Showing 36 of total 36 results (show query)
stan-dev
rstan:R Interface to Stan
User-facing R functions are provided to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the 'StanHeaders' package. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough Bayesian inference via 'variational' approximation, and (optionally penalized) maximum likelihood estimation via optimization. In all three cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives.
Maintained by Ben Goodrich. Last updated 2 days ago.
bayesian-data-analysisbayesian-inferencebayesian-statisticsmcmcstancpp
1.1k stars 18.86 score 14k scripts 281 dependentsstan-dev
bayesplot:Plotting for Bayesian Models
Plotting functions for posterior analysis, MCMC diagnostics, prior and posterior predictive checks, and other visualizations to support the applied Bayesian workflow advocated in Gabry, Simpson, Vehtari, Betancourt, and Gelman (2019) <doi:10.1111/rssa.12378>. The package is designed not only to provide convenient functionality for users, but also a common set of functions that can be easily used by developers working on a variety of R packages for Bayesian modeling, particularly (but not exclusively) packages interfacing with 'Stan'.
Maintained by Jonah Gabry. Last updated 2 months ago.
bayesianggplot2mcmcpandocstanstatistical-graphicsvisualization
436 stars 16.69 score 6.5k scripts 98 dependentsstan-dev
posterior:Tools for Working with Posterior Distributions
Provides useful tools for both users and developers of packages for fitting Bayesian models or working with output from Bayesian models. The primary goals of the package are to: (a) Efficiently convert between many different useful formats of draws (samples) from posterior or prior distributions. (b) Provide consistent methods for operations commonly performed on draws, for example, subsetting, binding, or mutating draws. (c) Provide various summaries of draws in convenient formats. (d) Provide lightweight implementations of state of the art posterior inference diagnostics. References: Vehtari et al. (2021) <doi:10.1214/20-BA1221>.
Maintained by Paul-Christian Bürkner. Last updated 2 days ago.
168 stars 16.21 score 3.3k scripts 346 dependentsstan-dev
StanHeaders:C++ Header Files for Stan
The C++ header files of the Stan project are provided by this package, but it contains little R code or documentation. The main reference is the vignette. There is a shared object containing part of the 'CVODES' library, but its functionality is not accessible from R. 'StanHeaders' is primarily useful for developers who want to utilize the 'LinkingTo' directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies. The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or 'variational' methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, 'templated' statistical and linear algebra functions that can handle the automatically 'differentiable' scalar types (and doubles, 'ints', etc.), and a parser for the Stan language. The 'rstan' package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models.
Maintained by Ben Goodrich. Last updated 2 days ago.
bayesian-data-analysisbayesian-inferencebayesian-statisticsmcmcstan
1.1k stars 15.68 score 291 scripts 346 dependentsstan-dev
shinystan:Interactive Visual and Numerical Diagnostics and Posterior Analysis for Bayesian Models
A graphical user interface for interactive Markov chain Monte Carlo (MCMC) diagnostics and plots and tables helpful for analyzing a posterior sample. The interface is powered by the 'Shiny' web application framework from 'RStudio' and works with the output of MCMC programs written in any programming language (and has extended functionality for 'Stan' models fit using the 'rstan' and 'rstanarm' packages).
Maintained by Jonah Gabry. Last updated 3 years ago.
bayesianbayesian-data-analysisbayesian-inferencebayesian-methodsbayesian-statisticsmcmcshiny-appsstanstatistical-graphics
200 stars 13.13 score 1.6k scripts 15 dependents
nimble-dev
nimble:MCMC, Particle Filtering, and Programmable Hierarchical Modeling
A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, Laplace Approximation, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at <https://r-nimble.org>.
Maintained by Christopher Paciorek. Last updated 18 days ago.
bayesian-inferencebayesian-methodshierarchical-modelsmcmcprobabilistic-programmingopenblascpp
169 stars 12.97 score 2.6k scripts 19 dependentsstan-dev
cmdstanr:R Interface to 'CmdStan'
A lightweight interface to 'Stan' <https://mc-stan.org>. The 'CmdStanR' interface is an alternative to 'RStan' that calls the command line interface for compilation and running algorithms instead of interfacing with C++ via 'Rcpp'. This has many benefits including always being compatible with the latest version of Stan, fewer installation errors, fewer unexpected crashes in RStudio, and a more permissive license.
