R-universe search: topic:bayesian-statistics

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 4 days ago.

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

1.1k stars 18.84 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 15 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

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 9 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 4 days ago.

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

1.1k stars 15.67 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

stan-dev

rstantools:Tools for Developing R Packages Interfacing with 'Stan'

Provides various tools for developers of R packages interfacing with 'Stan' <https://mc-stan.org>, including functions to set up the required package structure, S3 generics and default methods to unify function naming across 'Stan'-based R packages, and vignettes with recommendations for developers.

Maintained by Jonah Gabry. Last updated 2 months ago.

bayesian-data-analysis bayesian-statistics developer-tools stan

50 stars 13.09 score 134 scripts 222 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

ecmerkle

blavaan:Bayesian Latent Variable Analysis

Fit a variety of Bayesian latent variable models, including confirmatory factor analysis, structural equation models, and latent growth curve models. References: Merkle & Rosseel (2018) <doi:10.18637/jss.v085.i04>; Merkle et al. (2021) <doi:10.18637/jss.v100.i06>.

Maintained by Edgar Merkle. Last updated 9 days ago.

bayesian-statistics factor-analysis growth-curve-models latent-variables missing-data multilevel-models multivariate-analysis path-analysis psychometrics statistical-modeling structural-equation-modeling cpp

92 stars 10.84 score 183 scripts 3 dependents

r-lum

Luminescence:Comprehensive Luminescence Dating Data Analysis

A collection of various R functions for the purpose of Luminescence dating data analysis. This includes, amongst others, data import, export, application of age models, curve deconvolution, sequence analysis and plotting of equivalent dose distributions.

Maintained by Sebastian Kreutzer. Last updated 1 hours ago.

bayesian-statistics data-science geochronology luminescence luminescence-dating open-science osl plotting radiofluorescence tl xsyg cpp

15 stars 10.66 score 178 scripts 8 dependents

nicholasjclark

mvgam:Multivariate (Dynamic) Generalized Additive Models

Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2023) <doi:10.1111/2041-210X.13974>.

Maintained by Nicholas J Clark. Last updated 1 days ago.

bayesian-statistics dynamic-factor-models ecological-modelling forecasting gaussian-process generalised-additive-models generalized-additive-models joint-species-distribution-modelling multilevel-models multivariate-timeseries stan time-series-analysis timeseries vector-autoregression vectorautoregression cpp

146 stars 9.90 score 117 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

bayes-rules

bayesrules:Datasets and Supplemental Functions from Bayes Rules! Book

Provides datasets and functions used for analysis and visualizations in the Bayes Rules! book (<https://www.bayesrulesbook.com>). The package contains a set of functions that summarize and plot Bayesian models from some conjugate families and another set of functions for evaluation of some Bayesian models.

Maintained by Mine Dogucu. Last updated 3 years ago.

bayesian-statistics data

72 stars 8.06 score 466 scripts

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

wwiecek

baggr:Bayesian Aggregate Treatment Effects

Running and comparing meta-analyses of data with hierarchical Bayesian models in Stan, including convenience functions for formatting data, plotting and pooling measures specific to meta-analysis. This implements many models from Meager (2019) <doi:10.1257/app.20170299>.

Maintained by Witold Wiecek. Last updated 4 days ago.

bayesian-statistics meta-analysis quantile-regression stan treatment-effects cpp

49 stars 7.24 score 88 scripts

archaeostat

ArchaeoPhases:Post-Processing of Markov Chain Monte Carlo Simulations for Chronological Modelling

Statistical analysis of archaeological dates and groups of dates. This package allows to post-process Markov Chain Monte Carlo (MCMC) simulations from 'ChronoModel' <https://chronomodel.com/>, 'Oxcal' <https://c14.arch.ox.ac.uk/oxcal.html> or 'BCal' <https://bcal.shef.ac.uk/>. It provides functions for the study of rhythms of the long term from the posterior distribution of a series of dates (tempo and activity plot). It also allows the estimation and visualization of time ranges from the posterior distribution of groups of dates (e.g. duration, transition and hiatus between successive phases) as described in Philippe and Vibet (2020) <doi:10.18637/jss.v093.c01>.

Maintained by Anne Philippe. Last updated 12 months ago.

archaeology bayesian-statistics geochronology markov-chain radiocarbon-dates

10 stars 6.90 score 66 scripts

crp2a

BayLum:Chronological Bayesian Models Integrating Optically Stimulated Luminescence and Radiocarbon Age Dating

Bayesian analysis of luminescence data and C-14 age estimates. Bayesian models are based on the following publications: Combes, B. & Philippe, A. (2017) <doi:10.1016/j.quageo.2017.02.003> and Combes et al (2015) <doi:10.1016/j.quageo.2015.04.001>. This includes, amongst others, data import, export, application of age models and palaeodose model.

Maintained by Anne Philippe. Last updated 12 months ago.

archaeometry bayesian-statistics geochronology luminescence-dating radiocarbon-dates jags cpp

9 stars 6.22 score 37 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-analysis bayesian-statistics jags mcmc psychological-science cpp

10 stars 5.89 score 31 scripts

jeffreypullin

rater:Statistical Models of Repeated Categorical Rating Data

Fit statistical models based on the Dawid-Skene model - Dawid and Skene (1979) <doi:10.2307/2346806> - to repeated categorical rating data. Full Bayesian inference for these models is supported through the Stan modelling language. 'rater' also allows the user to extract and plot key parameters of these models.

