R-universe search: topic:bayesian-inference

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 23 hours ago.

bayesian-data-analysis bayesian-inference bayesian-statistics mcmc stan cpp

1.1k stars 18.86 score 14k scripts 281 dependents

stan-dev

loo:Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models

Efficient approximate leave-one-out cross-validation (LOO) for Bayesian models fit using Markov chain Monte Carlo, as described in Vehtari, Gelman, and Gabry (2017) <doi:10.1007/s11222-016-9696-4>. The approximation uses Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. As a byproduct of the calculations, we also obtain approximate standard errors for estimated predictive errors and for the comparison of predictive errors between models. The package also provides methods for using stacking and other model weighting techniques to average Bayesian predictive distributions.

Maintained by Jonah Gabry. Last updated 16 days ago.

bayes bayesian bayesian-data-analysis bayesian-inference bayesian-methods bayesian-statistics cross-validation information-criterion model-comparison stan

152 stars 17.30 score 2.6k scripts 297 dependents

paul-buerkner

brms:Bayesian Regression Models using 'Stan'

Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include both theory-driven and data-driven non-linear terms, auto-correlation structures, censoring and truncation, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their prior knowledge. Models can easily be evaluated and compared using several methods assessing posterior or prior predictions. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Bürkner (2018) <doi:10.32614/RJ-2018-017>; Bürkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

Maintained by Paul-Christian Bürkner. Last updated 3 days ago.

bayesian-inference brms multilevel-models stan statistical-models

1.3k stars 16.64 score 13k scripts 35 dependents

stan-dev

rstanarm:Bayesian Applied Regression Modeling via Stan

Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.

Maintained by Ben Goodrich. Last updated 10 days ago.

bayesian bayesian-data-analysis bayesian-inference bayesian-methods bayesian-statistics multilevel-models rstan rstanarm stan statistical-modeling cpp

393 stars 15.70 score 5.0k scripts 13 dependents

stan-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 23 hours ago.

bayesian-data-analysis bayesian-inference bayesian-statistics mcmc stan

1.1k stars 15.68 score 291 scripts 346 dependents

stan-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.

bayesian bayesian-data-analysis bayesian-inference bayesian-methods bayesian-statistics mcmc shiny-apps stan statistical-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 17 days ago.

bayesian-inference bayesian-methods hierarchical-models mcmc probabilistic-programming openblas cpp

169 stars 12.97 score 2.6k scripts 19 dependents

indrajeetpatil

statsExpressions:Tidy Dataframes and Expressions with Statistical Details

Utilities for producing dataframes with rich details for the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation analyses, contingency table analyses, and meta-analyses. The functions are pipe-friendly and provide a consistent syntax to work with tidy data. These dataframes additionally contain expressions with statistical details, and can be used in graphing packages. This package also forms the statistical processing backend for 'ggstatsplot'. References: Patil (2021) <doi:10.21105/joss.03236>.

Maintained by Indrajeet Patil. Last updated 1 months ago.

bayesian-inference bayesian-statistics contingency-table correlation effectsize meta-analysis parametric robust robust-statistics statistical-details statistical-tests tidy

312 stars 10.92 score 146 scripts 2 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.

bayesian bayesian-inference generalized-linear-models linear-regression logistic-regression mcmc model-selection poisson-regression predictive-modeling regression variable-selection fortran openblas

44 stars 10.63 score 420 scripts 3 dependents

richardli

SUMMER:Small-Area-Estimation Unit/Area Models and Methods for Estimation in R

Provides methods for spatial and spatio-temporal smoothing of demographic and health indicators using survey data, with particular focus on estimating and projecting under-five mortality rates, described in Mercer et al. (2015) <doi:10.1214/15-AOAS872>, Li et al. (2019) <doi:10.1371/journal.pone.0210645>, Wu et al. (DHS Spatial Analysis Reports No. 21, 2021), and Li et al. (2023) <doi:10.48550/arXiv.2007.05117>.

