Showing 178 of total 178 results (show query)
mjskay
tidybayes:Tidy Data and 'Geoms' for Bayesian Models
Compose data for and extract, manipulate, and visualize posterior draws from Bayesian models ('JAGS', 'Stan', 'rstanarm', 'brms', 'MCMCglmm', 'coda', ...) in a tidy data format. Functions are provided to help extract tidy data frames of draws from Bayesian models and that generate point summaries and intervals in a tidy format. In addition, 'ggplot2' 'geoms' and 'stats' are provided for common visualization primitives like points with multiple uncertainty intervals, eye plots (intervals plus densities), and fit curves with multiple, arbitrary uncertainty bands.
Maintained by Matthew Kay. Last updated 6 months ago.
bayesian-data-analysisbrmsggplot2jagsstantidy-datavisualization
733 stars 14.72 score 7.3k scripts 20 dependents
xfim
ggmcmc:Tools for Analyzing MCMC Simulations from Bayesian Inference
Tools for assessing and diagnosing convergence of Markov Chain Monte Carlo simulations, as well as for graphically display results from full MCMC analysis. The package also facilitates the graphical interpretation of models by providing flexible functions to plot the results against observed variables, and functions to work with hierarchical/multilevel batches of parameters (Fernández-i-Marín, 2016 <doi:10.18637/jss.v070.i09>).
Maintained by Xavier Fernández i Marín. Last updated 2 years ago.
bayesian-data-analysisggplot2graphicaljagsmcmcstan
111 stars 11.94 score 1.6k scripts 8 dependents
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 5 months ago.
8 stars 11.39 score 3.4k scripts 47 dependents
bioc
infercnv:Infer Copy Number Variation from Single-Cell RNA-Seq Data
Using single-cell RNA-Seq expression to visualize CNV in cells.
Maintained by Christophe Georgescu. Last updated 5 months ago.
softwarecopynumbervariationvariantdetectionstructuralvariationgenomicvariationgeneticstranscriptomicsstatisticalmethodbayesianhiddenmarkovmodelsinglecelljagscpp
601 stars 10.92 score 674 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 2 months ago.
35 stars 9.99 score 1.4k scripts 7 dependents
pecanproject
PEcAn.assim.batch:PEcAn Functions Used for Ecological Forecasts and Reanalysis
The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.
Maintained by Istem Fer. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
216 stars 9.96 score 20 scripts 2 dependentspecanproject
PEcAn.priors:PEcAn Functions Used to Estimate Priors from Data
Functions to estimate priors from data.
Maintained by David LeBauer. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
216 stars 9.95 score 13 scripts 6 dependentspecanproject
PEcAn.MA:PEcAn Functions Used for Meta-Analysis
The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation. The PEcAn.MA package contains the functions used in the Bayesian meta-analysis of trait data.
Maintained by David LeBauer. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
216 stars 9.90 score 7 scripts 7 dependents
martynplummer
rjags:Bayesian Graphical Models using MCMC
Interface to the JAGS MCMC library.
Maintained by Martyn Plummer. Last updated 5 days ago.
7 stars 9.88 score 4.0k scripts 165 dependentspecanproject
PEcAnRTM:PEcAn Functions Used for Radiative Transfer Modeling
Functions for performing forward runs and inversions of radiative transfer models (RTMs). Inversions can be performed using maximum likelihood, or more complex hierarchical Bayesian methods. Underlying numerical analyses are optimized for speed using Fortran code.
Maintained by Alexey Shiklomanov. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsfortranjagscpp
216 stars 9.70 score 132 scriptspecanproject
PEcAn.data.land:PEcAn Functions Used for Ecological Forecasts and Reanalysis
The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.
Maintained by Mike Dietze. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
216 stars 9.33 score 19 scripts 10 dependents
brianstock
MixSIAR:Bayesian Mixing Models in R
Creates and runs Bayesian mixing models to analyze biological tracer data (i.e. stable isotopes, fatty acids), which estimate the proportions of source (prey) contributions to a mixture (consumer). 'MixSIAR' is not one model, but a framework that allows a user to create a mixing model based on their data structure and research questions, via options for fixed/ random effects, source data types, priors, and error terms. 'MixSIAR' incorporates several years of advances since 'MixSIR' and 'SIAR'.
Maintained by Brian Stock. Last updated 4 years ago.
98 stars 9.27 score 122 scripts
andrewljackson
SIBER:Stable Isotope Bayesian Ellipses in R
Fits bi-variate ellipses to stable isotope data using Bayesian inference with the aim being to describe and compare their isotopic niche.
Maintained by Andrew Jackson. Last updated 10 months ago.
community-ecologyecologyniche-modellingstable-isotopesjagscpp
37 stars 9.15 score 187 scripts 1 dependentspecanproject
PEcAn.all:PEcAn Functions Used for Ecological Forecasts and Reanalysis
The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PEcAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.
Maintained by David LeBauer. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
216 stars 9.00 score 266 scriptspecanproject
PEcAn.BIOCRO:PEcAn Package for Integration of the BioCro Model
This module provides functions to link BioCro to PEcAn.
Maintained by David LeBauer. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
216 stars 8.94 score 23 scriptspecanproject
PEcAn.uncertainty:PEcAn Functions Used for Propagating and Partitioning Uncertainties in Ecological Forecasts and Reanalysis
The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.
Maintained by David LeBauer. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
216 stars 8.93 score 15 scripts 5 dependentspecanproject
PEcAn.photosynthesis:PEcAn functions used for leaf-level photosynthesis calculations
The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation. The PEcAn.photosynthesis package contains functions used in the Hierarchical Bayesian calibration of the Farquhar et al 1980 model.
Maintained by Mike Dietze. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
216 stars 8.86 score 19 scriptspecanproject
PEcAn.workflow:PEcAn Functions Used for Ecological Forecasts and Reanalysis
The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PEcAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation. This package provides workhorse functions that can be used to run the major steps of a PEcAn analysis.
Maintained by David LeBauer. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
216 stars 8.83 score 15 scripts 4 dependentspecanproject
PEcAn.ED2:PEcAn Package for Integration of ED2 Model
The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation. This package provides functions to link the Ecosystem Demography Model, version 2, to PEcAn.
Maintained by Mike Dietze. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
216 stars 8.74 score 145 scripts
csafe-isu
handwriter:Handwriting Analysis in R
Perform statistical writership analysis of scanned handwritten documents. Webpage provided at: <https://github.com/CSAFE-ISU/handwriter>.
Maintained by Stephanie Reinders. Last updated 2 months ago.
24 stars 8.63 score 27 scripts 2 dependentspecanproject
PEcAn.SIPNET:PEcAn Functions Used for Ecological Forecasts and Reanalysis
The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.
Maintained by Mike Dietze. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
216 stars 8.36 score 61 scriptspecanproject
PEcAn.LINKAGES:PEcAn Package for Integration of the LINKAGES Model
This module provides functions to link the (LINKAGES) to PEcAn.
Maintained by Ann Raiho. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
216 stars 8.35 score 59 scriptspecanproject
PEcAnAssimSequential:PEcAn Functions Used for Ecological Forecasts and Reanalysis
The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.
Maintained by Mike Dietze. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
216 stars 8.12 score 35 scripts
openpharma
crmPack:Object-Oriented Implementation of CRM Designs
Implements a wide range of model-based dose escalation designs, ranging from classical and modern continual reassessment methods (CRMs) based on dose-limiting toxicity endpoints to dual-endpoint designs taking into account a biomarker/efficacy outcome. The focus is on Bayesian inference, making it very easy to setup a new design with its own JAGS code. However, it is also possible to implement 3+3 designs for comparison or models with non-Bayesian estimation. The whole package is written in a modular form in the S4 class system, making it very flexible for adaptation to new models, escalation or stopping rules. Further details are presented in Sabanes Bove et al. (2019) <doi:10.18637/jss.v089.i10>.
Maintained by Daniel Sabanes Bove. Last updated 2 months ago.
21 stars 7.76 score 208 scripts
andrewcparnell
simmr:A Stable Isotope Mixing Model
Fits Stable Isotope Mixing Models (SIMMs) and is meant as a longer term replacement to the previous widely-used package SIAR. SIMMs are used to infer dietary proportions of organisms consuming various food sources from observations on the stable isotope values taken from the organisms' tissue samples. However SIMMs can also be used in other scenarios, such as in sediment mixing or the composition of fatty acids. The main functions are simmr_load() and simmr_mcmc(). The two vignettes contain a quick start and a full listing of all the features. The methods used are detailed in the papers Parnell et al 2010 <doi:10.1371/journal.pone.0009672>, and Parnell et al 2013 <doi:10.1002/env.2221>.
Maintained by Emma Govan. Last updated 12 months ago.
32 stars 7.59 score 81 scriptspecanproject
PEcAn.LDNDC:PEcAn package for integration of the LDNDC model
This module provides functions to link the (LDNDC) to PEcAn.