Maintained by Andrew Johnson. Last updated 13 hours ago.
bayesbayesianmarkov-chain-monte-carlomaximum-likelihoodmcmcstanvariational-inference
145 stars 12.65 score 5.2k scripts 9 dependents
xfim
ggmcmc:Tools for Analyzing MCMC Simulations from Bayesian Inference
Tools for assessing and diagnosing convergence of Markov Chain Monte Carlo simulations, as well as for graphically display results from full MCMC analysis. The package also facilitates the graphical interpretation of models by providing flexible functions to plot the results against observed variables, and functions to work with hierarchical/multilevel batches of parameters (Fernández-i-Marín, 2016 <doi:10.18637/jss.v070.i09>).
Maintained by Xavier Fernández i Marín. Last updated 2 years ago.
bayesian-data-analysisggplot2graphicaljagsmcmcstan
111 stars 11.94 score 1.6k scripts 8 dependents
merliseclyde
BAS:Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling
Package for Bayesian Variable Selection and Model Averaging in linear models and generalized linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are from Zellner's g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the mixture of g-priors from Liang et al (2008) <DOI:10.1198/016214507000001337> for linear models or mixtures of g-priors from Li and Clyde (2019) <DOI:10.1080/01621459.2018.1469992> in generalized linear models. Other model selection criteria include AIC, BIC and Empirical Bayes estimates of g. Sampling probabilities may be updated based on the sampled models using sampling w/out replacement or an efficient MCMC algorithm which samples models using a tree structure of the model space as an efficient hash table. See Clyde, Ghosh and Littman (2010) <DOI:10.1198/jcgs.2010.09049> for details on the sampling algorithms. Uniform priors over all models or beta-binomial prior distributions on model size are allowed, and for large p truncated priors on the model space may be used to enforce sampling models that are full rank. The user may force variables to always be included in addition to imposing constraints that higher order interactions are included only if their parents are included in the model. This material is based upon work supported by the National Science Foundation under Division of Mathematical Sciences grant 1106891. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Maintained by Merlise Clyde. Last updated 4 months ago.
bayesianbayesian-inferencegeneralized-linear-modelslinear-regressionlogistic-regressionmcmcmodel-selectionpoisson-regressionpredictive-modelingregressionvariable-selectionfortranopenblas
44 stars 10.63 score 420 scripts 3 dependents
florianhartig
BayesianTools:General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics
General-purpose MCMC and SMC samplers, as well as plots and diagnostic functions for Bayesian statistics, with a particular focus on calibrating complex system models. Implemented samplers include various Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential Monte Carlo (SMC) particle filter.
Maintained by Florian Hartig. Last updated 1 years ago.
bayesecological-modelsmcmcoptimizationsmcsystems-biologycpp
124 stars 10.18 score 580 scripts 5 dependents
dvats
mcmcse:Monte Carlo Standard Errors for MCMC
Provides tools for computing Monte Carlo standard errors (MCSE) in Markov chain Monte Carlo (MCMC) settings. MCSE computation for expectation and quantile estimators is supported as well as multivariate estimations. The package also provides functions for computing effective sample size and for plotting Monte Carlo estimates versus sample size.
Maintained by Dootika Vats. Last updated 2 months ago.
effective-sample-sizemcmcoutput-aopenblascpp
12 stars 8.77 score 314 scripts 17 dependents
poissonconsulting
mcmcr:Manipulate MCMC Samples
Functions and classes to store, manipulate and summarise Monte Carlo Markov Chain (MCMC) samples. For more information see Brooks et al. (2011) <isbn:978-1-4200-7941-8>.
Maintained by Joe Thorley. Last updated 2 months ago.
17 stars 7.66 score 111 scripts 10 dependents
dm13450
dirichletprocess:Build Dirichlet Process Objects for Bayesian Modelling
Perform nonparametric Bayesian analysis using Dirichlet processes without the need to program the inference algorithms. Utilise included pre-built models or specify custom models and allow the 'dirichletprocess' package to handle the Markov chain Monte Carlo sampling. Our Dirichlet process objects can act as building blocks for a variety of statistical models including and not limited to: density estimation, clustering and prior distributions in hierarchical models. See Teh, Y. W. (2011) <https://www.stats.ox.ac.uk/~teh/research/npbayes/Teh2010a.pdf>, among many other sources.
Maintained by Dean Markwick. Last updated 2 years ago.
bayesianbayesian-inferencebayesian-statisticsdirichlet-processmcmc
58 stars 7.40 score 72 scripts 2 dependentspoissonconsulting
term:Create, Manipulate and Query Parameter Terms
Creates, manipulates, queries and repairs vectors of parameter terms. Parameter terms are the labels used to reference values in vectors, matrices and arrays. They represent the names in coefficient tables and the column names in 'mcmc' and 'mcmc.list' objects.