Maintained by Zehang R Li. Last updated 3 months ago.

bayesian-inference small-area-estimation space-time

23 stars 10.28 score 134 scripts 2 dependents

stan-dev

projpred:Projection Predictive Feature Selection

Performs projection predictive feature selection for generalized linear models (Piironen, Paasiniemi, and Vehtari, 2020, <doi:10.1214/20-EJS1711>) with or without multilevel or additive terms (Catalina, Bürkner, and Vehtari, 2022, <https://proceedings.mlr.press/v151/catalina22a.html>), for some ordinal and nominal regression models (Weber, Glass, and Vehtari, 2023, <arXiv:2301.01660>), and for many other regression models (using the latent projection by Catalina, Bürkner, and Vehtari, 2021, <arXiv:2109.04702>, which can also be applied to most of the former models). The package is compatible with the 'rstanarm' and 'brms' packages, but other reference models can also be used. See the vignettes and the documentation for more information and examples.

Maintained by Frank Weber. Last updated 1 days ago.

bayes bayesian bayesian-inference rstanarm stan statistics variable-selection openblas cpp

112 stars 10.12 score 241 scripts

avehtari

aaltobda:Functionality and Data for the Aalto Course on Bayesian Data Analysis

Functionality and Data for the Aalto University Course on Bayesian Data Analysis.

Maintained by Aki Vehtari. Last updated 4 months ago.

bayes bayesian bayesian-data-analysis bayesian-inference bayesian-methods bayesian-workflow

2.2k stars 8.93 score 159 scripts

connordonegan

geostan:Bayesian Spatial Analysis

For spatial data analysis; provides exploratory spatial analysis tools, spatial regression, spatial econometric, and disease mapping models, model diagnostics, and special methods for inference with small area survey data (e.g., the America Community Survey (ACS)) and censored population health monitoring data. Models are pre-specified using the Stan programming language, a platform for Bayesian inference using Markov chain Monte Carlo (MCMC). References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Donegan (2021) <doi:10.31219/osf.io/3ey65>; Donegan (2022) <doi:10.21105/joss.04716>; Donegan, Chun and Hughes (2020) <doi:10.1016/j.spasta.2020.100450>; Donegan, Chun and Griffith (2021) <doi:10.3390/ijerph18136856>; Morris et al. (2019) <doi:10.1016/j.sste.2019.100301>.

Maintained by Connor Donegan. Last updated 3 months ago.

bayesian bayesian-inference bayesian-statistics epidemiology modeling public-health rspatial spatial stan cpp

80 stars 8.80 score 46 scripts

ropensci

babette:Control 'BEAST2'

'BEAST2' (<https://www.beast2.org>) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. 'BEAST2' is commonly accompanied by 'BEAUti 2', 'Tracer' and 'DensiTree'. 'babette' provides for an alternative workflow of using all these tools separately. This allows doing complex Bayesian phylogenetics easily and reproducibly from 'R'.

Maintained by Richèl J.C. Bilderbeek. Last updated 1 days ago.

bayesian-inference beast2 phylogenetics openjdk

45 stars 8.55 score 53 scripts 1 dependents

florale

multilevelcoda:Estimate Bayesian Multilevel Models for Compositional Data

Implement Bayesian Multilevel Modelling for compositional data in a multilevel framework. Compute multilevel compositional data and Isometric log ratio (ILR) at between and within-person levels, fit Bayesian multilevel models for compositional predictors and outcomes, and run post-hoc analyses such as isotemporal substitution models. References: Le, Stanford, Dumuid, and Wiley (2024) <doi:10.48550/arXiv.2405.03985>, Le, Dumuid, Stanford, and Wiley (2024) <doi:10.48550/arXiv.2411.12407>.

Maintained by Flora Le. Last updated 3 days ago.

bayesian-inference compositional-data-analysis multilevel-models multilevelcoda

15 stars 8.34 score 118 scripts

open-aims

bayesnec:A Bayesian No-Effect- Concentration (NEC) Algorithm

Implementation of No-Effect-Concentration estimation that uses 'brms' (see Burkner (2017)<doi:10.18637/jss.v080.i01>; Burkner (2018)<doi:10.32614/RJ-2018-017>; Carpenter 'et al.' (2017)<doi:10.18637/jss.v076.i01> to fit concentration(dose)-response data using Bayesian methods for the purpose of estimating 'ECx' values, but more particularly 'NEC' (see Fox (2010)<doi:10.1016/j.ecoenv.2009.09.012>), 'NSEC' (see Fisher and Fox (2023)<doi:10.1002/etc.5610>), and 'N(S)EC (see Fisher et al. 2023<doi:10.1002/ieam.4809>). A full description of this package can be found in Fisher 'et al.' (2024)<doi:10.18637/jss.v110.i05>. This package expands and supersedes an original version implemented in 'R2jags' (see Su and Yajima (2020)<https://CRAN.R-project.org/package=R2jags>; Fisher et al. (2020)<doi:10.5281/ZENODO.3966864>).