Maintained by Henri Kajasilta. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
216 stars 7.58 score
gertvv
gemtc:Network Meta-Analysis Using Bayesian Methods
Network meta-analyses (mixed treatment comparisons) in the Bayesian framework using JAGS. Includes methods to assess heterogeneity and inconsistency, and a number of standard visualizations.
Maintained by Gert van Valkenhoef. Last updated 5 years ago.
45 stars 7.49 score 71 scripts 1 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.
bayesiangeneralized-linear-modelsglmglmmimputationimputationsjagsjoint-analysislinear-mixed-modelslinear-regression-modelsmcmc-samplemcmc-samplingmissing-datamissing-valuessurvivalcpp
28 stars 7.30 score 59 scripts 1 dependents
drizopoulos
JMbayes:Joint Modeling of Longitudinal and Time-to-Event Data under a Bayesian Approach
Shared parameter models for the joint modeling of longitudinal and time-to-event data using MCMC; Dimitris Rizopoulos (2016) <doi:10.18637/jss.v072.i07>.
Maintained by Dimitris Rizopoulos. Last updated 4 years ago.
joint-modelslongitudinal-responsesprediction-modelsurvival-analysisopenblascppopenmpjags
59 stars 6.97 score 80 scripts
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 4 months ago.
bayesianhigh-performance-computingjagsmaker-targetopiareproducibilityrjagsstatisticstargetscpp
10 stars 6.95 score 32 scripts
datacloning
dclone:Data Cloning and MCMC Tools for Maximum Likelihood Methods
Low level functions for implementing maximum likelihood estimating procedures for complex models using data cloning and Bayesian Markov chain Monte Carlo methods as described in Solymos 2010 <doi:10.32614/RJ-2010-011>. Sequential and parallel MCMC support for 'JAGS', 'WinBUGS', 'OpenBUGS', and 'Stan'.
Maintained by Peter Solymos. Last updated 6 months ago.
7 stars 6.91 score 215 scripts 4 dependents
fbartos
RoBMA:Robust Bayesian Meta-Analyses
A framework for estimating ensembles of meta-analytic and meta-regression models (assuming either presence or absence of the effect, heterogeneity, publication bias, and moderators). The RoBMA framework uses Bayesian model-averaging to combine the competing meta-analytic models into a model ensemble, weights the posterior parameter distributions based on posterior model probabilities and uses Bayes factors to test for the presence or absence of the individual components (e.g., effect vs. no effect; Bartoš et al., 2022, <doi:10.1002/jrsm.1594>; Maier, Bartoš & Wagenmakers, 2022, <doi:10.1037/met0000405>). Users can define a wide range of prior distributions for + the effect size, heterogeneity, publication bias (including selection models and PET-PEESE), and moderator components. The package provides convenient functions for summary, visualizations, and fit diagnostics.
Maintained by František Bartoš. Last updated 2 months ago.
meta-analysismodel-averagingpublication-biasjagsopenblascpp
9 stars 6.88 score 53 scripts
furrer-lab
abn:Modelling Multivariate Data with Additive Bayesian Networks
The 'abn' R package facilitates Bayesian network analysis, a probabilistic graphical model that derives from empirical data a directed acyclic graph (DAG). This DAG describes the dependency structure between random variables. The R package 'abn' provides routines to help determine optimal Bayesian network models for a given data set. These models are used to identify statistical dependencies in messy, complex data. Their additive formulation is equivalent to multivariate generalised linear modelling, including mixed models with independent and identically distributed (iid) random effects. The core functionality of the 'abn' package revolves around model selection, also known as structure discovery. It supports both exact and heuristic structure learning algorithms and does not restrict the data distribution of parent-child combinations, providing flexibility in model creation and analysis. The 'abn' package uses Laplace approximations for metric estimation and includes wrappers to the 'INLA' package. It also employs 'JAGS' for data simulation purposes. For more resources and information, visit the 'abn' website.
Maintained by Matteo Delucchi. Last updated 18 days ago.
bayesian-networkbinomialcategorical-datagaussiangrouped-datasetsmixed-effectsmultinomialmultivariatepoissonstructure-learninggslopenblascppopenmpjags
6 stars 6.88 score 90 scripts
lindeloev
mcp:Regression with Multiple Change Points
Flexible and informed regression with Multiple Change Points. 'mcp' can infer change points in means, variances, autocorrelation structure, and any combination of these, as well as the parameters of the segments in between. All parameters are estimated with uncertainty and prediction intervals are supported - also near the change points. 'mcp' supports hypothesis testing via Savage-Dickey density ratio, posterior contrasts, and cross-validation. 'mcp' is described in Lindeløv (submitted) <doi:10.31219/osf.io/fzqxv> and generalizes the approach described in Carlin, Gelfand, & Smith (1992) <doi:10.2307/2347570> and Stephens (1994) <doi:10.2307/2986119>.
Maintained by Jonas Kristoffer Lindeløv. Last updated 6 months ago.
108 stars 6.74 score 85 scripts 1 dependents
pavlakrotka
NCC:Simulation and Analysis of Platform Trials with Non-Concurrent Controls
Design and analysis of flexible platform trials with non-concurrent controls. Functions for data generation, analysis, visualization and running simulation studies are provided. The implemented analysis methods are described in: Bofill Roig et al. (2022) <doi:10.1186/s12874-022-01683-w>, Saville et al. (2022) <doi:10.1177/17407745221112013> and Schmidli et al. (2014) <doi:10.1111/biom.12242>.
Maintained by Pavla Krotka. Last updated 19 days ago.
clinical-trialsplatform-trialssimulationstatistical-inferencejagscpp
5 stars 6.64 score 29 scripts
swfsc
swfscDAS:Processing DAS Data Files
Process and summarize DAS data files. These files are typically, but do not have to be DAS <https://swfsc-publications.fisheries.noaa.gov/publications/TM/SWFSC/NOAA-TM-NMFS-SWFSC-305.PDF> data produced by the Southwest Fisheries Science Center (SWFSC) program 'WinCruz'. This package standardizes and streamlines basic DAS data processing, and includes a PDF with the DAS data format requirements expected by the package.
Maintained by Sam Woodman. Last updated 15 days ago.
4 stars 6.64 score 24 scripts 3 dependents
thomasasmith
CRTspat:Workflow for Cluster Randomised Trials with Spillover
Design, workflow and statistical analysis of Cluster Randomised Trials of (health) interventions where there may be spillover between the arms (see <https://thomasasmith.github.io/index.html>).
Maintained by Thomas Smith. Last updated 11 days ago.
4 stars 6.57 score 24 scripts
benjaminhlina
nichetools:Complementary Package to 'nicheROVER' and 'SIBER'
Provides functions complementary to packages 'nicheROVER' and 'SIBER' allowing the user to extract Bayesian estimates from data objects created by the packages 'nicheROVER' and 'SIBER'. Please see the following publications for detailed methods on 'nicheROVER' and 'SIBER' Hansen et al. (2015) <doi:10.1890/14-0235.1>, Jackson et al. (2011) <do i:10.1111/j.1365-2656.2011.01806.x>, and Layman et al. (2007) <doi:10.1890/0012-9658(2007)88[42:CSIRPF]2.0.CO;2>, respectfully.
Maintained by Benjamin L. Hlina. Last updated 9 days ago.
2 stars 6.39 score 17 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.
archaeometrybayesian-statisticsgeochronologyluminescence-datingradiocarbon-datesjagscpp
9 stars 6.22 score 37 scriptscsafe-isu
handwriterRF:Handwriting Analysis with Random Forests
Perform forensic handwriting analysis of two scanned handwritten documents. This package implements the statistical method described by Madeline Johnson and Danica Ommen (2021) <doi:10.1002/sam.11566>. Similarity measures and a random forest produce a score-based likelihood ratio that quantifies the strength of the evidence in favor of the documents being written by the same writer or different writers.
Maintained by Stephanie Reinders. Last updated 2 days ago.
2 stars 6.21 score 15 scripts 1 dependents
zhenkewu
baker:"Nested Partially Latent Class Models"
Provides functions to specify, fit and visualize nested partially-latent class models ( Wu, Deloria-Knoll, Hammitt, and Zeger (2016) <doi:10.1111/rssc.12101>; Wu, Deloria-Knoll, and Zeger (2017) <doi:10.1093/biostatistics/kxw037>; Wu and Chen (2021) <doi:10.1002/sim.8804>) for inference of population disease etiology and individual diagnosis. In the motivating Pneumonia Etiology Research for Child Health (PERCH) study, because both quantities of interest sum to one hundred percent, the PERCH scientists frequently refer to them as population etiology pie and individual etiology pie, hence the name of the package.
Maintained by Zhenke Wu. Last updated 11 months ago.
bayesiancase-controllatent-class-analysisjagscpp
8 stars 6.00 score 21 scripts
leoegidi
pivmet:Pivotal Methods for Bayesian Relabelling and k-Means Clustering
Collection of pivotal algorithms for: relabelling the MCMC chains in order to undo the label switching problem in Bayesian mixture models; fitting sparse finite mixtures; initializing the centers of the classical k-means algorithm in order to obtain a better clustering solution. For further details see Egidi, Pappadà, Pauli and Torelli (2018b)<ISBN:9788891910233>.