Maintained by Joe Thorley. Last updated 2 months ago.
10 stars 7.15 score 15 scripts 13 dependents
asael697
bayesforecast:Bayesian Time Series Modeling with Stan
Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
Maintained by Asael Alonzo Matamoros. Last updated 1 years ago.
bayesian-inferenceforecasting-modelsmcmcstantime-series-analysiscpp
45 stars 6.92 score 62 scripts
franzmohr
bvartools:Bayesian Inference of Vector Autoregressive and Error Correction Models
Assists in the set-up of algorithms for Bayesian inference of vector autoregressive (VAR) and error correction (VEC) models. Functions for posterior simulation, forecasting, impulse response analysis and forecast error variance decomposition are largely based on the introductory texts of Chan, Koop, Poirier and Tobias (2019, ISBN: 9781108437493), Koop and Korobilis (2010) <doi:10.1561/0800000013> and Luetkepohl (2006, ISBN: 9783540262398).
Maintained by Franz X. Mohr. Last updated 1 years ago.
bayesianbayesian-inferencebayesian-varbvarbvecmgibbs-samplingmcmcvector-autoregressionvector-error-correction-modelopenblascpp
31 stars 6.80 score 34 scripts 1 dependents
uscbiostats
fmcmc:A friendly MCMC framework
Provides a friendly (flexible) Markov Chain Monte Carlo (MCMC) framework for implementing Metropolis-Hastings algorithm in a modular way allowing users to specify automatic convergence checker, personalized transition kernels, and out-of-the-box multiple MCMC chains using parallel computing. Most of the methods implemented in this package can be found in Brooks et al. (2011, ISBN 9781420079425). Among the methods included, we have: Haario (2001) <doi:10.1007/s11222-011-9269-5> Adaptive Metropolis, Vihola (2012) <doi:10.1007/s11222-011-9269-5> Robust Adaptive Metropolis, and Thawornwattana et al. (2018) <doi:10.1214/17-BA1084> Mirror transition kernels.
Maintained by George Vega Yon. Last updated 2 years ago.
adaptivebayesian-inferencemarkov-chain-monte-carlomcmcmetropolis-hastingsparallel-computing
16 stars 6.79 score 86 scripts 1 dependents
smartdata-analysis-and-statistics
SimTOST:Sample Size Estimation for Bio-Equivalence Trials Through Simulation
Sample size estimation for bio-equivalence trials is supported through a simulation-based approach that extends the Two One-Sided Tests (TOST) procedure. The methodology provides flexibility in hypothesis testing, accommodates multiple treatment comparisons, and accounts for correlated endpoints. Users can model complex trial scenarios, including parallel and crossover designs, intra-subject variability, and different equivalence margins. Monte Carlo simulations enable accurate estimation of power and type I error rates, ensuring well-calibrated study designs. The statistical framework builds on established methods for equivalence testing and multiple hypothesis testing in bio-equivalence studies, as described in Schuirmann (1987) <doi:10.1007/BF01068419>, Mielke et al. (2018) <doi:10.1080/19466315.2017.1371071>, Shieh (2022) <doi:10.1371/journal.pone.0269128>, and Sozu et al. (2015) <doi:10.1007/978-3-319-22005-5>. Comprehensive documentation and vignettes guide users through implementation and interpretation of results.
Maintained by Thomas Debray. Last updated 1 months ago.
mcmcmulti-armmultiple-comparisonssample-size-calculationsample-size-estimationtrial-simulationopenblascpp
2 stars 6.47 score 7 scripts
helske
walker:Bayesian Generalized Linear Models with Time-Varying Coefficients
Efficient Bayesian generalized linear models with time-varying coefficients as in Helske (2022, <doi:10.1016/j.softx.2022.101016>). Gaussian, Poisson, and binomial observations are supported. The Markov chain Monte Carlo (MCMC) computations are done using Hamiltonian Monte Carlo provided by Stan, using a state space representation of the model in order to marginalise over the coefficients for efficient sampling. For non-Gaussian models, the package uses the importance sampling type estimators based on approximate marginal MCMC as in Vihola, Helske, Franks (2020, <doi:10.1111/sjos.12492>).
