R-universe search: rjags

martynplummer

rjags:Bayesian Graphical Models using MCMC

Interface to the JAGS MCMC library.

Maintained by Martyn Plummer. Last updated 7 months ago.

jags cpp

51.9 match 7 stars 9.48 score 4.0k scripts 165 dependents

ropensci

jagstargets:Targets for JAGS Pipelines

Bayesian data analysis usually incurs long runtimes and cumbersome custom code. A pipeline toolkit tailored to Bayesian statisticians, the 'jagstargets' R package is leverages 'targets' and 'R2jags' to ease this burden. 'jagstargets' makes it super easy to set up scalable JAGS pipelines that automatically parallelize the computation and skip expensive steps when the results are already up to date. Minimal custom code is required, and there is no need to manually configure branching, so usage is much easier than 'targets' alone. For the underlying methodology, please refer to the documentation of 'targets' <doi:10.21105/joss.02959> and 'JAGS' (Plummer 2003) <https://www.r-project.org/conferences/DSC-2003/Proceedings/Plummer.pdf>.

Maintained by William Michael Landau. Last updated 3 months ago.

bayesian high-performance-computing jags make r-targetopia reproducibility rjags statistics targets cpp

10.0 match 10 stars 7.01 score 32 scripts

bioc

HiLDA:Conducting statistical inference on comparing the mutational exposures of mutational signatures by using hierarchical latent Dirichlet allocation

A package built under the Bayesian framework of applying hierarchical latent Dirichlet allocation. It statistically tests whether the mutational exposures of mutational signatures (Shiraishi-model signatures) are different between two groups. The package also provides inference and visualization.

Maintained by Zhi Yang. Last updated 5 months ago.

software somaticmutation sequencing statisticalmethod bayesian mutational-signatures rjags somatic-mutations cpp jags

10.0 match 3 stars 5.56 score 7 scripts 1 dependents

bioc

selectKSigs:Selecting the number of mutational signatures using a perplexity-based measure and cross-validation

A package to suggest the number of mutational signatures in a collection of somatic mutations using calculating the cross-validated perplexity score.

Maintained by Zhi Yang. Last updated 5 months ago.

software somaticmutation sequencing statisticalmethod clustering mutational-signatures rjags somatic-mutations cpp jags

10.0 match 3 stars 4.08 score 1 scripts

kenkellner

jagsUI:A Wrapper Around 'rjags' to Streamline 'JAGS' Analyses

A set of wrappers around 'rjags' functions to run Bayesian analyses in 'JAGS' (specifically, via 'libjags'). A single function call can control adaptive, burn-in, and sampling MCMC phases, with MCMC chains run in sequence or in parallel. Posterior distributions are automatically summarized (with the ability to exclude some monitored nodes if desired) and functions are available to generate figures based on the posteriors (e.g., predictive check plots, traceplots). Function inputs, argument syntax, and output format are nearly identical to the 'R2WinBUGS'/'R2OpenBUGS' packages to allow easy switching between MCMC samplers.

Maintained by Ken Kellner. Last updated 1 months ago.

jags cpp

3.4 match 35 stars 10.02 score 1.4k scripts 7 dependents

s-mckay-curtis

mcmcplots:Create Plots from MCMC Output

Functions for convenient plotting and viewing of MCMC output.

Maintained by S. McKay Curtis. Last updated 7 years ago.

4.0 match 4 stars 6.53 score 880 scripts 4 dependents

kwstat

agridat:Agricultural Datasets

Datasets from books, papers, and websites related to agriculture. Example graphics and analyses are included. Data come from small-plot trials, multi-environment trials, uniformity trials, yield monitors, and more.

Maintained by Kevin Wright. Last updated 27 days ago.

data

2.0 match 125 stars 11.02 score 1.7k scripts 2 dependents

suyusung

R2jags:Using R to Run 'JAGS'

Providing wrapper functions to implement Bayesian analysis in JAGS. Some major features include monitoring convergence of a MCMC model using Rubin and Gelman Rhat statistics, automatically running a MCMC model till it converges, and implementing parallel processing of a MCMC model for multiple chains.