Maintained by Leonardo Egidi. Last updated 10 months ago.
5 stars 5.94 score 25 scripts
oeysan
bfw:Bayesian Framework for Computational Modeling
Derived from the work of Kruschke (2015, <ISBN:9780124058880>), the present package aims to provide a framework for conducting Bayesian analysis using Markov chain Monte Carlo (MCMC) sampling utilizing the Just Another Gibbs Sampler ('JAGS', Plummer, 2003, <https://mcmc-jags.sourceforge.io>). The initial version includes several modules for conducting Bayesian equivalents of chi-squared tests, analysis of variance (ANOVA), multiple (hierarchical) regression, softmax regression, and for fitting data (e.g., structural equation modeling).
Maintained by Øystein Olav Skaar. Last updated 3 years ago.
bayesian-data-analysisbayesian-statisticsjagsmcmcpsychological-sciencecpp
10 stars 5.89 score 31 scripts
mbtyers
jagshelper:Extracting and Visualizing Output from 'jagsUI'
Tools are provided to streamline Bayesian analyses in 'JAGS' using the 'jagsUI' package. Included are functions for extracting output in simpler format, functions for streamlining assessment of convergence, and functions for producing summary plots of output. Also included is a function that provides a simple template for running 'JAGS' from 'R'. Referenced materials can be found at <DOI:10.1214/ss/1177011136>.
Maintained by Matt Tyers. Last updated 5 months ago.
5.74 score 91 scripts
cschwarz-stat-sfu-ca
BTSPAS:Bayesian Time-Stratified Population Analysis
Provides advanced Bayesian methods to estimate abundance and run-timing from temporally-stratified Petersen mark-recapture experiments. Methods include hierarchical modelling of the capture probabilities and spline smoothing of the daily run size. Theory described in Bonner and Schwarz (2011) <doi:10.1111/j.1541-0420.2011.01599.x>.
Maintained by Carl J Schwarz. Last updated 5 months ago.
1 stars 5.70 score 28 scripts 1 dependents
georgiosseitidis
ssifs:Stochastic Search Inconsistency Factor Selection
Evaluating the consistency assumption of Network Meta-Analysis both globally and locally in the Bayesian framework. Inconsistencies are located by applying Bayesian variable selection to the inconsistency factors. The implementation of the method is described by Seitidis et al. (2022) <arXiv:2211.07258>.
Maintained by Georgios Seitidis. Last updated 5 days ago.
consistencymetaanalysisnetworknmassifsssvsvariable-selectionjagscpp
2 stars 5.66 score 4 scriptsbioc
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.
softwaresomaticmutationsequencingstatisticalmethodbayesianmutational-signaturesrjagssomatic-mutationscppjags
3 stars 5.56 score 7 scripts 1 dependentsswfsc
rfPermute:Estimate Permutation p-Values for Random Forest Importance Metrics
Estimate significance of importance metrics for a Random Forest model by permuting the response variable. Produces null distribution of importance metrics for each predictor variable and p-value of observed. Provides summary and visualization functions for 'randomForest' results.
Maintained by Eric Archer. Last updated 4 days ago.
27 stars 5.44 score
ianjonsen
bsam:Bayesian State-Space Models for Animal Movement
Tools to fit Bayesian state-space models to animal tracking data. Models are provided for location filtering, location filtering and behavioural state estimation, and their hierarchical versions. The models are primarily intended for fitting to ARGOS satellite tracking data but options exist to fit to other tracking data types. For Global Positioning System data, consider the 'moveHMM' package. Simplified Markov Chain Monte Carlo convergence diagnostic plotting is provided but users are encouraged to explore tools available in packages such as 'coda' and 'boa'.
Maintained by Ian Jonsen. Last updated 9 months ago.
17 stars 5.42 score 31 scriptsbioc
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.
softwarestatisticalmethodbayesiansequencingcoveragejagscpp
10 stars 5.38 score 12 scripts
angabrio
missingHE:Missing Outcome Data in Health Economic Evaluation
Contains a suite of functions for health economic evaluations with missing outcome data. The package can fit different types of statistical models under a fully Bayesian approach using the software 'JAGS' (which should be installed locally and which is loaded in 'missingHE' via the 'R' package 'R2jags'). Three classes of models can be fitted under a variety of missing data assumptions: selection models, pattern mixture models and hurdle models. In addition to model fitting, 'missingHE' provides a set of specialised functions to assess model convergence and fit, and to summarise the statistical and economic results using different types of measures and graphs. The methods implemented are described in Mason (2018) <doi:10.1002/hec.3793>, Molenberghs (2000) <doi:10.1007/978-1-4419-0300-6_18> and Gabrio (2019) <doi:10.1002/sim.8045>.
Maintained by Andrea Gabrio. Last updated 2 years ago.
cost-effectiveness-analysishealth-economic-evaluationindividual-level-datajagsmissing-dataparametric-modellingsensitivity-analysiscpp
5 stars 5.38 score 24 scripts
pyhernvann
EcoDiet:Estimating a Diet Matrix from Biotracer and Stomach Content Data
Biotracers and stomach content analyses are combined in a Bayesian hierarchical model to estimate a probabilistic topology matrix (all trophic link probabilities) and a diet matrix (all diet proportions). The package relies on the JAGS software and the 'jagsUI' package to run a Markov chain Monte Carlo approximation of the different variables.
Maintained by Pierre-Yves Hernvann. Last updated 1 years ago.
4 stars 5.33 score 12 scripts
fukayak
occumb:Site Occupancy Modeling for Environmental DNA Metabarcoding
Fits multispecies site occupancy models to environmental DNA metabarcoding data collected using spatially-replicated survey design. Model fitting results can be used to evaluate and compare the effectiveness of species detection to find an efficient survey design. Reference: Fukaya et al. (2022) <doi:10.1111/2041-210X.13732>.
Maintained by Keiichi Fukaya. Last updated 2 months ago.
2 stars 5.30 score 10 scripts
rich-payne
dreamer:Dose Response Models for Bayesian Model Averaging
Fits dose-response models utilizing a Bayesian model averaging approach as outlined in Gould (2019) <doi:10.1002/bimj.201700211> for both continuous and binary responses. Longitudinal dose-response modeling is also supported in a Bayesian model averaging framework as outlined in Payne, Ray, and Thomann (2024) <doi:10.1080/10543406.2023.2292214>. Functions for plotting and calculating various posterior quantities (e.g. posterior mean, quantiles, probability of minimum efficacious dose, etc.) are also implemented. Copyright Eli Lilly and Company (2019).
Maintained by Richard Daniel Payne. Last updated 3 months ago.
bayesiandose-response-modelingjagscpp
9 stars 5.26 score 5 scripts
sarahhbellum
NobBS:Nowcasting by Bayesian Smoothing
A Bayesian approach to estimate the number of occurred-but-not-yet-reported cases from incomplete, time-stamped reporting data for disease outbreaks. 'NobBS' learns the reporting delay distribution and the time evolution of the epidemic curve to produce smoothed nowcasts in both stable and time-varying case reporting settings, as described in McGough et al. (2020) <doi:10.1371/journal.pcbi.1007735>.
Maintained by Sarah McGough. Last updated 1 years ago.
18 stars 5.06 score 32 scripts
kimberlywebb
COMBO:Correcting Misclassified Binary Outcomes in Association Studies
Use frequentist and Bayesian methods to estimate parameters from a binary outcome misclassification model. These methods correct for the problem of "label switching" by assuming that the sum of outcome sensitivity and specificity is at least 1. A description of the analysis methods is available in Hochstedler and Wells (2023) <doi:10.48550/arXiv.2303.10215>.
Maintained by Kimberly Hochstedler Webb. Last updated 1 months ago.
1 stars 5.04 score 4 scripts
emmagovan
cosimmr:Fast Fitting of Stable Isotope Mixing Models with Covariates
Fast fitting of Stable Isotope Mixing Models in R. Allows for the inclusion of covariates. Also has built-in summary functions and plot functions which allow for the creation of isospace plots. Variational Bayes is used to fit these models, methods as described in: Tran et al., (2021) <doi:10.48550/arXiv.2103.01327>.
Maintained by Emma Govan. Last updated 7 months ago.
1 stars 5.00 score 7 scripts
poissonconsulting
jmbr:Analyses Using JAGS
Facilitates analyses using 'Just Another Gibbs Sampler'.
Maintained by Joe Thorley. Last updated 1 months ago.
3 stars 4.98 score 5 scriptsbioc
CNVrd2:CNVrd2: a read depth-based method to detect and genotype complex common copy number variants from next generation sequencing data.
CNVrd2 uses next-generation sequencing data to measure human gene copy number for multiple samples, indentify SNPs tagging copy number variants and detect copy number polymorphic genomic regions.
