Search Results for "bayesian"
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Showing 48 open source projects for "bayesian"
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PyMC
Bayesian Modeling and Probabilistic Programming in Python
PyMC is a Python library for probabilistic programming focused on Bayesian statistical modeling and machine learning. Built on top of computational tools like Aesara and NumPy, PyMC allows users to define models using intuitive syntax and perform inference using MCMC, variational inference, and other advanced algorithms. It’s widely used in scientific research, data science, and decision modeling. -
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CausalImpact
An R package for causal inference in time series
...It uses Bayesian modeling to fit a structural time series to the pre-period and extrapolate a counterfactual prediction for the post period, then compares observed vs predicted to infer the causal effect. The package supports plotting, summary tables, and verbal narratives for interpretive reports. -
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awesome-single-cell
Community-curated list of software packages and data resources
Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc. List of software packages (and the people developing these methods) for single-cell data analysis, including RNA-seq, ATAC-seq, etc. Rapid, accurate and memory-frugal preprocessing of single-cell and single-nucleus RNA-seq data. Find bimodal, unimodal, and multimodal features in your data. Ascend is an R package comprised of fast, streamlined analysis functions optimized to... -
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BACE for gretl
Bayesian Averaging of Classical Estimates
Bayesian Averaging of Classical Estimates package. -
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Bayes+Estimate is a Rust and C++ library that implement numerical algorithms for Bayesian estimation. They provide tested and consistent numerical methods and represents the wide variety of Bayesian estimation algorithms and system model.
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UnBBayes
Framework & GUI for Bayes Nets and other probabilistic models.
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. -
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JAGS is Just Another Gibbs Sampler. It is a program for the statistical analysis of Bayesian hierarchical models by Markov Chain Monte Carlo.
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STK
a Small (Matlab/Octave) Toolbox for Kriging
...Its primary focus in on the interpolation / regression technique known as kriging, which is very closely related to Splines and Radial Basis Functions, and can be interpreted as a non-parametric Bayesian method using a Gaussian Process (GP) prior. The STK also provides tools for the sequential and non-sequential design of experiments. Even though it is, currently, mostly geared towards the Design and Analysis of Computer Experiments (DACE), the STK can be useful for other applications areas (such as Geostatistics, Machine Learning, Non-parametric Regression, etc.). -
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R packages (maintained by YJLEE)
R packages for PK/PD modeling , BE/BA, drug stability, ivivc, etc.
These R packages are developed for data analysis of PK/PD modeling & simulation, bioequivalence/bioavailability (BE/BA), drug stability, in-vitro and in-vivo correlation (ivivc), as well as therapeutic drug monitoring (TDM). -
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Habfuzz
A command-line tool for data-driven fuzzy modelling
Input 1 - A training dataset (multiple observations) of up to four variables (predictors) against one (response variable) Input 2 - A test dataset (multiple observations) of the same four variables with unknown response variable Output - Calculation of the response variable for each test observation using fuzzy logic or fuzzy rule-based Bayesian algorithms HABFUZZ is a habitat model, which can be used in ecohydraulic modelling applications for the calculation of the instream habitat suitability in various discharge scenarios in a simulated river reach. It comes with no graphical user interface but it's a one-click tool. Just provide your input and let HABFUZZ provide you the output. ... -
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...Citation: 1. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007 Aug;73(16):5261-7. 2. Wang Q, Cole JR. Updated RDP taxonomy and RDP Classifier for more accurate taxonomic classification. Microbiol Resour Announc. 2024 Apr 11;13(4):e0106323.
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rethinking
Statistical Rethinking course and book package
This R package accompanies Richard McElreath’s Statistical Rethinking (2nd edition), offering utilities to fit and compare Bayesian models using both MAP estimation (quap) and Hamiltonian Monte Carlo via RStan (ulam). It supports specifying models via explicit distributional assumptions, providing flexibility for advanced statistical workflows. -
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CABBaGe
Classification Algorithm Based on a Bayesian method for Genomics
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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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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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BayesAss
Bayesian Inference of Recent Migration Using Multilocus Genotypes
Inference of recent migration rates between populations using multi-locus marker genotype data. -
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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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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 * -
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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).
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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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StatAlign
An extendable statistical multiple alignment tool.
StatAlign is an extendable software package for Bayesian analysis of Protein, DNA and RNA sequences. Multiple alignments, phylogenetic trees and evolutionary parameters are co-estimated in a Markov Chain Monte Carlo framework, allowing for reliable measurement of the accuracy of the results. This approach eliminates common artifacts that traditional methods suffer from, at the cost of increased computational time. -
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BiomeNet
BAYESIAN INFERENCE OF METABOLIC DIVERGENCE AMONG MICROBIAL COMMUNITIES
...Using such data to infer community-level metabolic divergence is hindered by the lack of a suitable statistical framework. Here, we describe a novel hierarchical Bayesian model, called BiomeNet (Bayesian inference of metabolic networks), for inferring differential prevalence of metabolic networks among microbial communities. To infer the structure of community-level metabolic interactions, BiomeNet applies a mixed-membership modelling framework to enzyme abundance information. The basic idea is that the mixture components of the model (metabolic reactions, subnetworks, and networks) are shared across all groups (microbiome samples), but the mixture proportions vary from group to group. ... -
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bagemass
Bayesian age and mass estimates for transiting planet host stars
Source code, makefile and README for installation of software used for the analysis in Maxted, Serenelli & Southworth, "Bayesian mass and age estimates for transiting exoplanet host stars", A&A 575, 36, 2015. -
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FamSeq
Variant calling on the basis of pedigree information
...FamSeq accommodates de novo mutations and can perform variant calling at chromosome X. To accommodate variations in data complexity, FamSeq consists of three distinct implementations of the Mendelian genetic model: the Bayesian network algorithm, Elston-Stewart algorithm and Markov chain Monte Carlo algorithm. To make the software efficient and applicable to large families, we parallelized the Bayesian network algorithm that copes with pedigrees with inbreeding loops without losing calculation precision on an NVIDIA® graphics processing unit. -
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phcfM
R package for modelling anthropogenic deforestation
...It was named after the REDD+ pilot-project 'programme holistique de conservation des forêts à Madagascar'. phcfM includes two main functions: (i) demography(), to model the population growth with time in a hierarchical Bayesian framework using population census data and Gaussian linear mixed models and (ii) deforestation(), to model the deforestation process in a hierarchical Bayesian framework using land-cover change data and Binomial logistic regression models with variable time-intervals between land-cover observations. The two functions use embedded Gibbs samplers written in C++ with the Scythe statistical library to reduce computational time.