Maintained by Rebecca Fisher. Last updated 7 months ago.

bayesian-inference concentration-response ecotoxicology no-effect-concentration non-linear-decay threshold-derivation toxicology

13 stars 8.15 score 360 scripts

ropensci

dynamite:Bayesian Modeling and Causal Inference for Multivariate Longitudinal Data

Easy-to-use and efficient interface for Bayesian inference of complex panel (time series) data using dynamic multivariate panel models by Helske and Tikka (2024) <doi:10.1016/j.alcr.2024.100617>. The package supports joint modeling of multiple measurements per individual, time-varying and time-invariant effects, and a wide range of discrete and continuous distributions. Estimation of these dynamic multivariate panel models is carried out via 'Stan'. For an in-depth tutorial of the package, see (Tikka and Helske, 2024) <doi:10.48550/arXiv.2302.01607>.

Maintained by Santtu Tikka. Last updated 3 days ago.

bayesian-inference panel-data stan statistical-models

29 stars 7.90 score 20 scripts

statswithr

statsr:Companion Software for the Coursera Statistics with R Specialization

Data and functions to support Bayesian and frequentist inference and decision making for the Coursera Specialization "Statistics with R". See <https://github.com/StatsWithR/statsr> for more information.

Maintained by Merlise Clyde. Last updated 4 years ago.

bayesian-inference coursera statistics

71 stars 7.82 score 880 scripts

danheck

metaBMA:Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis

Computes the posterior model probabilities for standard meta-analysis models (null model vs. alternative model assuming either fixed- or random-effects, respectively). These posterior probabilities are used to estimate the overall mean effect size as the weighted average of the mean effect size estimates of the random- and fixed-effect model as proposed by Gronau, Van Erp, Heck, Cesario, Jonas, & Wagenmakers (2017, <doi:10.1080/23743603.2017.1326760>). The user can define a wide range of non-informative or informative priors for the mean effect size and the heterogeneity coefficient. Moreover, using pre-compiled Stan models, meta-analysis with continuous and discrete moderators with Jeffreys-Zellner-Siow (JZS) priors can be fitted and tested. This allows to compute Bayes factors and perform Bayesian model averaging across random- and fixed-effects meta-analysis with and without moderators. For a primer on Bayesian model-averaged meta-analysis, see Gronau, Heck, Berkhout, Haaf, & Wagenmakers (2021, <doi:10.1177/25152459211031256>).

Maintained by Daniel W. Heck. Last updated 1 years ago.

bayes bayes-factor bayesian-inference evidence-synthesis meta-analysis model-averaging stan cpp

28 stars 7.75 score 54 scripts 4 dependents

bsvars

bsvars:Bayesian Estimation of Structural Vector Autoregressive Models

Provides fast and efficient procedures for Bayesian analysis of Structural Vector Autoregressions. This package estimates a wide range of models, including homo-, heteroskedastic, and non-normal specifications. Structural models can be identified by adjustable exclusion restrictions, time-varying volatility, or non-normality. They all include a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, verification of heteroskedasticity, non-normality, and hypotheses on autoregressive parameters, as well as analyses of structural shocks, volatilities, and fitted values. Beautiful plots, informative summary functions, and extensive documentation including the vignette by Woźniak (2024) <doi:10.48550/arXiv.2410.15090> complement all this. The implemented techniques align closely with those presented in Lütkepohl, Shang, Uzeda, & Woźniak (2024) <doi:10.48550/arXiv.2404.11057>, Lütkepohl & Woźniak (2020) <doi:10.1016/j.jedc.2020.103862>, and Song & Woźniak (2021) <doi:10.1093/acrefore/9780190625979.013.174>. The 'bsvars' package is aligned regarding objects, workflows, and code structure with the R package 'bsvarSIGNs' by Wang & Woźniak (2024) <doi:10.32614/CRAN.package.bsvarSIGNs>, and they constitute an integrated toolset.