Maintained by Hoang Tan Nguyen. Last updated 5 months ago.
copynumbervariationsnpsequencingsoftwarecoveragelinkagedisequilibriumclustering.jagscpp
3 stars 4.92 score
yenyiho-lab
scDECO:Estimating Dynamic Correlation
Implementations for two different Bayesian models of differential co-expression. scdeco.cop() fits the bivariate Gaussian copula model from Zichen Ma, Shannon W. Davis, Yen-Yi Ho (2023) <doi:10.1111/biom.13701>, while scdeco.pg() fits the bivariate Poisson-Gamma model from Zhen Yang, Yen-Yi Ho (2022) <doi:10.1111/biom.13457>.
Maintained by Anderson Bussing. Last updated 10 months ago.
4.78 score
fawda123
EBASE:Estuarine Bayesian Single-Station Estimation Method for Ecosystem Metabolism
Estimate ecosystem metabolism in a Bayesian framework for individual water quality monitoring stations with continuous dissolved oxygen time series. A mass balance equation is used that provides estimates of parameters for gross primary production, respiration, and gas exchange. Methods adapted from Grace et al. (2015) <doi:10.1002/lom3.10011> and Wanninkhof (2014) <doi:10.4319/lom.2014.12.351>. Details in Beck et al. (2024) <doi:10.1002/lom3.10620>.
Maintained by Marcus Beck. Last updated 6 months ago.
4.73 score 18 scripts
datacloning
dcmle:Hierarchical Models Made Easy with Data Cloning
S4 classes around infrastructure provided by the 'coda' and 'dclone' packages to make package development easy as a breeze with data cloning for hierarchical models.
Maintained by Peter Solymos. Last updated 6 months ago.
4.60 score 66 scripts 2 dependents
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.
7 stars 4.54 score 7 scripts
brechtdv
prevalence:Tools for Prevalence Assessment Studies
The prevalence package provides Frequentist and Bayesian methods for prevalence assessment studies. IMPORTANT: the truePrev functions in the prevalence package call on JAGS (Just Another Gibbs Sampler), which therefore has to be available on the user's system. JAGS can be downloaded from <https://mcmc-jags.sourceforge.io/>.
Maintained by Brecht Devleesschauwer. Last updated 3 years ago.
2 stars 4.48 score 38 scriptscschwarz-stat-sfu-ca
Petersen:Estimators for Two-Sample Capture-Recapture Studies
A comprehensive implementation of Petersen-type estimators and its many variants for two-sample capture-recapture studies. A conditional likelihood approach is used that allows for tag loss; non reporting of tags; reward tags; categorical, geographical and temporal stratification; partial stratification; reverse capture-recapture; and continuous variables in modeling the probability of capture. Many examples from fisheries management are presented.
Maintained by Carl Schwarz. Last updated 1 months ago.
1 stars 4.48 score 12 scripts
xsswang
remiod:Reference-Based Multiple Imputation for Ordinal/Binary Response
Reference-based multiple imputation of ordinal and binary responses under Bayesian framework, as described in Wang and Liu (2022) <arXiv:2203.02771>. Methods for missing-not-at-random include Jump-to-Reference (J2R), Copy Reference (CR), and Delta Adjustment which can generate tipping point analysis.
Maintained by Tony Wang. Last updated 2 years ago.
bayesiancontrol-basedcopy-referencedelta-adjustmentgeneralized-linear-modelsglmjagsjump-to-referencemcmcmissing-at-randommissing-datamissing-not-at-randommultiple-imputationnon-ignorableordinal-regressionpattern-mixture-modelreference-basedstatisticscpp
4.30 score 3 scripts
frbcesab
popbayes:Bayesian Model to Estimate Population Trends from Counts Series
Infers the trends of one or several animal populations over time from series of counts. It does so by accounting for count precision (provided or inferred based on expert knowledge, e.g. guesstimates), smoothing the population rate of increase over time, and accounting for the maximum demographic potential of species. Inference is carried out in a Bayesian framework. This work is part of the FRB-CESAB working group AfroBioDrivers <https://www.fondationbiodiversite.fr/en/the-frb-in-action/programs-and-projects/le-cesab/afrobiodrivers/>.
Maintained by Nicolas Casajus. Last updated 1 years ago.
animalbayesiancountspopulationprecisiontemporal-trendjagscpp
1 stars 4.30 score
htx-r
crossnma:Cross-Design & Cross-Format Network Meta-Analysis and Regression
Network meta-analysis and meta-regression (allows including up to three covariates) for individual participant data, aggregate data, and mixtures of both formats using the three-level hierarchical model. Each format can come from randomized controlled trials or non-randomized studies or mixtures of both. Estimates are generated in a Bayesian framework using JAGS. The implemented models are described by Hamza et al. 2023 <DOI:10.1002/jrsm.1619>.
Maintained by Guido Schwarzer. Last updated 4 months ago.
1 stars 4.29 score 13 scriptsmikejseo
bipd:Bayesian Individual Patient Data Meta-Analysis using 'JAGS'
We use a Bayesian approach to run individual patient data meta-analysis and network meta-analysis using 'JAGS'. The methods incorporate shrinkage methods and calculate patient-specific treatment effects as described in Seo et al. (2021) <DOI:10.1002/sim.8859>. This package also includes user-friendly functions that impute missing data in an individual patient data using mice-related packages.
Maintained by Michael Seo. Last updated 3 years ago.
3 stars 4.26 score 20 scriptsbioc
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.
softwaresomaticmutationsequencingstatisticalmethodclusteringmutational-signaturesrjagssomatic-mutationscppjags
3 stars 4.08 score 1 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.
4.00 score 6 scripts
mirkoth
BayesRS:Bayes Factors for Hierarchical Linear Models with Continuous Predictors
Runs hierarchical linear Bayesian models. Samples from the posterior distributions of model parameters in JAGS (Just Another Gibbs Sampler; Plummer, 2003, <doi:10.1.1.13.3406>). Computes Bayes factors for group parameters of interest with the Savage-Dickey density ratio (Wetzels, Raaijmakers, Jakab, Wagenmakers, 2009, <doi:10.3758/PBR.16.4.752>).
Maintained by Mirko Thalmann. Last updated 7 years ago.
3.95 score 6 scripts
sidiwang
snSMART:Small N Sequential Multiple Assignment Randomized Trial Methods
Consolidated data simulation, sample size calculation and analysis functions for several snSMART (small sample sequential, multiple assignment, randomized trial) designs under one library. See Wei, B., Braun, T.M., Tamura, R.N. and Kidwell, K.M. "A Bayesian analysis of small n sequential multiple assignment randomized trials (snSMARTs)." (2018) Statistics in medicine, 37(26), pp.3723-3732 <doi:10.1002/sim.7900>.
Maintained by Michael Kleinsasser. Last updated 6 months ago.
bayesian-analysisclinical-trialsrare-diseasesmall-samplejagscpp
1 stars 3.90 score 3 scriptsrich-payne
beaver:Bayesian Model Averaging of Covariate Adjusted Negative-Binomial Dose-Response
Dose-response modeling for negative-binomial distributed data with a variety of dose-response models. Covariate adjustment and Bayesian model averaging is supported. Functions are provided to easily obtain inference on the dose-response relationship and plot the dose-response curve.
Maintained by Hollins Showalter. Last updated 10 months ago.
1 stars 3.89 score 78 scriptsswfsc
CruzPlot:Plot Shipboard DAS Data
A utility program oriented to create maps, plot data, and do basic data summaries of DAS data files. These files are typically, but do not have to be DAS <https://swfsc-publications.fisheries.noaa.gov/publications/TM/SWFSC/NOAA-TM-NMFS-SWFSC-305.PDF> data produced by the Southwest Fisheries Science Center (SWFSC) program 'WinCruz'.
Maintained by Sam Woodman. Last updated 6 months ago.
2 stars 3.85 score 2 scriptscsafe-isu
handwriterApp:A 'shiny' Application for Handwriting Analysis
Perform statistical writership analysis of scanned handwritten documents with a 'shiny' app for 'handwriter'.
Maintained by Stephanie Reinders. Last updated 4 months ago.
1 stars 3.85 score 9 scripts
kelliejarcher
ordinalbayes:Bayesian Ordinal Regression for High-Dimensional Data
Provides a function for fitting various penalized Bayesian cumulative link ordinal response models when the number of parameters exceeds the sample size. These models have been described in Zhang and Archer (2021) <doi:10.1186/s12859-021-04432-w>.
Maintained by Kellie J. Archer. Last updated 3 years ago.
1 stars 3.70 score 1 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.
1 stars 3.70 score 3 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.
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.
3.70 score 4 scripts
malfly
JAGStree:Automatically Write 'JAGS' Code for Hierarchical Bayesian Models on Trees
When relationships between sources of data can be represented by a tree, the generation of appropriate Markov Chain Monte Carlo modeling code to be used with 'JAGS' to run a Bayesian hierarchical model can be automatically generated by this package. Any admissible tree-structured data can be used, under the assumption that node counts are multinomial and branching probabilities are Dirichlet among sibling groups. The methodological basis used to create this package can be found in Flynn (2023) <http://hdl.handle.net/2429/86174>.