Maintained by Yu-Sung Su. Last updated 4 months ago.

jags cpp

1.9 match 8 stars 11.43 score 3.4k scripts 47 dependents

mikemeredith

mcmcOutput:Functions to Store, Manipulate and Display Markov Chain Monte Carlo (MCMC) Output

Implements a class ('mcmcOutput') for efficiently storing and handling Markov chain Monte Carlo (MCMC) output, intended as an aid for those writing customized MCMC samplers. A range of constructor methods are provided covering common output formats. Functions are provided to generate summary and diagnostic statistics and to display histograms or density plots of posterior distributions, for the entire output, or subsets of draws, nodes, or parameters.

Maintained by Ngumbang Juat. Last updated 2 years ago.

3.4 match 4 stars 4.74 score 46 scripts 2 dependents

jcliff89

ContRespPP:Predictive Probability for a Continuous Response with an ANOVA Structure

A Bayesian approach to using predictive probability in an ANOVA construct with a continuous normal response, when threshold values must be obtained for the question of interest to be evaluated as successful (Sieck and Christensen (2021) <doi:10.1002/qre.2802>). The Bayesian Mission Mean (BMM) is used to evaluate a question of interest (that is, a mean that randomly selects combination of factor levels based on their probability of occurring instead of averaging over the factor levels, as in the grand mean). Under this construct, in contrast to a Gibbs sampler (or Metropolis-within-Gibbs sampler), a two-stage sampling method is required. The nested sampler determines the conditional posterior distribution of the model parameters, given Y, and the outside sampler determines the marginal posterior distribution of Y (also commonly called the predictive distribution for Y). This approach provides a sample from the joint posterior distribution of Y and the model parameters, while also accounting for the threshold value that must be obtained in order for the question of interest to be evaluated as successful.

Maintained by Victoria Sieck. Last updated 2 years ago.

3.8 match 3.70 score 4 scripts

bioc

beer:Bayesian Enrichment Estimation in R

BEER implements a Bayesian model for analyzing phage-immunoprecipitation sequencing (PhIP-seq) data. Given a PhIPData object, BEER returns posterior probabilities of enriched antibody responses, point estimates for the relative fold-change in comparison to negative control samples, and more. Additionally, BEER provides a convenient implementation for using edgeR to identify enriched antibody responses.

Maintained by Athena Chen. Last updated 5 months ago.

software statisticalmethod bayesian sequencing coverage jags cpp

2.0 match 10 stars 5.38 score 12 scripts

mamaz7

AICcmodavg:Model Selection and Multimodel Inference Based on (Q)AIC(c)

Functions to implement model selection and multimodel inference based on Akaike's information criterion (AIC) and the second-order AIC (AICc), as well as their quasi-likelihood counterparts (QAIC, QAICc) from various model object classes. The package implements classic model averaging for a given parameter of interest or predicted values, as well as a shrinkage version of model averaging parameter estimates or effect sizes. The package includes diagnostics and goodness-of-fit statistics for certain model types including those of 'unmarkedFit' classes estimating demographic parameters after accounting for imperfect detection probabilities. Some functions also allow the creation of model selection tables for Bayesian models of the 'bugs', 'rjags', and 'jagsUI' classes. Functions also implement model selection using BIC. Objects following model selection and multimodel inference can be formatted to LaTeX using 'xtable' methods included in the package.

Maintained by Marc J. Mazerolle. Last updated 10 days ago.

0.5 match 1 stars 7.83 score 1.8k scripts 8 dependents

nerler

JointAI:Joint Analysis and Imputation of Incomplete Data

Joint analysis and imputation of incomplete data in the Bayesian framework, using (generalized) linear (mixed) models and extensions there of, survival models, or joint models for longitudinal and survival data, as described in Erler, Rizopoulos and Lesaffre (2021) <doi:10.18637/jss.v100.i20>. Incomplete covariates, if present, are automatically imputed. The package performs some preprocessing of the data and creates a 'JAGS' model, which will then automatically be passed to 'JAGS' <https://mcmc-jags.sourceforge.io/> with the help of the package 'rjags'.