Maintained by Tomasz Woźniak. Last updated 2 months ago.

bayesian-inference econometrics vector-autoregression openblas cpp openmp

46 stars 7.67 score 32 scripts 1 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.

bayesian bayesian-inference bayesian-statistics dirichlet-process mcmc

58 stars 7.40 score 72 scripts 2 dependents

anothersamwilson

ParBayesianOptimization:Parallel Bayesian Optimization of Hyperparameters

Fast, flexible framework for implementing Bayesian optimization of model hyperparameters according to the methods described in Snoek et al. <arXiv:1206.2944>. The package allows the user to run scoring function in parallel, save intermediary results, and tweak other aspects of the process to fully utilize the computing resources available to the user.

Maintained by Samuel Wilson. Last updated 2 years ago.

bayesian-inference machine-learning

108 stars 7.19 score 86 scripts

hneth

riskyr:Rendering Risk Literacy more Transparent

Risk-related information (like the prevalence of conditions, the sensitivity and specificity of diagnostic tests, or the effectiveness of interventions or treatments) can be expressed in terms of frequencies or probabilities. By providing a toolbox of corresponding metrics and representations, 'riskyr' computes, translates, and visualizes risk-related information in a variety of ways. Adopting multiple complementary perspectives provides insights into the interplay between key parameters and renders teaching and training programs on risk literacy more transparent.

Maintained by Hansjoerg Neth. Last updated 10 months ago.

2x2-matrix bayesian-inference contingency-table representation risk risk-literacy visualization

19 stars 7.18 score 80 scripts

flyaflya

causact:Fast, Easy, and Visual Bayesian Inference

Accelerate Bayesian analytics workflows in 'R' through interactive modelling, visualization, and inference. Define probabilistic graphical models using directed acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians, and programmers. This package relies on interfacing with the 'numpyro' python package.

Maintained by Adam Fleischhacker. Last updated 2 months ago.

bayesian-inference dags posterior-probability probabilistic-graphical-models probabilistic-programming

45 stars 6.97 score 52 scripts

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-inference forecasting-models mcmc stan time-series-analysis cpp

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.

bayesian bayesian-inference bayesian-var bvar bvecm gibbs-sampling mcmc vector-autoregression vector-error-correction-model openblas cpp

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.

adaptive bayesian-inference markov-chain-monte-carlo mcmc metropolis-hastings parallel-computing

16 stars 6.79 score 86 scripts 1 dependents

paulnorthrop

rust:Ratio-of-Uniforms Simulation with Transformation

Uses the generalized ratio-of-uniforms (RU) method to simulate from univariate and (low-dimensional) multivariate continuous distributions. The user specifies the log-density, up to an additive constant. The RU algorithm is applied after relocation of mode of the density to zero, and the user can choose a tuning parameter r. For details see Wakefield, Gelfand and Smith (1991) <DOI:10.1007/BF01889987>, Efficient generation of random variates via the ratio-of-uniforms method, Statistics and Computing (1991) 1, 129-133. A Box-Cox variable transformation can be used to make the input density suitable for the RU method and to improve efficiency. In the multivariate case rotation of axes can also be used to improve efficiency. From version 1.2.0 the 'Rcpp' package <https://cran.r-project.org/package=Rcpp> can be used to improve efficiency.

Maintained by Paul J. Northrop. Last updated 7 months ago.

1977 bayesian-inference kinderman monahan of posterior-samples ratio ratio-of-uniforms ratio-of-uniforms-method rcpp simulation transformation uniforms openblas cpp

6.53 score 36 scripts 7 dependents

helske

bssm:Bayesian Inference of Non-Linear and Non-Gaussian State Space Models

Efficient methods for Bayesian inference of state space models via Markov chain Monte Carlo (MCMC) based on parallel importance sampling type weighted estimators (Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>), particle MCMC, and its delayed acceptance version. Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities and basic stochastic volatility models with linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretised diffusion models are supported. See Helske and Vihola (2021, <doi:10.32614/RJ-2021-103>) for details.

Maintained by Jouni Helske. Last updated 7 months ago.

bayesian-inference cpp markov-chain-monte-carlo particle-filter state-space time-series openblas cpp openmp

42 stars 6.43 score 11 scripts

bioc

scMET:Bayesian modelling of cell-to-cell DNA methylation heterogeneity

High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression.