Maintained by Jeffrey Pullin. Last updated 2 years ago.

annotations bayesian bayesian-statistics stan cpp

17 stars 5.83 score 20 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

bayesplay

bayesplay:The Bayes Factor Playground

A lightweight modelling syntax for defining likelihoods and priors and for computing Bayes factors for simple one parameter models. It includes functionality for computing and plotting priors, likelihoods, and model predictions. Additional functionality is included for computing and plotting posteriors.

Maintained by Lincoln John Colling. Last updated 1 years ago.

bayes bayesian bayesian-statistics

6 stars 5.54 score 23 scripts

annajenul

UBayFS:A User-Guided Bayesian Framework for Ensemble Feature Selection (UBayFS)

Implements the user-guided Bayesian framework for ensemble feature selection (UBayFS) : Jenul et al., (2022) <doi:10.1007/s10994-022-06221-9>.

Maintained by Anna Jenul. Last updated 2 years ago.

bayesian-statistics ensemble-models feature-selection user-knowledge

5 stars 5.11 score 13 scripts

connordonegan

surveil:Time Series Models for Disease Surveillance

Fits time trend models for routine disease surveillance tasks and returns probability distributions for a variety of quantities of interest, including age-standardized rates, period and cumulative percent change, and measures of health inequality. The models are appropriate for count data such as disease incidence and mortality data, employing a Poisson or binomial likelihood and the first-difference (random-walk) prior for unknown risk. Optionally add a covariance matrix for multiple, correlated time series models. Inference is completed using Markov chain Monte Carlo via the Stan modeling language. References: Donegan, Hughes, and Lee (2022) <doi:10.2196/34589>; Stan Development Team (2021) <https://mc-stan.org>; Theil (1972, ISBN:0-444-10378-3).

Maintained by Connor Donegan. Last updated 9 months ago.

bayesian-statistics cancer health-equity public-health rstan cpp

2 stars 4.98 score 12 scripts

graemeleehickey

goldilocks:Goldilocks Adaptive Trial Designs for Time-to-Event Endpoints

Implements the Goldilocks adaptive trial design for a time to event outcome using a piecewise exponential model and conjugate Gamma prior distributions. The method closely follows the article by Broglio and colleagues <doi:10.1080/10543406.2014.888569>, which allows users to explore the operating characteristics of different trial designs.

Maintained by Graeme L. Hickey. Last updated 2 months ago.

adaptive bayesian bayesian-statistics clinical-trials statistics cpp

7 stars 4.85 score 4 scripts

graemeleehickey

adaptDiag:Bayesian Adaptive Designs for Diagnostic Trials

Simulate clinical trials for diagnostic test devices and evaluate the operating characteristics under an adaptive design with futility assessment determined via the posterior predictive probabilities.

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

adaptive bayesian bayesian-statistics clinical-trials diagnostic-tests diagnostics statistics

4 stars 4.60 score 5 scripts

inbo

inlatools:Diagnostic Tools for INLA Models

Several functions which can be useful to choose sensible priors and diagnose the fitted model.

Maintained by Thierry Onkelinx. Last updated 6 months ago.

bayesian-statistics gplv3 inla mixed-models model-checking model-validation

4 stars 4.41 score 43 scripts

santoroma

CircSpaceTime:Spatial and Spatio-Temporal Bayesian Model for Circular Data

Implementation of Bayesian models for spatial and spatio-temporal interpolation of circular data using Gaussian Wrapped and Gaussian Projected distributions. We developed the methods described in Jona Lasinio G. et al. (2012) <doi: 10.1214/12-aoas576>, Wang F. et al. (2014) <doi: 10.1080/01621459.2014.934454> and Mastrantonio G. et al. (2016) <doi: 10.1007/s11749-015-0458-y>.

Maintained by Mario Santoro. Last updated 6 years ago.

bayesian-statistics circular-statistics projected-gaussian projected-normal spatial-data-analysis spatio-temporal wrapped-gaussian wrapped-normal openblas cpp openmp

7 stars 3.98 score 27 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

archaeostat

ArchaeoChron:Bayesian Modeling of Archaeological Chronologies

Provides a list of functions for the Bayesian modeling of archaeological chronologies. The Bayesian models are implemented in 'JAGS' (Plummer 2003). The inputs are measurements with their associated standard deviations and the study period. The output is the MCMC sample of the posterior distribution of the event date with or without radiocarbon calibration.

Maintained by Anne Philippe. Last updated 1 years ago.

archaeology bayesian-statistics geochronology markov-chain radiocarbon-dates jags cpp

3 stars 3.65 score 15 scripts

giabaio

bmhe:This Package Creates a Set of Functions Useful for Bayesian modelling

A set of utility functions that can be used to post-process BUGS or JAGS objects as well as other to facilitate various Bayesian modelling activities (including in HTA).

Maintained by Gianluca Baio. Last updated 23 days ago.

bayesian-statistics bugs cost-effectiveness-analysis jags tidyverse

2 stars 3.00 score 7 scripts