Search Results for "mcmc-jags"
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Showing 43 open source projects for "mcmc-jags"
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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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blavaan
An R package for Bayesian structural equation modeling
blavaan is a free, open-source R package for Bayesian latent variable analysis. It relies on JAGS and Stan to estimate models via MCMC. The blavaan functions and syntax are similar to lavaan. The development version of blavaan (containing updates not yet on CRAN) can be installed via the command provided in the documentation. Compilation is required; this may be a problem for users who currently rely on a binary version of blavaan from CRAN. -
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jags-wiener
Wiener functions in JAGS
The JAGS Wiener module is an extension for JAGS, which provides wiener process distribution functions, mainly the Wiener first passage time density. It allows to include stochastic nodes with the first hitting time distribution of a diffusion process. Ubuntu users can also checkout our PPA: https://launchpad.net/~cidlab/+archive/jwm -
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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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PairPlots.jl
Beautiful and flexible vizualizations of high dimensional data
...This package produces pair plots, otherwise known as corner plots or scatter plot matrices: grids of 1D and 2D histograms that allow you to visualize high-dimensional data. Pair plots are an excellent way to visualize the results of MCMC simulations, but are also a useful way to visualize correlations in general data tables. The default styles of this package roughly reproduce the output of the Python library corner.py for a single series and chainconsumer.py for multiple series. If these are not to your tastes, the package aims to be highly configurable. -
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Meridian
Meridian is an MMM framework
...The framework provides a robust foundation for constructing in-house MMM pipelines capable of handling both national and geo-level data, with built-in support for calibration using experimental data or prior knowledge. Meridian uses the No-U-Turn Sampler (NUTS) for Markov Chain Monte Carlo (MCMC) sampling to produce statistically rigorous results, and it includes GPU acceleration to significantly reduce computation time. -
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DSGE.jl
Solve and estimate Dynamic Stochastic General Equilibrium models
DSGE.jl is a Julia package developed by the Federal Reserve Bank of New York for estimating and analyzing dynamic stochastic general equilibrium (DSGE) models. It provides tools for Bayesian estimation, filtering, forecasting, and model comparison, supporting both academic research and policy applications. DSGE.jl includes pre-configured models used by central banks and offers extensibility for custom macroeconomic modeling. -
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Bayesian Statistics
This repository holds slides and code for a full Bayesian statistics
This repository holds slides and code for a full Bayesian statistics graduate course. Bayesian statistics is an approach to inferential statistics based on Bayes' theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used... -
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PyMC3
Probabilistic programming in Python
PyMC3 allows you to write down models using an intuitive syntax to describe a data generating process. Fit your model using gradient-based MCMC algorithms like NUTS, using ADVI for fast approximate inference — including minibatch-ADVI for scaling to large datasets, or using Gaussian processes to build Bayesian nonparametric models. PyMC3 includes a comprehensive set of pre-defined statistical distributions that can be used as model building blocks. Sometimes an unknown parameter or variable in a model is not a scalar value or a fixed-length vector, but a function. ... -
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RASP
RASP (Reconstruct Ancestral State in Phylogenies)
RASP (Reconstruct Ancestral State in Phylogenies) is a tool for inferring ancestral state using S-DIVA (Statistical dispersal-vicariance analysis), Lagrange (DEC), Bayes-Lagrange (S-DEC), BayArea, BBM (Bayesian Binary MCMC) method, Bayestraits and BioGeoBEARS packages. All documentation and source code for RASP is freely available at: http://mnh.scu.edu.cn/soft/blog/RASP and http://github.com/sculab/RASP -
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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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pexm - JAGS module
JAGS module for the piecewise exponential distribution
This new module was built for users interested in a programming language similar to BUGS to fit a Bayesian model based on the piecewise exponential distribution. The module is an extension of the open-source program JAGS. The PE distribution is widely used in the fields of survival analysis and reliability. Currently, it can only be implemented in JAGS through methods to indirectly specify the likelihood based on the Poisson or Bernoulli probabilities. Our module provides a more straightforward implementation with faster results. The "tar.gz" file provided for download here (installation for both Windows and Unix systems) is related to an R package called "pexm". ... -
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analyseMCMC
Analyse output from MCMC codes like SPINspiral and lalinference_mcmc
analyseMCMC post-processes, analyses and plots output from the LIGO/Virgo gravitational-wave inspiral parameter-estimation codes SPINspiral and lalinference_mcmc. -
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jags-zibg
Zero-inflated bivariate geometric distributions in JAGS
The JAGS ZIBGeometric module extends JAGS by providing a zero-inflated bivariate geometric distribution class. Currently, it only supports probability mass computations in posterior parameter distribution estimation. -
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BayesFM-MCMC-V1.0
A package for fine mapping causitive variants
A new Bayesian MCMC method for fine mapping causitive variants within GWAS identified region for complex traits. The small region is typically across 1Mb. The feature of the package is that (1) it identify multiple causitive variants (credible set) simultaneousely; (2) it generates credible set containing highly linked variants rather in addition to a lead variants for each signal. -
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HBV interventions model
ODE HBV model and MCMC for fitting HBsAg, HBcAg and HBeAg data.
This code implements the MCMC and ordinary differential equation (ODE) model described in [1]. The core MCMC and ODE code is implemented in C/C++, and is wrapped with an R front end. This is not an R-package (although there are plans to extend the code and eventually make it into an R-package). Please read the PDF file supplied for further instructions on how to use this code -
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BNMCMC
Infer Bayesian Network Structure from data using MCMC methods
Infer Bayesian Network Structure from data using MCMC methods -
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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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GeoJAGS
JAGS module with covariance functions and the CAR model.
A module for JAGS containing several covariance functions for point-referenced data analysis and a function that creates the precision matrix for the proper CAR model for areal data analysis. -
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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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runjags
The 'runjags' R package and standalone JAGS extension module
This package provides high-level interface utilities for MCMC models via Just Another Gibbs Sampler (JAGS), facilitating the use of parallel (or distributed) processors for multiple chains, automated control of convergence and sample length diagnostics, and evaluation of the performance of a model using drop-k validation or against simulated data. Template model specifications can be generated using a standard lme4-style formula interface to assist users less familiar with the BUGS syntax. ... -
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Phylobuntu is a software package that contains 37 software tools related to phylogenetic profile trees. Phylobuntu aims to provide a complete workstation for phylogenetic analysis
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BPandP
Bayesian MCMC algorithms for the analysis of phylogeographic DNA data
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SPR MCMC
A windows executable for running the Bayesian inference on SPR data.
A windows executable (C# application) to run the Bayesian inference to estimate the affinity kinetics as well as the active concentration on the SPR (surface plasmon resonance) data. -
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bayescount
The bayescount R package
A set of functions to allow analysis of count data (such as faecal egg count data) using Bayesian MCMC methods. Returns information on the possible values for mean count, coefficient of variation and zero inflation (true prevalence) present in the data. A complete faecal egg count reduction test (FECRT) model is implemented, which returns inference on the true efficacy of the drug from the pre- and post-treatment data provided, using non-parametric bootstrapping as well as using Bayesian MCMC. ...