Maintained by Andreas C. Kapourani. Last updated 5 months ago.

immunooncology dnamethylation differentialmethylation differentialexpression geneexpression generegulation epigenetics genetics clustering featureextraction regression bayesian sequencing coverage singlecell bayesian-inference generalised-linear-models heterogeneity hierarchical-models methylation-analysis single-cell cpp

20 stars 6.23 score 42 scripts

bsvars

bsvarSIGNs:Bayesian SVARs with Sign, Zero, and Narrative Restrictions

Implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions (SVARs) identified by sign, zero, and narrative restrictions. The core model is based on a flexible Vector Autoregression with estimated hyper-parameters of the Minnesota prior and the dummy observation priors as in Giannone, Lenza, Primiceri (2015) <doi:10.1162/REST_a_00483>. The sign restrictions are implemented employing the methods proposed by Rubio-Ramírez, Waggoner & Zha (2010) <doi:10.1111/j.1467-937X.2009.00578.x>, while identification through sign and zero restrictions follows the approach developed by Arias, Rubio-Ramírez, & Waggoner (2018) <doi:10.3982/ECTA14468>. Furthermore, our tool provides algorithms for identification via sign and narrative restrictions, in line with the methods introduced by Antolín-Díaz and Rubio-Ramírez (2018) <doi:10.1257/aer.20161852>. Users can also estimate a model with sign, zero, and narrative restrictions imposed at once. The package facilitates predictive and structural analyses using impulse responses, forecast error variance and historical decompositions, forecasting and conditional forecasting, as well as analyses of structural shocks and fitted values. All this is complemented by colourful plots, user-friendly summary functions, and comprehensive documentation including the vignette by Wang & Woźniak (2024) <doi:10.48550/arXiv.2501.16711>. The 'bsvarSIGNs' package is aligned regarding objects, workflows, and code structure with the R package 'bsvars' by Woźniak (2024) <doi:10.32614/CRAN.package.bsvars>, and they constitute an integrated toolset. It was granted the Di Cook Open-Source Statistical Software Award by the Statistical Society of Australia in 2024.

Maintained by Xiaolei Wang. Last updated 2 months ago.

bayesian-inference econometrics vector-autoregression openblas cpp openmp

13 stars 6.21 score 10 scripts

sor16

bdrc:Bayesian Discharge Rating Curves

Fits a discharge rating curve based on the power-law and the generalized power-law from data on paired stage and discharge measurements in a given river using a Bayesian hierarchical model as described in Hrafnkelsson et al. (2020) <arXiv:2010.04769>.

Maintained by Rafael Daníel Vias. Last updated 7 months ago.

bayesian-inference power-law rivers cpp

12 stars 6.07 score 11 scripts

jtimonen

lgpr:Longitudinal Gaussian Process Regression

Interpretable nonparametric modeling of longitudinal data using additive Gaussian process regression. Contains functionality for inferring covariate effects and assessing covariate relevances. Models are specified using a convenient formula syntax, and can include shared, group-specific, non-stationary, heterogeneous and temporally uncertain effects. Bayesian inference for model parameters is performed using 'Stan'. The modeling approach and methods are described in detail in Timonen et al. (2021) <doi:10.1093/bioinformatics/btab021>.

Maintained by Juho Timonen. Last updated 7 months ago.

bayesian-inference gaussian-processes longitudinal-data stan cpp

25 stars 5.94 score 69 scripts

ercrema

baorista:Bayesian Aoristic Analyses

Provides an alternative approach to aoristic analyses for archaeological datasets by fitting Bayesian parametric growth models and non-parametric random-walk Intrinsic Conditional Autoregressive (ICAR) models on time frequency data (Crema (2024)<doi:10.1111/arcm.12984>). It handles event typo-chronology based timespans defined by start/end date as well as more complex user-provided vector of probabilities.

Maintained by Enrico Crema. Last updated 7 months ago.

aoristic-analyses archaeology bayesian-inference

11 stars 5.78 score 7 scripts

reckziegel

ffp:Fully Flexible Probabilities for Stress Testing and Portfolio Construction

Implements numerical entropy-pooling for portfolio construction and scenario analysis as described in Meucci, Attilio (2008) and Meucci, Attilio (2010) <doi:10.2139/ssrn.1696802>.

