. The methods are described in: Silvestro, D., Schnitzler, J. and Zizka, G. (2011) A Bayesian framework to estimate diversification rates and their variation through time and space. BMC Evolutionary Biology, 11, 311 Silvestro D., Zizka G. & Schulte K. (2014) Disentangling the effects of key innovations on the diversification of Bromelioideae (Bromeliaceae). ...
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  • 12

    Pathoscope

    Predicts strains of genomes in Nextgen seq alignment file (sam/bl8)

    ...Please visit github for the latest version of the software: https://github.com/pathoscope PathoScope takes next-generation sequencing reads from a mixture sample and predicts which genomes are present. We use a Bayesian framework combined with an initial reference-based alignment to assign reads to the correct genome of origin. Pathoscope 2.0: Wiki: http://sourceforge.net/p/pathoscope/wiki/Home/ Tutorial: http://sourceforge.net/projects/pathoscope/files/pathoscope2.0_v0.02_tutorial.pdf Clinical Pathoscope version: http://sourceforge.net/p/pathoscope/wiki/clinical_pathoscope/ PathoQC download: http://sourceforge.net/projects/pathoscope/files/pathoqc_v0.1.4.tar.gz/download Publications: http://www.microbiomejournal.com/content/2/1/33 http://www.biomedcentral.com/1471-2105/15/262 http://genome.cshlp.org/content/23/10/1721 Support: PS: Please send an email to mani2012 at bu dot edu if you have any questions.
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  • 13

    Pyclamp

    A Python package used to extract and analyse electrophysiological data

    ...Presently, Pyclamp is designed to perform very specific forms of analysis on evoked synaptic responses: Data analysis (under development) : This is a highly user-interactive en-vironment that can be used to discriminate synaptic events, obtain various measures of their kinetics and size, and output the results. Quantal analysis : This part of the package performs simple variance-mean analysis and Bayesian quantal analysis to estimate the quantal sizea dn number of release sites among other measures.
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  • 14
    BPL

    BPL

    Bayesian Program Learning model for one-shot learning

    BPL (Bayesian Program Learning) is a MATLAB implementation of the Bayesian Program Learning framework for one-shot concept learning (especially on handwritten characters). The approach treats each concept (e.g. a character) as being generated by a probabilistic program (motor primitives, strokes, spatial relationships), and inference proceeds by fitting those generative programs to a single example, generalizing to new examples, and generating new exemplars.
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  • 15

    BNP-Homology

    Alignment data for Protein Remote Homology Search

    Alignment data from the application of a Bayesian mixture model to protein remote homology detection.
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  • 16
    RStan

    RStan

    RStan, the R interface to Stan

    ...It is used in research, applied statistics, and modelling workflows where flexibility and rigor in Bayesian methods are required.
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  • 17

    BPandP

    Bayesian MCMC algorithms for the analysis of phylogeographic DNA data

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  • 18

    PyRate

    Bayesian Estimation of Speciation and Extinction from Fossil Data

    PyRate is a Python program to estimate speciation, extinction, and preservation rates from fossil occurrence data using a Bayesian framework. The method was described by D Silvestro, J Schnitzler, LH Liow, A Antonelli, and N Salamin in Systematic Biology (http://sysbio.oxfordjournals.org/content/early/2014/02/08/sysbio.syu006.abstract). *Please download the most up-to-date code from the "PyRate code" tab on this page or from: https://github.com/dsilvestro/PyRate * *An updated manual can be found here: https://github.com/dsilvestro/PyRate/tree/master/tutorials *
    Downloads: 2 This Week
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  • 19
    RadicalSpam Virtual Appliance

    RadicalSpam Virtual Appliance

    Virtual Appliance of RadicalSpam

    RadicalSpam Virtual Appliance takes full solution of RadicalSpam Community Edition , pre-installed in a OVF virtual machine ( Open Virtual Format ) compatible with the best virtualization platforms on the market , including VMware ESX Server. More information : http://www.radical-spam.org
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  • 20
    Problem Description: 20 newsgroup Classification problem Bayesian learning for classifying net news text articles: Naive Bayes classifiers are among the most successful known algorithms for learning to classify text documents. We will provide a data set containing 20,000 newsgroup messages drawn from the 20 newsgroups. The dataset contains 1000 documents from each of the 20 newsgroups. 1.
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  • 21
    RadicalSpam

    RadicalSpam

    Open Source Anti-Spam and Anti-Virus Gateway

    RadicalSpam is a free and open source package distributed under GPL v2, including products such as Postfix, SpamAssassin Amavisd-new, Clamav, Razor, DCC, Postgrey, Bind; providing a secure SMTP relay, ready to use with linux and docker environement. More information : http://www.radical-spam.org
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  • 22
    ...The second package includes source and object files of DEMass-DBSCAN to be used with the WEKA system. 3. The third package DEMassBayes includes the source and object files of a Bayesian classifier using DEMass. DEMassBayes.7z has jar file to be used with WEKA and a readme file listing parameters used. The source files are included in DEMassBayes_Source.7z. 4. The four package is MassTER includes source and JAR file to be used with WEKA system..
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  • 23
    The Java Data Mining Package (JDMP) is a library that provides methods for analyzing data with the help of machine learning algorithms (e.g. clustering, classification, graphical models, neural networks, Bayesian networks, text processing, optimization).
    Downloads: 1 This Week
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  • 24

    Bayesian Analysis: Neutron Stars, M & R

    Bayesian Analysis of Neutron Star Mass and Radius Observations

    A Bayesian analysis of neutron star mass and radius observations based on O2scl.
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  • 25
    Mocapy++
    Mocapy++ is a Dynamic Bayesian Network toolkit, implemented in C++. It supports discrete, multinomial, Gaussian, Kent, Von Mises and Poisson nodes. Inference and learning is done by Gibbs sampling/Stochastic-EM.
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