Maintained by Mallory J Flynn. Last updated 5 months ago.
3.70 scoreswfsc
swfscAirDAS:Southwest Fisheries Science Center Aerial DAS Data Processing
Process and summarize aerial survey 'DAS' data (AirDAS) <https://swfsc-publications.fisheries.noaa.gov/publications/TM/SWFSC/NOAA-TM-NMFS-SWFSC-185.PDF> collected using an aerial survey program from the Southwest Fisheries Science Center (SWFSC) <https://www.fisheries.noaa.gov/west-coast/science-data/california-current-marine-mammal-assessment-program>. PDF files detailing the relevant AirDAS data formats are included in this package.
Maintained by Sam Woodman. Last updated 6 months ago.
3.70 score 7 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.
archaeologybayesian-statisticsgeochronologymarkov-chainradiocarbon-datesjagscpp
3 stars 3.65 score 15 scriptsswfsc
banter:BioAcoustic eveNT classifiER
Create a hierarchical acoustic event species classifier out of multiple call type detectors as described in Rankin et al (2017) <doi:10.1111/mms.12381>.
Maintained by Eric Archer. Last updated 1 years ago.
acousticsbioacousticscetaceansclassificationdolphinsmachine-learningnoaarandom-forestspecies-identificationsupervised-learningsupervised-machine-learningwhalesjagscpp
9 stars 3.65 scorebioc
MADSEQ:Mosaic Aneuploidy Detection and Quantification using Massive Parallel Sequencing Data
The MADSEQ package provides a group of hierarchical Bayeisan models for the detection of mosaic aneuploidy, the inference of the type of aneuploidy and also for the quantification of the fraction of aneuploid cells in the sample.
Maintained by Yu Kong. Last updated 5 months ago.
genomicvariationsomaticmutationvariantdetectionbayesiancopynumbervariationsequencingcoveragejagscpp
4 stars 3.60 score 1 scripts
germaine86
eefAnalytics:Robust Analytical Methods for Evaluating Educational Interventions using Randomised Controlled Trials Designs
Analysing data from evaluations of educational interventions using a randomised controlled trial design. Various analytical tools to perform sensitivity analysis using different methods are supported (e.g. frequentist models with bootstrapping and permutations options, Bayesian models). The included commands can be used for simple randomised trials, cluster randomised trials and multisite trials. The methods can also be used more widely beyond education trials. This package can be used to evaluate other intervention designs using Frequentist and Bayesian multilevel models.
Maintained by Germaine Uwimpuhwe. Last updated 6 months ago.
3.58 score 19 scripts
donaldrwilliams
vICC:Varying Intraclass Correlation Coefficients
Compute group-specific intraclass correlation coefficients, Bayesian testing of homogenous within-group variance, and spike-and-slab model selection to determine which groups share a common within-group variance in a one-way random effects model <10.31234/osf.io/hpq7w>.
Maintained by Donald Williams. Last updated 4 years ago.
7 stars 3.54 score 3 scriptsfbartos
RoBSA:Robust Bayesian Survival Analysis
A framework for estimating ensembles of parametric survival models with different parametric families. The RoBSA framework uses Bayesian model-averaging to combine the competing parametric survival models into a model ensemble, weights the posterior parameter distributions based on posterior model probabilities and uses Bayes factors to test for the presence or absence of the individual predictors or preference for a parametric family (Bartoš, Aust & Haaf, 2022, <doi:10.1186/s12874-022-01676-9>). The user can define a wide range of informative priors for all parameters of interest. The package provides convenient functions for summary, visualizations, fit diagnostics, and prior distribution calibration.
Maintained by František Bartoš. Last updated 5 days ago.
bayesianmodel-averagingsurvival-analysisjagscpp
7 stars 3.54 score 1 scripts
fhui28
boral:Bayesian Ordination and Regression AnaLysis
Bayesian approaches for analyzing multivariate data in ecology. Estimation is performed using Markov Chain Monte Carlo (MCMC) methods via Three. JAGS types of models may be fitted: 1) With explanatory variables only, boral fits independent column Generalized Linear Models (GLMs) to each column of the response matrix; 2) With latent variables only, boral fits a purely latent variable model for model-based unconstrained ordination; 3) With explanatory and latent variables, boral fits correlated column GLMs with latent variables to account for any residual correlation between the columns of the response matrix.
Maintained by Francis K.C. Hui. Last updated 1 years ago.
2 stars 3.45 score 79 scripts
psolymos
PVAClone:Population Viability Analysis with Data Cloning
Likelihood based population viability analysis in the presence of observation error and missing data. The package can be used to fit, compare, predict, and forecast various growth model types using data cloning.
Maintained by Peter Solymos. Last updated 6 months ago.
1 stars 3.32 score 21 scripts
dnzmarcio
ewoc:Escalation with Overdose Control
An implementation of a variety of escalation with overdose control designs introduced by Babb, Rogatko and Zacks (1998) <doi:10.1002/(SICI)1097-0258(19980530)17:10%3C1103::AID-SIM793%3E3.0.CO;2-9>. It calculates the next dose as a clinical trial proceeds and performs simulations to obtain operating characteristics.
Maintained by Marcio A. Diniz. Last updated 4 years ago.
2 stars 3.30 score 20 scripts
vdinizm
pexm:Loading a JAGS Module for the Piecewise Exponential Distribution
Load the Just Another Gibbs Sampling (JAGS) module 'pexm'. The module provides the tools to work with the Piecewise Exponential (PE) distribution in a Bayesian model with the corresponding Markov Chain Monte Carlo algorithm (Gibbs Sampling) implemented via JAGS. Details about the module implementation can be found in Mayrink et al. (2021) <doi:10.18637/jss.v100.i08>.
Maintained by Vinicius Mayrink. Last updated 1 years ago.
4 stars 3.30 score 1 scripts
lbau7
basksim:Simulation-Based Calculation of Basket Trial Operating Characteristics
Provides a unified syntax for the simulation-based comparison of different single-stage basket trial designs with a binary endpoint and equal sample sizes in all baskets. Methods include the designs by Baumann et al. (2024) <doi:10.48550/arXiv.2309.06988>, Fujikawa et al. (2020) <doi:10.1002/bimj.201800404>, Berry et al. (2020) <doi:10.1177/1740774513497539>, Neuenschwander et al. (2016) <doi:10.1002/pst.1730> and Psioda et al. (2021) <doi:10.1093/biostatistics/kxz014>. For the latter three designs, the functions are mostly wrappers for functions provided by the packages 'bhmbasket' and 'bmabasket'.
Maintained by Lukas Baumann. Last updated 12 months ago.
1 stars 3.28 score 19 scripts
virgile-baudrot
morse:Modelling Reproduction and Survival Data in Ecotoxicology
Advanced methods for a valuable quantitative environmental risk assessment using Bayesian inference of survival and reproduction Data. Among others, it facilitates Bayesian inference of the general unified threshold model of survival (GUTS). See our companion paper Baudrot and Charles (2021) <doi:10.21105/joss.03200>, as well as complementary details in Baudrot et al. (2018) <doi:10.1021/acs.est.7b05464> and Delignette-Muller et al. (2017) <doi:10.1021/acs.est.6b05326>.
Maintained by Virgile Baudrot. Last updated 6 months ago.
3.26 score 60 scripts
tbaghfalaki
UHM:Unified Zero-Inflated Hurdle Regression Models
Run a Gibbs sampler for hurdle models to analyze data showing an excess of zeros, which is common in zero-inflated count and semi-continuous models. The package includes the hurdle model under Gaussian, Gamma, inverse Gaussian, Weibull, Exponential, Beta, Poisson, negative binomial, logarithmic, Bell, generalized Poisson, and binomial distributional assumptions. The models described in Ganjali et al. (2024) <doi:...>.
Maintained by Taban Baghfalaki. Last updated 5 months ago.
3.18 score
ingaschwabe
BayesTwin:Bayesian Analysis of Item-Level Twin Data
Bayesian analysis of item-level hierarchical twin data using an integrated item response theory model. Analyses are based on Schwabe & van den Berg (2014) <doi:10.1007/s10519-014-9649-7>, Molenaar & Dolan (2014) <doi:10.1007/s10519-014-9647-9>, Schwabe, Jonker & van den Berg (2016) <doi:10.1007/s10519-015-9768-9> and Schwabe, Boomsma & van den Berg (2016) <doi:10.1016/j.lindif.2017.01.018>. Caution! The subroutines of this package rely on the program JAGS, which can be freely obtained from http://mcmc-jags.sourceforge.net.