Maintained by Bernardo Reckziegel. Last updated 2 years ago.

bayesian-inference entropy-pooling flexible-probabilities portolio-optimization risk-management scenarios views

16 stars 5.71 score 32 scripts

bioc

ppcseq:Probabilistic Outlier Identification for RNA Sequencing Generalized Linear Models

Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis of transcript abundance using RNA sequencing data, the negative binomial model is by far the most frequently adopted. However, common methods that are based on a negative binomial model are not robust to extreme outliers, which we found to be abundant in public datasets. So far, no rigorous and probabilistic methods for detection of outliers have been developed for RNA sequencing data, leaving the identification mostly to visual inspection. Recent advances in Bayesian computation allow large-scale comparison of observed data against its theoretical distribution given in a statistical model. Here we propose ppcseq, a key quality-control tool for identifying transcripts that include outlier data points in differential expression analysis, which do not follow a negative binomial distribution. Applying ppcseq to analyse several publicly available datasets using popular tools, we show that from 3 to 10 percent of differentially abundant transcripts across algorithms and datasets had statistics inflated by the presence of outliers.

Maintained by Stefano Mangiola. Last updated 5 months ago.

rnaseq differentialexpression geneexpression normalization clustering qualitycontrol sequencing transcription transcriptomics bayesian-inference deseq2 edger negative-binomial outlier stan cpp

8 stars 5.71 score 16 scripts

graemeleehickey

bayesDP:Implementation of the Bayesian Discount Prior Approach for Clinical Trials

Functions for data augmentation using the Bayesian discount prior method for single arm and two-arm clinical trials, as described in Haddad et al. (2017) <doi:10.1080/10543406.2017.1300907>. The discount power prior methodology was developed in collaboration with the The Medical Device Innovation Consortium (MDIC) Computer Modeling & Simulation Working Group.

Maintained by Graeme L. Hickey. Last updated 3 months ago.

bayesian bayesian-inference bayesian-statistics clinical-trials mdic posterior-predictive posterior-probability prior-distribution openblas cpp

5.56 score 20 scripts 1 dependents

vabar

vibass:Valencia International Bayesian Summer School

Materials for the introductory course on Bayesian inference. Practicals, data and interactive apps.

Maintained by Facundo Muñoz. Last updated 9 months ago.

bayesian-inference teaching

7 stars 5.40 score 2 scripts

gorkang

BayesianReasoning:Plot Positive and Negative Predictive Values for Medical Tests

Functions to plot and help understand positive and negative predictive values (PPV and NPV), and their relationship with sensitivity, specificity, and prevalence. See Akobeng, A.K. (2007) <doi:10.1111/j.1651-2227.2006.00180.x> for a theoretical overview of the technical concepts and Navarrete et al. (2015) for a practical explanation about the importance of their understanding <doi:10.3389/fpsyg.2015.01327>.

Maintained by Gorka Navarrete. Last updated 12 months ago.

bayesian-inference negative-predictive-value positive-predictive-value

8 stars 5.38 score 15 scripts

ercrema

nimbleCarbon:Bayesian Analyses of Radiocarbon Dates with NIMBLE

Provides utility functions and custom probability distribution for Bayesian analyses of radiocarbon dates within the 'nimble' modelling framework. It includes various population growth models, nimbleFunction objects, as well as a suite of functions for prior and posterior predictive checks for demographic inference (Crema and Shoda (2021) <doi:10.1371/journal.pone.0251695>) and other analyses.

Maintained by Enrico Crema. Last updated 7 months ago.

bayesian-inference radiocarbon-dates

18 stars 5.08 score 67 scripts

jbolstad

hbamr:Hierarchical Bayesian Aldrich-McKelvey Scaling via 'Stan'

Perform hierarchical Bayesian Aldrich-McKelvey scaling using Hamiltonian Monte Carlo via 'Stan'. Aldrich-McKelvey ('AM') scaling is a method for estimating the ideological positions of survey respondents and political actors on a common scale using positional survey data. The hierarchical versions of the Bayesian 'AM' model included in this package outperform other versions both in terms of yielding meaningful posterior distributions for respondent positions and in terms of recovering true respondent positions in simulations. The package contains functions for preparing data, fitting models, extracting estimates, plotting key results, and comparing models using cross-validation. The original version of the default model is described in Bølstad (2024) <doi:10.1017/pan.2023.18>.