Maintained by Nicole S. Erler. Last updated 12 months ago.

bayesian generalized-linear-models glm glmm imputation imputations jags joint-analysis linear-mixed-models linear-regression-models mcmc-sample mcmc-sampling missing-data missing-values survival cpp

0.5 match 28 stars 7.30 score 59 scripts 1 dependents

nimble-dev

compareMCMCs:Compare MCMC Efficiency from 'nimble' and/or Other MCMC Engines

Manages comparison of MCMC performance metrics from multiple MCMC algorithms. These may come from different MCMC configurations using the 'nimble' package or from other packages. Plug-ins for JAGS via 'rjags' and Stan via 'rstan' are provided. It is possible to write plug-ins for other packages. Performance metrics are held in an MCMCresult class along with samples and timing data. It is easy to apply new performance metrics. Reports are generated as html pages with figures comparing sets of runs. It is possible to configure the html pages, including providing new figure components.

Maintained by Perry de Valpine. Last updated 6 months ago.

0.5 match 1 stars 4.71 score 17 scripts

mikejseo

bnma:Bayesian Network Meta-Analysis using 'JAGS'

Network meta-analyses using Bayesian framework following Dias et al. (2013) <DOI:10.1177/0272989X12458724>. Based on the data input, creates prior, model file, and initial values needed to run models in 'rjags'. Able to handle binomial, normal and multinomial arm-level data. Can handle multi-arm trials and includes methods to incorporate covariate and baseline risk effects. Includes standard diagnostics and visualization tools to evaluate the results.

Maintained by Michael Seo. Last updated 1 years ago.

jags cpp

0.5 match 7 stars 4.54 score 7 scripts

arinams

saeHB.spatial:Small Area Estimation Hierarchical Bayes For Spatial Model

Provides several functions and datasets for area level of Small Area Estimation under Spatial Model using Hierarchical Bayesian (HB) Method. Model-based estimators include the HB estimators based on a Spatial Fay-Herriot model with univariate normal distribution for variable of interest.The 'rjags' package is employed to obtain parameter estimates. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.

Maintained by Arina Mana Sikana. Last updated 4 months ago.

jags cpp

0.5 match 4.00 score 6 scripts

ratihrodliyah

saeHB.ME.beta:SAE with Measurement Error using HB under Beta Distribution

Implementation of Small Area Estimation (SAE) using Hierarchical Bayesian (HB) Method when auxiliary variable measured with error under Beta Distribution. The 'rjags' package is employed to obtain parameter estimates. For the references, see J.N.K & Molina (2015) <doi:10.1002/9781118735855>, Ybarra and Sharon (2008) <doi:10.1093/biomet/asn048>, and Ntzoufras (2009, ISBN-10: 1118210352).

Maintained by Ratih Rodliyah. Last updated 2 years ago.

jags cpp

0.5 match 3.70 score 3 scripts

veliatrimarliana

saeHB.panel:Small Area Estimation using Hierarchical Bayesian Method for Rao Yu Model

We designed this package to provide several functions for area level of small area estimation using hierarchical Bayesian (HB) method. This package provides model using panel data for variable interest.This package also provides a dataset produced by a data generation. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015).

Maintained by Velia Tri Marliana. Last updated 3 years ago.

jags cpp

0.5 match 1 stars 3.70 score 3 scripts

dianrahmawatisalis

saeHB.panel.beta:Small Area Estimation using HB for Rao Yu Model under Beta Distribution

Several functions are provided for small area estimation at the area level using the hierarchical bayesian (HB) method with panel data under beta distribution for variable interest. This package also provides a dataset produced by data generation. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involve the HB estimators, which include the mean and the variation of the mean. For the reference, see Rao and Molina (2015, ISBN: 978-1-118-73578-7).

Maintained by Dian Rahmawati Salis. Last updated 2 years ago.

jags cpp

0.5 match 3.70 score 4 scripts

reymath99

saeHB.twofold:Hierarchical Bayes Twofold Subarea Level Model SAE

We designed this package to provides several functions for area and subarea level of small area estimation under Twofold Subarea Level Model using hierarchical Bayesian (HB) method with Univariate Normal distribution for variables of interest. Some dataset simulated by a data generation are also provided. The 'rjags' package is employed to obtain parameter estimates using Gibbs Sampling algorithm. Model-based estimators involves the HB estimators which include the mean, the variation of mean, and the quantile. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Torabi and Rao (2014) <doi:10.1016/j.jmva.2014.02.001>, Leyla Mohadjer et al.(2007) <http://www.asasrms.org/Proceedings/y2007/Files/JSM2007-000559.pdf>, and Erciulescu et al.(2019) <doi:10.1111/rssa.12390>.