Maintained by Inga Schwabe. Last updated 7 years ago.
bayesiangeneticsheritabilityitem-response-theorymcmc-samplerpsychometricsjagscpp
3.04 score 11 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 24 days ago.
bayesian-statisticsbugscost-effectiveness-analysisjagstidyverse
2 stars 3.00 score 7 scripts
imares-group
glossa:User-Friendly 'shiny' App for Bayesian Species Distribution Models
A user-friendly 'shiny' application for Bayesian machine learning analysis of marine species distributions. GLOSSA (Global Species Spatiotemporal Analysis) uses Bayesian Additive Regression Trees (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) to model species distributions with intuitive workflows for data upload, processing, model fitting, and result visualization. It supports presence-absence and presence-only data (with pseudo-absence generation), spatial thinning, cross-validation, and scenario-based projections. GLOSSA is designed to facilitate ecological research by providing easy-to-use tools for analyzing and visualizing marine species distributions across different spatial and temporal scales.
Maintained by Jorge Mestre-Tomás. Last updated 4 months ago.
1 stars 3.00 score 5 scripts
calbertsen
covafillr:Local Polynomial Regression of State Dependent Covariates in State-Space Models
Facilitates local polynomial regression for state dependent covariates in state-space models. The functionality can also be used from 'C++' based model builder tools such as 'Rcpp'/'inline', 'TMB', or 'JAGS'.
Maintained by Christoffer Moesgaard Albertsen. Last updated 5 years ago.
cppeigenjagslocal-polynomial-regressionrcpptmbcpp
1 stars 3.00 score 20 scripts
bumbanian
isoWater:Discovery, Retrieval, and Analysis of Water Isotope Data
The wiDB...() functions provide an interface to the public API of the wiDB <https://github.com/SPATIAL-Lab/isoWater/blob/master/Protocol.md>: build, check and submit queries, and receive and unpack responses. Data analysis functions support Bayesian inference of the source and source isotope composition of water samples that may have experienced evaporation. Algorithms adapted from Bowen et al. (2018, <doi:10.1007/s00442-018-4192-5>).
Maintained by Gabe Bowen. Last updated 8 months ago.
2.81 score 13 scripts
luzhangstat
phase1PRMD:Personalized Repeated Measurement Design for Phase I Clinical Trials
Implements Bayesian phase I repeated measurement design that accounts for multidimensional toxicity endpoints and longitudinal efficacy measure from multiple treatment cycles. The package provides flags to fit a variety of model-based phase I design, including 1 stage models with or without individualized dose modification, 3-stage models with or without individualized dose modification, etc. Functions are provided to recommend dosage selection based on the data collected in the available patient cohorts and to simulate trial characteristics given design parameters. Yin, Jun, et al. (2017) <doi:10.1002/sim.7134>.
Maintained by Lu Zhang. Last updated 5 years ago.
2.74 score 11 scripts
alfrzlp
saeHB.unit:Basic Unit Level Model using Hierarchical Bayesian Approach
Small area estimation unit level models (Battese-Harter-Fuller model) with a Bayesian Hierarchical approach. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Battese et al. (1988) <doi:10.1080/01621459.1988.10478561>.
Maintained by Ridson Al Farizal P. Last updated 1 years ago.
1 stars 2.70 score 1 scriptspsolymos
sharx:Models and Data Sets for the Study of Species-Area Relationships
Hierarchical models for the analysis of species-area relationships (SARs) by combining several data sets and covariates; with a global data set combining individual SAR studies; as described in Solymos and Lele (2012) <doi:10.1111/j.1466-8238.2011.00655.x>.
Maintained by Peter Solymos. Last updated 10 months ago.
1 stars 2.70 score 4 scripts
vivienjyin
phase1RMD:Repeated Measurement Design for Phase I Clinical Trial
Implements our Bayesian phase I repeated measurement design that accounts for multidimensional toxicity endpoints from multiple treatment cycles. The package also provides a novel design to account for both multidimensional toxicity endpoints and early-stage efficacy endpoints in the phase I design. For both designs, functions are provided to recommend the next dosage selection based on the data collected in the available patient cohorts and to simulate trial characteristics given design parameters. Yin, Jun, et al. (2017) <doi:10.1002/sim.7134>.
Maintained by Jun Yin. Last updated 4 years ago.
2.70 score 8 scripts
umich-biostatistics
lcra:Bayesian Joint Latent Class and Regression Models
For fitting Bayesian joint latent class and regression models using Gibbs sampling. See the documentation for the model. The technical details of the model implemented here are described in Elliott, Michael R., Zhao, Zhangchen, Mukherjee, Bhramar, Kanaya, Alka, Needham, Belinda L., "Methods to account for uncertainty in latent class assignments when using latent classes as predictors in regression models, with application to acculturation strategy measures" (2020) In press at Epidemiology <doi:10.1097/EDE.0000000000001139>.
Maintained by Michael Kleinsasser. Last updated 1 years ago.
2.70 score 2 scripts
sullivan0147
midas2:An Information Borrowing Drug-Combination Bayesian Platform Design(MIDAS-2)
An Information borrowing drug-combination Bayesian platform design with subgroup exploration and hierarchical constrain.
Maintained by Su Liwen. Last updated 3 years ago.
2.70 score
gravesti
psborrow:Bayesian Dynamic Borrowing with Propensity Score
A tool which aims to help evaluate the effect of external borrowing using an integrated approach described in Lewis et al., (2019) <doi:10.1080/19466315.2018.1497533> that combines propensity score and Bayesian dynamic borrowing methods.
Maintained by Isaac Gravestock. Last updated 1 months ago.
1 stars 2.60 score 4 scripts
davidchampredon
ern:Effective Reproduction Number Estimation
Estimate the effective reproduction number from wastewater and clinical data sources.
Maintained by David Champredon. Last updated 2 months ago.
2.45 score 14 scripts
daifengstat
agRee:Various Methods for Measuring Agreement
Bland-Altman plot and scatter plot with identity line for visualization and point and interval estimates for different metrics related to reproducibility/repeatability/agreement including the concordance correlation coefficient, intraclass correlation coefficient, within-subject coefficient of variation, smallest detectable difference, and mean normalized smallest detectable difference.
Maintained by Dai Feng. Last updated 5 years ago.
2.34 score 44 scripts
cran
SeqFeatR:A Tool to Associate FASTA Sequences and Features
Provides user friendly methods for the identification of sequence patterns that are statistically significantly associated with a property of the sequence. For instance, SeqFeatR allows to identify viral immune escape mutations for hosts of given HLA types. The underlying statistical method is Fisher's exact test, with appropriate corrections for multiple testing, or Bayes. Patterns may be point mutations or n-tuple of mutations. SeqFeatR offers several ways to visualize the results of the statistical analyses, see Budeus (2016) <doi:10.1371/journal.pone.0146409>.
Maintained by Bettina Budeus. Last updated 6 years ago.
2.30 score
vharntzen
doublIn:Estimate Incubation or Latency Time using Doubly Interval Censored Observations
Visualize contact tracing data using a 'shiny' app and estimate the incubation or latency time of an infectious disease respecting the following characteristics in the analysis; (i) doubly interval censoring with (partly) overlapping or distinct windows; (ii) an infection risk corresponding to exponential growth; (iii) right truncation allowing for individual truncation times; (iv) different choices concerning the family of the distribution. For our earlier work, we refer to Arntzen et al. (2023) <doi:10.1002/sim.9726>. A paper describing our approach in detail will follow.
Maintained by Vera Arntzen. Last updated 10 months ago.
2.30 score 3 scriptsdaifengstat
miscF:Miscellaneous Functions
Various functions for random number generation, density estimation, classification, curve fitting, and spatial data analysis.
Maintained by Dai Feng. Last updated 5 years ago.
2.26 score 15 scripts 2 dependents
jrlockwood
eivtools:Measurement Error Modeling Tools
This includes functions for analysis with error-prone covariates, including deconvolution, latent regression and errors-in-variables regression. It implements methods by Rabe-Hesketh et al. (2003) <doi:10.1191/1471082x03st056oa>, Lockwood and McCaffrey (2014) <doi:10.3102/1076998613509405>, and Lockwood and McCaffrey (2017) <doi:10.1007/s11336-017-9556-y>, among others.
Maintained by J.R. Lockwood. Last updated 3 years ago.
2.26 score 18 scriptsdaifengstat
PottsUtils:Utility Functions of the Potts Models
There are three sets of functions. The first produces basic properties of a graph and generates samples from multinomial distributions to facilitate the simulation functions (they maybe used for other purposes as well). The second provides various simulation functions for a Potts model in Potts, R. B. (1952) <doi:10.1017/S0305004100027419>. The third currently includes only one function which computes the normalizing constant of a Potts model based on simulation results.
Maintained by Dai Feng. Last updated 5 months ago.
2.11 score 13 scripts
linlf
pcnetmeta:Patient-Centered Network Meta-Analysis
Performs Bayesian arm-based network meta-analysis for datasets with binary, continuous, and count outcomes (Zhang et al., 2014 <doi:10.1177/1740774513498322>; Lin et al., 2017 <doi:10.18637/jss.v080.i05>).
Maintained by Lifeng Lin. Last updated 3 years ago.