Maintained by Jørgen Bølstad. Last updated 4 days ago.

bayesian bayesian-inference ideal-point-estimation stan survey-analysis cpp

2 stars 5.04 score

robson-fernandes

bnviewer:Bayesian Networks Interactive Visualization and Explainable Artificial Intelligence

Bayesian networks provide an intuitive framework for probabilistic reasoning and its graphical nature can be interpreted quite clearly. Graph based methods of machine learning are becoming more popular because they offer a richer model of knowledge that can be understood by a human in a graphical format. The 'bnviewer' is an R Package that allows the interactive visualization of Bayesian Networks. The aim of this package is to improve the Bayesian Networks visualization over the basic and static views offered by existing packages.

Maintained by Robson Fernandes. Last updated 5 years ago.

bayesian-inference bayesian-network bayesian-networks probabilistic-graphical-models

7 stars 4.86 score 69 scripts 1 dependents

robson-fernandes

dbnlearn:Dynamic Bayesian Network Structure Learning, Parameter Learning and Forecasting

It allows to learn the structure of univariate time series, learning parameters and forecasting. Implements a model of Dynamic Bayesian Networks with temporal windows, with collections of linear regressors for Gaussian nodes, based on the introductory texts of Korb and Nicholson (2010) <doi:10.1201/b10391> and Nagarajan, Scutari and Lèbre (2013) <doi:10.1007/978-1-4614-6446-4>.

Maintained by Robson Fernandes. Last updated 5 years ago.

bayesian-inference bayesian-networks dynamic-bayesian-networks probabilistic-graphical-models time-series

16 stars 4.32 score 26 scripts

jpritikin

pcFactorStan:Stan Models for the Paired Comparison Factor Model

Provides convenience functions and pre-programmed Stan models related to the paired comparison factor model. Its purpose is to make fitting paired comparison data using Stan easy. This package is described in Pritikin (2020) <doi:10.1016/j.heliyon.2020.e04821>.

Maintained by Joshua N. Pritikin. Last updated 2 years ago.

bayesian-inference factor-analysis paired-comparisons stan cpp

2 stars 4.00 score

jlepird

prefeR:R Package for Pairwise Preference Elicitation

Allows users to derive multi-objective weights from pairwise comparisons, which research shows is more repeatable, transparent, and intuitive other techniques. These weights can be rank existing alternatives or to define a multi-objective utility function for optimization.

Maintained by John Lepird. Last updated 3 years ago.

bayesian-inference multiobjective-optimization preference-elicitation

1 stars 3.90 score 16 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.

bayes bayesian bayesian-data-analysis bayesian-inference bayesian-statistics brms cmdstanr gui mcmc rstan shiny shiny-app stan statistical-analysis statistical-inference statistical-models statistics

10 stars 3.70 score 3 scripts

waleedalmutiry

EpiILMCT:Continuous Time Distance-Based and Network-Based Individual Level Models for Epidemics

Provides tools for simulating from continuous-time individual level models of disease transmission, and carrying out infectious disease data analyses with the same models. The epidemic models considered are distance-based and/or contact network-based models within Susceptible-Infectious-Removed (SIR) or Susceptible-Infectious-Notified-Removed (SINR) compartmental frameworks. An overview of the implemented continuous-time individual level models for epidemics is given by Almutiry and Deardon (2019) <doi:10.1515/ijb-2017-0092>.

Maintained by Waleed Almutiry. Last updated 5 years ago.

bayesian-inference distance epidemic-data epidemics infectious-disease-models kerneltype network plots fortran

6 stars 3.65 score 15 scripts

italo-granato

BGGE:Bayesian Genomic Linear Models Applied to GE Genome Selection

Application of genome prediction for a continuous variable, focused on genotype by environment (GE) genomic selection models (GS). It consists a group of functions that help to create regression kernels for some GE genomic models proposed by Jarquín et al. (2014) <doi:10.1007/s00122-013-2243-1> and Lopez-Cruz et al. (2015) <doi:10.1534/g3.114.016097>. Also, it computes genomic predictions based on Bayesian approaches. The prediction function uses an orthogonal transformation of the data and specific priors present by Cuevas et al. (2014) <doi:10.1534/g3.114.013094>.

