R-universe search: topic:bayesian-methods

Showing 19 of total 19 results (show query)

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 17 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 11 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

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 18 days ago.

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

169 stars 12.97 score 2.6k scripts 19 dependents

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

stochastictree

stochtree:Stochastic Tree Ensembles (XBART and BART) for Supervised Learning and Causal Inference

Flexible stochastic tree ensemble software. Robust implementations of Bayesian Additive Regression Trees (BART) Chipman, George, McCulloch (2010) <doi:10.1214/09-AOAS285> for supervised learning and Bayesian Causal Forests (BCF) Hahn, Murray, Carvalho (2020) <doi:10.1214/19-BA1195> for causal inference. Enables model serialization and parallel sampling and provides a low-level interface for custom stochastic forest samplers.

Maintained by Drew Herren. Last updated 3 days ago.

bart bayesian-machine-learning bayesian-methods decision-trees gradient-boosted-trees machine-learning probabilistic-models tree-ensembles cpp

23 stars 8.60 score 40 scripts

n-kall

priorsense:Prior Diagnostics and Sensitivity Analysis

Provides functions for prior and likelihood sensitivity analysis in Bayesian models. Currently it implements methods to determine the sensitivity of the posterior to power-scaling perturbations of the prior and likelihood.

Maintained by Noa Kallioinen. Last updated 26 days ago.

bayes bayesian bayesian-data-analysis bayesian-methods prior-distribution sensitivity-analysis stan

59 stars 8.27 score 70 scripts

frankportman

bayesAB:Fast Bayesian Methods for AB Testing

A suite of functions that allow the user to analyze A/B test data in a Bayesian framework. Intended to be a drop-in replacement for common frequentist hypothesis test such as the t-test and chi-sq test.

Maintained by Frank Portman. Last updated 4 years ago.

ab-testing bayesian-methods bayesian-tests cpp

308 stars 7.43 score 88 scripts

thevaachandereng

bayesCT:Simulation and Analysis of Adaptive Bayesian Clinical Trials

Simulation and analysis of Bayesian adaptive clinical trials for binomial, continuous, and time-to-event data types, incorporates historical data and allows early stopping for futility or early success. The package uses novel and efficient Monte Carlo methods for estimating Bayesian posterior probabilities, evaluation of loss to follow up, and imputation of incomplete data. The package has the functionality for dynamically incorporating historical data into the analysis via the power prior or non-informative priors.

Maintained by Thevaa Chandereng. Last updated 5 years ago.

adaptive bayesian-methods bayesian-trial clinical-trials statistical-power

14 stars 6.30 score 36 scripts

epiverse-trace

serofoi:Bayesian Estimation of the Force of Infection from Serological Data

Estimating the force of infection from time varying, age varying, or constant serocatalytic models from population based seroprevalence studies using a Bayesian framework, including data simulation functions enabling the generation of serological surveys based on this models. This tool also provides a flexible prior specification syntax for the force of infection and the seroreversion rate, as well as methods to assess model convergence and comparison criteria along with useful visualisation functions.

Maintained by Zulma M. Cucunubá. Last updated 2 days ago.

antibodies bayesian-methods epidemiology epiverse serological-surveys stan-language cpp

19 stars 6.20 score 10 scripts

traitecoevo

hmde:Hierarchical Methods for Differential Equations

Wrapper for Stan that offers a number of in-built models to implement a hierarchical Bayesian longitudinal model for repeat observation data. Model choice selects the differential equation that is fit to the observations. Single and multi-individual models are available.

Maintained by Tess OBrien. Last updated 6 days ago.

bayesian-inverse-problems bayesian-methods differential-equations hierarchical-models rstan stan cpp

3 stars 5.56 score 10 scripts

guillaumebiessy

WH:Enhanced Implementation of Whittaker-Henderson Smoothing

An enhanced implementation of Whittaker-Henderson smoothing for the gradation of one-dimensional and two-dimensional actuarial tables used to quantify Life Insurance risks. 'WH' is based on the methods described in Biessy (2023) <doi:10.48550/arXiv.2306.06932>. Among other features, it generalizes the original smoothing algorithm to maximum likelihood estimation, automatically selects the smoothing parameter(s) and extrapolates beyond the range of data.