1 stars 2.08 score 12 scripts
bayesstats
bamdit:Bayesian Meta-Analysis of Diagnostic Test Data
Provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS).
Maintained by Pablo Emilio Verde. Last updated 2 months ago.
2.05 score 14 scriptsbayesstats
jarbes:Just a Rather Bayesian Evidence Synthesis
Provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS).
Maintained by Pablo Emilio Verde. Last updated 1 days ago.
1 stars 2.03 score 27 scriptscran
eSIR:Extended State-Space SIR Models
An implementation of extended state-space SIR models developed by Song Lab at UM school of Public Health. There are several functions available by 1) including a time-varying transmission modifier, 2) adding a time-dependent quarantine compartment, 3) adding a time-dependent antibody-immunization compartment. Wang L. (2020) <doi:10.6339/JDS.202007_18(3).0003>.
Maintained by Michael Kleinsasser. Last updated 3 years ago.
1 stars 2.00 scorejrlockwood
HETOP:MLE and Bayesian Estimation of Heteroskedastic Ordered Probit (HETOP) Model
Provides functions for maximum likelihood and Bayesian estimation of the Heteroskedastic Ordered Probit (HETOP) model, using methods described in Lockwood, Castellano and Shear (2018) <doi:10.3102/1076998618795124> and Reardon, Shear, Castellano and Ho (2017) <doi:10.3102/1076998616666279>. It also provides a general function to compute the triple-goal estimators of Shen and Louis (1998) <doi:10.1111/1467-9868.00135>.
Maintained by J.R. Lockwood. Last updated 3 years ago.
1 stars 2.00 score
dkahle
unmconf:Modeling with Unmeasured Confounding
Tools for fitting and assessing Bayesian multilevel regression models that account for unmeasured confounders.
Maintained by David Kahle. Last updated 7 months ago.
2.00 score
atanubhattacharjee
dscoreMSM:Survival Proximity Score Matching in Multi-State Survival Model
Implements survival proximity score matching in multi-state survival models. Includes tools for simulating survival data and estimating transition-specific coxph models with frailty terms. The primary methodological work on multistate censored data modeling using propensity score matching has been published by Bhattacharjee et al.(2024) <doi:10.1038/s41598-024-54149-y>.
Maintained by Atanu Bhattacharjee. Last updated 4 months ago.
2.00 score
bbcrown
phenoCDM:Continuous Development Models for Incremental Time-Series Analysis
Using the Bayesian state-space approach, we developed a continuous development model to quantify dynamic incremental changes in the response variable. While the model was originally developed for daily changes in forest green-up, the model can be used to predict any similar process. The CDM can capture both timing and rate of nonlinear processes. Unlike statics methods, which aggregate variations into a single metric, our dynamic model tracks the changing impacts over time. The CDM accommodates nonlinear responses to variation in predictors, which changes throughout development.
Maintained by Bijan Seyednasrollah. Last updated 7 years ago.
1 stars 2.00 score 8 scripts
qingzhaoyu
BayesianMediationA:Bayesian Mediation Analysis
We perform general mediation analysis in the Bayesian setting using the methods described in Yu and Li (2022, ISBN:9780367365479). With the package, the mediation analysis can be performed on different types of outcomes (e.g., continuous, binary, categorical, or time-to-event), with default or user-defined priors and predictive models. The Bayesian estimates and credible sets of mediation effects are reported as analytic results.
Maintained by Qingzhao Yu. Last updated 3 years ago.
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.
2.00 score 4 scripts
mmcarli
groupWQS:Grouped Weighted Quantile Sum Regression
Fits weighted quantile sum (WQS) regressions for one or more chemical groups with continuous or binary outcomes. Wheeler D, Czarnota J.(2016) <doi:10.1289/isee.2016.4698>.
Maintained by Matthew Carli. Last updated 5 years ago.
2.00 score 4 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.
2.00 score 2 scripts
formidify
BayesCACE:Bayesian Model for CACE Analysis
Performs CACE (Complier Average Causal Effect analysis) on either a single study or meta-analysis of datasets with binary outcomes, using either complete or incomplete noncompliance information. Our package implements the Bayesian methods proposed in Zhou et al. (2019) <doi:10.1111/biom.13028>, which introduces a Bayesian hierarchical model for estimating CACE in meta-analysis of clinical trials with noncompliance, and Zhou et al. (2021) <doi:10.1080/01621459.2021.1900859>, with an application example on Epidural Analgesia.
Maintained by Jinhui Yang. Last updated 2 years ago.
2.00 scorecran
IIVpredictor:Modeling Within Individual Variability as Predictor
Time parceling method and Bayesian variability modeling methods for modeling within individual variability indicators as predictors.For more details, see <https://github.com/xliu12/IIVpredicitor>.
Maintained by Xiao Liu. Last updated 4 years ago.
2.00 scorecran
zoib:Bayesian Inference for Beta Regression and Zero-or-One Inflated Beta Regression
Fits beta regression and zero-or-one inflated beta regression and obtains Bayesian Inference of the model via the Markov Chain Monte Carlo approach implemented in JAGS.
Maintained by Fang Liu. Last updated 2 years ago.
3 stars 1.78 score
dsjohnson
mbpp:Model-based estimation of northern fur seal pup production
Impliments methods for model-based estimation of northern fur seal pup production.
Maintained by Devin S. Johnson. Last updated 3 years ago.
1.70 score
minniesun
blrm:Dose Escalation Design in Phase I Oncology Trial Using Bayesian Logistic Regression Modeling
Design dose escalation using Bayesian logistic regression modeling in Phase I oncology trial.
Maintained by Furong Sun. Last updated 3 years ago.
1 stars 1.70 score 1 scripts
jagoodrich
BaHZING:Bayesian Hierarchical Zero-Inflated Negative Binomial Regression with G-Computation
A Bayesian model for examining the association between environmental mixtures and all Taxa measured in a hierarchical microbiome dataset in a single integrated analysis. Compared with analyzing the associations of environmental mixtures with each Taxa individually, 'BaHZING' controls Type 1 error rates and provides more stable effect estimates when dealing with small sample sizes.
Maintained by Jesse Goodrich. Last updated 1 months ago.
1.70 score 3 scripts
workshop-brg
abmR:Agent-Based Models in R
Supplies tools for running agent-based models (ABM) in R, as discussed in Gochanour et al. (2022) <doi:10.1111/2041-210X.14014>. The package contains two movement functions, each of which is based on the Ornstein-Uhlenbeck (OU) model (Ornstein & Uhlenbeck, 1930) <doi:10.1103/PhysRev.36.823>. It also contains several visualization and data summarization functions to facilitate the presentation of simulation results.
Maintained by Benjamin Gochanour. Last updated 2 years ago.
1 stars 1.70 scorecran
BeQut:Bayesian Estimation for Quantile Regression Mixed Models
Using a Bayesian estimation procedure, this package fits linear quantile regression models such as linear quantile models, linear quantile mixed models, quantile regression joint models for time-to-event and longitudinal data. The estimation procedure is based on the asymmetric Laplace distribution and the 'JAGS' software is used to get posterior samples (Yang, Luo, DeSantis (2019) <doi:10.1177/0962280218784757>).
Maintained by Antoine Barbieri. Last updated 1 years ago.
1 stars 1.70 scorecran
bayesmix:Bayesian Mixture Models with JAGS
Fits finite mixture models of univariate Gaussian distributions using JAGS within a Bayesian framework.
Maintained by Bettina Gruen. Last updated 2 years ago.
1.48 score 1 dependentsjjacklee116
bacistool:Bayesian Classification and Information Sharing (BaCIS) Tool for the Design of Multi-Group Phase II Clinical Trials
Provides the design of multi-group phase II clinical trials with binary outcomes using the hierarchical Bayesian classification and information sharing (BaCIS) model. Subgroups are classified into two clusters on the basis of their outcomes mimicking the hypothesis testing framework. Subsequently, information sharing takes place within subgroups in the same cluster, rather than across all subgroups. This method can be applied to the design and analysis of multi-group clinical trials with binary outcomes. Reference: Nan Chen and J. Jack Lee (2019) <doi:10.1002/bimj.201700275>.
Maintained by J. Jack Lee. Last updated 5 years ago.
1.11 score 13 scriptsmmcarli
BayesGWQS:Bayesian Grouped Weighted Quantile Sum Regression
Fits Bayesian grouped weighted quantile sum (BGWQS) regressions for one or more chemical groups with binary outcomes. Wheeler DC et al. (2019) <doi:10.1016/j.sste.2019.100286>.
Maintained by Matthew Carli. Last updated 3 years ago.
1 stars 1.08 score 12 scriptsatanubhattacharjee
longit:High Dimensional Longitudinal Data Analysis Using MCMC
High dimensional longitudinal data analysis with Markov Chain Monte Carlo(MCMC). Currently support mixed effect regression with or without missing observations by considering covariance structures. It provides estimates by missing at random and missing not at random assumptions. In this R package, we present Bayesian approaches that statisticians and clinical researchers can easily use. The functions' methodology is based on the book "Bayesian Approaches in Oncology Using R and OpenBUGS" by Bhattacharjee A (2020) <doi:10.1201/9780429329449-14>.