Maintained by Italo Granato. Last updated 6 years ago.

bayesian-inference ge-genomic-models genomic genotype-by-environment prediction statistics

1 stars 3.60 score 5 scripts

fleverest

elections.dtree:Ranked Voting Election Audits with Dirichlet-Trees

Perform ballot-polling Bayesian audits for ranked voting elections using Dirichlet-tree prior distributions. Everest et al. (2022) <arXiv:2206.14605>, <arXiv:2209.03881>.

Maintained by Floyd Everest. Last updated 2 years ago.

auditing bayesian-inference election-analysis sequential-models cpp

6 stars 3.48 score 3 scripts

cyianor

CMF:Collective Matrix Factorization

Collective matrix factorization (CMF) finds joint low-rank representations for a collection of matrices with shared row or column entities. This code learns a variational Bayesian approximation for CMF, supporting multiple likelihood potentials and missing data, while identifying both factors shared by multiple matrices and factors private for each matrix. For further details on the method see Klami et al. (2014) <arXiv:1312.5921>. The package can also be used to learn Bayesian canonical correlation analysis (CCA) and group factor analysis (GFA) models, both of which are special cases of CMF. This is likely to be useful for people looking for CCA and GFA solutions supporting missing data and non-Gaussian likelihoods. See Klami et al. (2013) <https://research.cs.aalto.fi/pml/online-papers/klami13a.pdf> and Virtanen et al. (2012) <http://proceedings.mlr.press/v22/virtanen12.html> for details on Bayesian CCA and GFA, respectively.

Maintained by Felix Held. Last updated 2 years ago.

bayesian-inference cmf data-integration cpp

1 stars 3.30 score 8 scripts

zhengxiaouvic

rmBayes:Performing Bayesian Inference for Repeated-Measures Designs

A Bayesian credible interval is interpreted with respect to posterior probability, and this interpretation is far more intuitive than that of a frequentist confidence interval. However, standard highest-density intervals can be wide due to between-subjects variability and tends to hide within-subject effects, rendering its relationship with the Bayes factor less clear in within-subject (repeated-measures) designs. This urgent issue can be addressed by using within-subject intervals in within-subject designs, which integrate four methods including the Wei-Nathoo-Masson (2023) <doi:10.3758/s13423-023-02295-1>, the Loftus-Masson (1994) <doi:10.3758/BF03210951>, the Nathoo-Kilshaw-Masson (2018) <doi:10.1016/j.jmp.2018.07.005>, and the Heck (2019) <doi:10.31234/osf.io/whp8t> interval estimates.

Maintained by Zhengxiao Wei. Last updated 1 years ago.

bayesian-inference credible-interval hdi repeated-measures stan within-subject cpp

2 stars 3.00 score 2 scripts

giabaio

survHEinla:Survival Analysis in Health Economic Evaluation using INLA

A module to complement the backbone structure of the package survHE and expand its functionality to run survival models under a Bayesian approach (based on Integrated Nested Laplace Approximation; the underlying 'INLA' package is available for download at <https://inla.r-inla-download.org/R/stable/>). <doi:10.18637/jss.v095.i14>.

Maintained by Gianluca Baio. Last updated 26 days ago.

bayesian-inference cost-effectiveness-analysis health-economic-evaluation integrated-nested-laplace-approximation survival-analysis uncertainty openjdk

4 stars 2.78 score

federicocomoglio

dupiR:Bayesian Inference from Count Data using Discrete Uniform Priors

We consider a set of sample counts obtained by sampling arbitrary fractions of a finite volume containing an homogeneously dispersed population of identical objects. This package implements a Bayesian derivation of the posterior probability distribution of the population size using a binomial likelihood and non-conjugate, discrete uniform priors under sampling with or without replacement. This can be used for a variety of statistical problems involving absolute quantification under uncertainty. See Comoglio et al. (2013) <doi:10.1371/journal.pone.0074388>.

Maintained by Federico Comoglio. Last updated 1 years ago.

bayesian-inference

1 stars 2.70 score 7 scripts