Maintained by Guillaume Biessy. Last updated 4 months ago.

actuarial-science bayesian-methods mortality-estimation smoothing-methods

9 stars 5.35 score 6 scripts

comodin19

BayesVarSel:Bayes Factors, Model Choice and Variable Selection in Linear Models

Bayes factors and posterior probabilities in Linear models, aimed at provide a formal Bayesian answer to testing and variable selection problems.

Maintained by Gonzalo Garcia-Donato. Last updated 3 months ago.

bayesian-methods linear-models gsl

8 stars 5.18 score 26 scripts 1 dependents

boehringer-ingelheim

tipmap:Tipping Point Analysis for Bayesian Dynamic Borrowing

Tipping point analysis for clinical trials that employ Bayesian dynamic borrowing via robust meta-analytic predictive (MAP) priors. Further functions facilitate expert elicitation of a primary weight of the informative component of the robust MAP prior and computation of operating characteristics. Intended use is the planning, analysis and interpretation of extrapolation studies in pediatric drug development, but applicability is generally wider.

Maintained by Christian Stock. Last updated 12 months ago.

bayesian-borrowing bayesian-methods clinical-trial evidence-synthesis extrapolation pediatrics pharmaceutical-development prior-elicitation tipping-point weighting

2 stars 4.38 score 12 scripts

dmenne

breathteststan:Stan-Based Fit to Gastric Emptying Curves

Stan-based curve-fitting function for use with package 'breathtestcore' by the same author. Stan functions are refactored here for easier testing.

Maintained by Dieter Menne. Last updated 3 months ago.

bayesian-methods fitting medical nonlinear stan cpp

3 stars 4.18 score 9 scripts

frahik

IBCF.MTME:Item Based Collaborative Filtering for Multi-Trait and Multi-Environment Data

Implements the item based collaborative filtering (IBCF) method for continues phenotypes in the context of plant breeding where data are collected for various traits that were studied in various environments proposed by Montesinos-López et al. (2017) <doi:10.1534/g3.117.300309>.

Maintained by Francisco Javier Luna-Vazquez. Last updated 6 years ago.

bayesian-methods predictive-modeling

2 stars 3.28 score 19 scripts

topipa

iwmm:Importance weighted moment matching

iwmm provides functions for adaptive importance sampling.

Maintained by Topi Paananen. Last updated 7 months ago.

bayesian bayesian-data-analysis bayesian-methods stan

8 stars 3.20 score 3 scripts

alexmclain

probe:Sparse High-Dimensional Linear Regression with a PaRtitiOned Empirical Bayes Ecm (PROBE) Algorithm

Implements an efficient and powerful Bayesian approach for sparse high-dimensional linear regression. It uses minimal prior assumptions on the parameters through plug-in empirical Bayes estimates of hyperparameters. An efficient Parameter-Expanded Expectation-Conditional-Maximization (PX-ECM) algorithm estimates maximum a posteriori (MAP) values of regression parameters and variable selection probabilities. The PX-ECM results in a robust computationally efficient coordinate-wise optimization, which adjusts for the impact of other predictor variables. The E-step is motivated by the popular two-group approach to multiple testing. The result is a PaRtitiOned empirical Bayes Ecm (PROBE) algorithm applied to sparse high-dimensional linear regression, implemented using one-at-a-time or all-at-once type optimization. Simulation studies found the all-at-once variant to be superior.

Maintained by Alexander McLain. Last updated 8 months ago.

bayesian-methods high-dimensional-data high-dimensional-inference linear-models machine-learning openblas cpp openmp

1 stars 3.18 score 4 scripts

boehringer-ingelheim

BPrinStratTTE:Causal Effects in Principal Strata Defined by Antidrug Antibodies

Bayesian models to estimate causal effects of biological treatments on time-to-event endpoints in clinical trials with principal strata defined by the occurrence of antidrug antibodies. The methodology is based on Frangakis and Rubin (2002) <doi:10.1111/j.0006-341x.2002.00021.x> and Imbens and Rubin (1997) <doi:10.1214/aos/1034276631>, and here adapted to a specific time-to-event setting.

Maintained by Christian Stock. Last updated 12 months ago.

bayesian-methods causal-inference clinical-trial estimand mcmc-methods pharmaceutical-development principal-stratification simulation stan time-to-event cpp

3.18 score