Maintained by Reyhan Saadi. Last updated 3 years ago.

jags cpp

0.5 match 1 stars 3.70 score 3 scripts

rizqinar

saeHB.ZIB:Small Area Estimation using Hierarchical Bayesian under Zero Inflated Binomial Distribution

Provides function for area level of small area estimation using hierarchical Bayesian (HB) method with Zero-Inflated Binomial distribution for variables of interest. Some dataset produced by a data generation are also provided. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean.

Maintained by Rizqina Rahmati. Last updated 3 years ago.

jags cpp

0.5 match 2.00 score 2 scripts

jjacklee116

BCHM:Clinical Trial Calculation Based on BCHM Design

Users can estimate the treatment effect for multiple subgroups basket trials based on the Bayesian Cluster Hierarchical Model (BCHM). In this model, a Bayesian non-parametric method is applied to dynamically calculate the number of clusters by conducting the multiple cluster classification based on subgroup outcomes. Hierarchical model is used to compute the posterior probability of treatment effect with the borrowing strength determined by the Bayesian non-parametric clustering and the similarities between subgroups. To use this package, 'JAGS' software and 'rjags' package are required, and users need to pre-install them.

Maintained by J. Jack Lee. Last updated 5 years ago.

jags cpp

0.5 match 2.00 score 4 scripts

cran

msaeHB:Multivariate Small Area Estimation using Hierarchical Bayesian Method

Implements area level of multivariate small area estimation using Hierarchical Bayesian method under Normal and T distribution. The 'rjags' package is employed to obtain parameter estimates. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.

Maintained by Novia Permatasari. Last updated 3 years ago.

jags cpp

0.5 match 1.00 score

cran

saeHB.ME:Small Area Estimation with Measurement Error using Hierarchical Bayesian Method

Implementation of small area estimation using Hierarchical Bayesian (HB) Method when auxiliary variable measured with error. The 'rjags' package is employed to obtain parameter estimates. For the references, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Ybarra and Lohr (2008) <doi:10.1093/biomet/asn048>, and Ntzoufras (2009, ISBN-10: 1118210352).

Maintained by Muhammad Rifqi Mubarak. Last updated 2 years ago.

jags cpp

0.5 match 1 stars 1.00 score

cran

BACCT:Bayesian Augmented Control for Clinical Trials

Implements the Bayesian Augmented Control (BAC, a.k.a. Bayesian historical data borrowing) method under clinical trial setting by calling 'Just Another Gibbs Sampler' ('JAGS') software. In addition, the 'BACCT' package evaluates user-specified decision rules by computing the type-I error/power, or probability of correct go/no-go decision at interim look. The evaluation can be presented numerically or graphically. Users need to have 'JAGS' 4.0.0 or newer installed due to a compatibility issue with 'rjags' package. Currently, the package implements the BAC method for binary outcome only. Support for continuous and survival endpoints will be added in future releases. We would like to thank AbbVie's Statistical Innovation group and Clinical Statistics group for their support in developing the 'BACCT' package.

Maintained by Hongtao Zhang. Last updated 9 years ago.

jags cpp

0.5 match 1.00 score 9 scripts

kjryan13

ordinalRR:Analysis of Repeatability and Reproducibility Studies with Ordinal Measurements

Implements Bayesian data analyses of balanced repeatability and reproducibility studies with ordinal measurements. Model fitting is based on MCMC posterior sampling with 'rjags'. Function ordinalRR() directly carries out the model fitting, and this function has the flexibility to allow the user to specify key aspects of the model, e.g., fixed versus random effects. Functions for preprocessing data and for the numerical and graphical display of a fitted model are also provided. There are also functions for displaying the model at fixed (user-specified) parameters and for simulating a hypothetical data set at a fixed (user-specified) set of parameters for a random-effects rater population. For additional technical details, refer to Culp, Ryan, Chen, and Hamada (2018) and cite this Technometrics paper when referencing any aspect of this work. The demo of this package reproduces results from the Technometrics paper.

Maintained by Ken Ryan. Last updated 5 years ago.

jags cpp

0.5 match 1.00 score 9 scripts