Maintained by Jouni Helske. Last updated 7 months ago.
bayesiangeneralized-linear-modelsmcmcstantime-seriesopenblascpp
44 stars 6.42 score 15 scripts
mkln
meshed:Bayesian Regression with Meshed Gaussian Processes
Fits Bayesian regression models based on latent Meshed Gaussian Processes (MGP) as described in Peruzzi, Banerjee, Finley (2020) <doi:10.1080/01621459.2020.1833889>, Peruzzi, Banerjee, Dunson, and Finley (2021) <arXiv:2101.03579>, Peruzzi and Dunson (2024) <arXiv:2201.10080>. Funded by ERC grant 856506 and NIH grant R01ES028804.
Maintained by Michele Peruzzi. Last updated 8 months ago.
bayesianmcmcmultivariateregressionspatialspatiotemporalopenblascppopenmp
13 stars 6.11 score 49 scripts
oeysan
bfw:Bayesian Framework for Computational Modeling
Derived from the work of Kruschke (2015, <ISBN:9780124058880>), the present package aims to provide a framework for conducting Bayesian analysis using Markov chain Monte Carlo (MCMC) sampling utilizing the Just Another Gibbs Sampler ('JAGS', Plummer, 2003, <https://mcmc-jags.sourceforge.io>). The initial version includes several modules for conducting Bayesian equivalents of chi-squared tests, analysis of variance (ANOVA), multiple (hierarchical) regression, softmax regression, and for fitting data (e.g., structural equation modeling).
Maintained by Øystein Olav Skaar. Last updated 3 years ago.
bayesian-data-analysisbayesian-statisticsjagsmcmcpsychological-sciencecpp
10 stars 5.89 score 31 scripts
poissonconsulting
mcmcderive:Derive MCMC Parameters
Generates derived parameter(s) from Monte Carlo Markov Chain (MCMC) samples using R code. This allows Bayesian models to be fitted without the inclusion of derived parameters which add unnecessary clutter and slow model fitting. For more information on MCMC samples see Brooks et al. (2011) <isbn:978-1-4200-7941-8>.
Maintained by Joe Thorley. Last updated 2 months ago.
5.53 score 5 scripts 8 dependents
longhaisk
HTLR:Bayesian Logistic Regression with Heavy-Tailed Priors
Efficient Bayesian multinomial logistic regression based on heavy-tailed (hyper-LASSO, non-convex) priors. The posterior of coefficients and hyper-parameters is sampled with restricted Gibbs sampling for leveraging the high-dimensionality and Hamiltonian Monte Carlo for handling the high-correlation among coefficients. A detailed description of the method: Li and Yao (2018), Journal of Statistical Computation and Simulation, 88:14, 2827-2851, <arXiv:1405.3319>.
Maintained by Longhai Li. Last updated 5 months ago.
bayesianclassificationhigh-dimensional-datamachine-learningmcmcopenblascppopenmp
10 stars 5.18 score 7 scripts
eppicenter
moire:Multiplicity of Infection and Allele Frequency Recovery from Noisy Polyallelic Genetics Data
A Markov Chain Monte Carlo (MCMC) based approach to Bayesian estimation of individual level multiplicity of infection, within host relatedness, and population allele frequencies from polyallelic genetic data.
Maintained by Maxwell Murphy. Last updated 5 months ago.
7 stars 5.14 score 22 scripts
deploid-dev
DEploid:Deconvolute Mixed Genomes with Unknown Proportions
Traditional phasing programs are limited to diploid organisms. Our method modifies Li and Stephens algorithm with Markov chain Monte Carlo (MCMC) approaches, and builds a generic framework that allows haplotype searches in a multiple infection setting. This package is primarily developed as part of the Pf3k project, which is a global collaboration using the latest sequencing technologies to provide a high-resolution view of natural variation in the malaria parasite Plasmodium falciparum. Parasite DNA are extracted from patient blood sample, which often contains more than one parasite strain, with unknown proportions. This package is used for deconvoluting mixed haplotypes, and reporting the mixture proportions from each sample.
Maintained by Joe Zhu. Last updated 2 months ago.
deconvoluting-mixed-genomeshmmmalariamcmcparasitesphasingunknown-proportionszlibcpp
1 stars 4.99 score 39 scripts
bearloga
MLPUGS:Multi-Label Prediction Using Gibbs Sampling (and Classifier Chains)
An implementation of classifier chains (CC's) for multi-label prediction. Users can employ an external package (e.g. 'randomForest', 'C50'), or supply their own. The package can train a single set of CC's or train an ensemble of CC's -- in parallel if running in a multi-core environment. New observations are classified using a Gibbs sampler since each unobserved label is conditioned on the others. The package includes methods for evaluating the predictions for accuracy and aggregating across iterations and models to produce binary or probabilistic classifications.
Maintained by Mikhail Popov. Last updated 5 years ago.
classificationmachine-learningmcmcmulti-label-classificationsupervised-learning
11 stars 4.74 score 6 scripts
bisaloo
mcmcensemble:Ensemble Sampler for Affine-Invariant MCMC
Provides ensemble samplers for affine-invariant Monte Carlo Markov Chain, which allow a faster convergence for badly scaled estimation problems. Two samplers are proposed: the 'differential.evolution' sampler from ter Braak and Vrugt (2008) <doi:10.1007/s11222-008-9104-9> and the 'stretch' sampler from Goodman and Weare (2010) <doi:10.2140/camcos.2010.5.65>.
Maintained by Hugo Gruson. Last updated 1 years ago.
2 stars 4.60 score 8 scripts
xsswang
remiod:Reference-Based Multiple Imputation for Ordinal/Binary Response
Reference-based multiple imputation of ordinal and binary responses under Bayesian framework, as described in Wang and Liu (2022) <arXiv:2203.02771>. Methods for missing-not-at-random include Jump-to-Reference (J2R), Copy Reference (CR), and Delta Adjustment which can generate tipping point analysis.
Maintained by Tony Wang. Last updated 2 years ago.
bayesiancontrol-basedcopy-referencedelta-adjustmentgeneralized-linear-modelsglmjagsjump-to-referencemcmcmissing-at-randommissing-datamissing-not-at-randommultiple-imputationnon-ignorableordinal-regressionpattern-mixture-modelreference-basedstatisticscpp
4.30 score 3 scripts
ardiad
bayesGARCH:Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations
Provides the bayesGARCH() function which performs the Bayesian estimation of the GARCH(1,1) model with Student's t innovations as described in Ardia (2008) <doi:10.1007/978-3-540-78657-3>.
Maintained by David Ardia. Last updated 4 years ago.
bayesiangarchmcmcrisk-modelsstudent
14 stars 4.02 score 15 scripts
fweber144
shinybrms:Graphical User Interface ('shiny' App) for 'brms'
A graphical user interface (GUI) for fitting Bayesian regression models using the package 'brms' which in turn relies on 'Stan' (<https://mc-stan.org/>). The 'shinybrms' GUI is a 'shiny' app.
Maintained by Frank Weber. Last updated 12 months ago.
bayesbayesianbayesian-data-analysisbayesian-inferencebayesian-statisticsbrmscmdstanrguimcmcrstanshinyshiny-appstanstatistical-analysisstatistical-inferencestatistical-modelsstatistics
10 stars 3.70 score 3 scripts
mcol
hsstan:Hierarchical Shrinkage Stan Models for Biomarker Selection
Linear and logistic regression models penalized with hierarchical shrinkage priors for selection of biomarkers (or more general variable selection), which can be fitted using Stan (Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>). It implements the horseshoe and regularized horseshoe priors (Piironen and Vehtari (2017) <doi:10.1214/17-EJS1337SI>), as well as the projection predictive selection approach to recover a sparse set of predictive biomarkers (Piironen, Paasiniemi and Vehtari (2020) <doi:10.1214/20-EJS1711>).
Maintained by Marco Colombo. Last updated 1 years ago.
bayesianfeature-selectionmcmccpp
7 stars 3.66 score 13 scriptsardiad
AdMit:Adaptive Mixture of Student-t Distributions
Provides functions to perform the fitting of an adaptive mixture of Student-t distributions to a target density through its kernel function as described in Ardia et al. (2009) <doi:10.18637/jss.v029.i03>. The mixture approximation can then be used as the importance density in importance sampling or as the candidate density in the Metropolis-Hastings algorithm to obtain quantities of interest for the target density itself.
Maintained by David Ardia. Last updated 3 years ago.
adaptivedistributionfittingmcmcmixturemixture-model
2 stars 3.00 score 9 scripts
pcbrom
bgumbel:Bimodal Gumbel Distribution
Bimodal Gumbel distribution. General functions for performing extreme value analysis.
Maintained by Pedro C. Brom. Last updated 4 years ago.
bgbromciragumbel-distributionmcmcpereiraroberto-vila
2 stars 3.00 score 1 scripts
jamesuanhoro
ssrhom:Hierarchical ordinal models for analyzing single subject designs
Hierarchical ordinal models for analyzing single subject designs using Bayesian models fit with Stan.
Maintained by James Uanhoro. Last updated 6 months ago.
bayesian-statisticshierarchical-modelsmcmcsingle-case-designstancpp
2.30 score 3 scripts