Maintained by Atanu Bhattacharjee. Last updated 4 years ago.
1.00 scorecran
RobustBayesianCopas:Robust Bayesian Copas Selection Model
Fits the robust Bayesian Copas (RBC) selection model of Bai et al. (2020) <arXiv:2005.02930> for correcting and quantifying publication bias in univariate meta-analysis. Also fits standard random effects meta-analysis and the Copas-like selection model of Ning et al. (2017) <doi:10.1093/biostatistics/kxx004>.
Maintained by Ray Bai. Last updated 4 years ago.
1.00 scoreatanubhattacharjee
afthd:Accelerated Failure Time for High Dimensional Data with MCMC
Functions for Posterior estimates of Accelerated Failure Time(AFT) model with MCMC and Maximum likelihood estimates of AFT model without MCMC for univariate and multivariate analysis in high dimensional gene expression data are available in this 'afthd' package. AFT model with Bayesian framework for multivariate in high dimensional data has been proposed by Prabhash et al.(2016) <doi:10.21307/stattrans-2016-046>.
Maintained by Atanu Bhattacharjee. Last updated 3 years ago.
1.00 score 7 scripts
czhong9106
SAME:Seamless Adaptive Multi-Arm Multi-Stage Enrichment
Design a Bayesian seamless multi-arm biomarker-enriched phase II/III design with the survival endpoint with allowing sample size re-estimation. James M S Wason, Jean E Abraham, Richard D Baird, Ioannis Gournaris, Anne-Laure Vallier, James D Brenton, Helena M Earl, Adrian P Mander (2015) <doi:10.1038/bjc.2015.278>. Guosheng Yin, Nan Chen, J. Jack Lee (2018) <doi:10.1007/s12561-017-9199-7>. Ying Yuan, Beibei Guo, Mark Munsell, Karen Lu, Amir Jazaeri (2016) <doi:10.1002/sim.6971>.
Maintained by Chengxue Zhong. Last updated 2 years ago.
1.00 score 1 scriptsdaifengstat
auRoc:Various Methods to Estimate the AUC
Estimate the AUC using a variety of methods as follows: (1) frequentist nonparametric methods based on the Mann-Whitney statistic or kernel methods. (2) frequentist parametric methods using the likelihood ratio test based on higher-order asymptotic results, the signed log-likelihood ratio test, the Wald test, or the approximate ''t'' solution to the Behrens-Fisher problem. (3) Bayesian parametric MCMC methods.
Maintained by Dai Feng. Last updated 5 years ago.
1.00 score 4 scriptscran
bdribs:Bayesian Detection of Potential Risk Using Inference on Blinded Safety Data
Implements Bayesian inference to detect signal from blinded clinical trial when total number of adverse events of special concerns and total risk exposures from all patients are available in the study. For more details see the article by Mukhopadhyay et. al. (2018) titled 'Bayesian Detection of Potential Risk Using Inference on Blinded Safety Data', in Pharmaceutical Statistics (to appear).
Maintained by Saurabh Mukhopadhyay. Last updated 7 years ago.
1.00 score
atanubhattacharjee
SurviMChd:High Dimensional Survival Data Analysis with Markov Chain Monte Carlo
High dimensional survival data analysis with Markov Chain Monte Carlo(MCMC). Currently supports frailty data analysis. Allows for Weibull and Exponential distribution. Includes function for interval censored data.
Maintained by Atanu Bhattacharjee. Last updated 1 years ago.
1.00 score
drsmukherjee
hbbr:Hierarchical Bayesian Benefit-Risk Assessment Using Discrete Choice Experiment
Implements assessment of benefit-risk balance using Bayesian Discrete Choice Experiment. For more details see the article by Mukhopadhyay et al. (2019) <DOI:10.1080/19466315.2018.1527248>.
Maintained by Saurabh Mukhopadhyay. Last updated 5 years ago.
1.00 score
ddalthorp
eoa3:Wildlife Mortality Estimator for Low Fatality Rates and Imperfect Detection
Evidence of Absence software (EoA) is a user-friendly application for estimating bird and bat fatalities at wind farms and designing search protocols. The software is particularly useful in addressing whether the number of fatalities has exceeded a given threshold and what search parameters are needed to give assurance that thresholds were not exceeded. The models are applicable even when zero carcasses have been found in searches, following Huso et al. (2015) <doi:10.1890/14-0764.1>, Dalthorp et al. (2017) <doi:10.3133/ds1055>, and Dalthorp and Huso (2015) <doi:10.3133/ofr20151227>.
Maintained by Daniel Dalthorp. Last updated 4 months ago.
1.00 scorecran
lira:LInear Regression in Astronomy
Performs Bayesian linear regression and forecasting in astronomy. The method accounts for heteroscedastic errors in both the independent and the dependent variables, intrinsic scatters (in both variables) and scatter correlation, time evolution of slopes, normalization, scatters, Malmquist and Eddington bias, upper limits and break of linearity. The posterior distribution of the regression parameters is sampled with a Gibbs method exploiting the JAGS library.
Maintained by Mauro Sereno. Last updated 7 years ago.
1.00 scorecran
EWOC.Comb:Escalation with Overdose Control using 2 Drug Combinations
Implements Escalation With Overdose Control trial designs using two drug combinations described by this paper <doi:10.1002/sim.6961>(Tighiouart et al., 2016). It calculates the recommended dose for next cohorts and perform simulations to obtain operating characteristics.
Maintained by Yujie Cui. Last updated 8 months ago.
1.00 scorecran
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.
1.00 scorecran
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.
1 stars 1.00 scoreqingzhaoyu
hdbma:Bayesian Mediation Analysis with High-Dimensional Data
Mediation analysis is used to identify and quantify intermediate effects from factors that intervene the observed relationship between an exposure/predicting variable and an outcome. We use a Bayesian adaptive lasso method to take care of the hierarchical structures and high dimensional exposures or mediators.
Maintained by Qingzhao Yu. Last updated 1 years ago.
1.00 scorecran
CoDaLoMic:Compositional Models to Longitudinal Microbiome Data
Implementation of models to analyse compositional microbiome time series taking into account the interaction between groups of bacteria. The models implemented are described in Creus-Martí et al (2018, ISBN:978-84-09-07541-6), Creus-Martí et al (2021) <doi:10.1155/2021/9951817> and Creus-Martí et al (2022) <doi:10.1155/2022/4907527>.
Maintained by Irene Creus Martí. Last updated 2 months ago.
1.00 score
zgompert
gbs2ploidy:Inference of Ploidy from (Genotyping-by-Sequencing) GBS Data
Functions for inference of ploidy from (Genotyping-by-sequencing) GBS data, including a function to infer allelic ratios and allelic proportions in a Bayesian framework.
Maintained by Zachariah Gompert. Last updated 8 years ago.
1 stars 1.00 score 7 scripts
czhong9106
BayesOrdDesign:Bayesian Group Sequential Design for Ordinal Data
The proposed group-sequential trial design is based on Bayesian methods for ordinal endpoints, including three methods, the proportional-odds-model (PO)-based, non-proportional-odds-model (NPO)-based, and PO/NPO switch-model-based designs, which makes our proposed methods generic to be able to deal with various scenarios. Richard J. Barker, William A. Link (2013) <doi:10.1080/00031305.2013.791644>. Thomas A. Murray, Ying Yuan, Peter F. Thall, Joan H. Elizondo, Wayne L.Hofstetter (2018) <doi:10.1111/biom.12842>. Chengxue Zhong, Haitao Pan, Hongyu Miao (2021) <arXiv:2108.06568>.
Maintained by Chengxue Zhong. Last updated 2 years ago.
1.00 score
mansukoh
bayesMRM:Bayesian Multivariate Receptor Modeling
Bayesian analysis of multivariate receptor modeling. The package consists of implementations of the methods of Park and Oh (2015) <doi:10.1016/j.chemolab.2015.08.021>.The package uses 'JAGS'(Just Another Gibbs Sampler) to generate Markov chain Monte Carlo samples of parameters.
Maintained by Man-Suk Oh. Last updated 2 years ago.
1.00 score
boyanglu
NMADiagT:Network Meta-Analysis of Multiple Diagnostic Tests
Implements HSROC (hierarchical summary receiver operating characteristic) model developed by Ma, Lian, Chu, Ibrahim, and Chen (2018) <doi:10.1093/biostatistics/kxx025> and hierarchical model developed by Lian, Hodges, and Chu (2019) <doi:10.1080/01621459.2018.1476239> for performing meta-analysis for 1-5 diagnostic tests to simultaneously compare multiple tests within a missing data framework. This package evaluates the accuracy of multiple diagnostic tests and also gives graphical representation of the results.
Maintained by Boyang Lu. Last updated 5 years ago.
1.00 score 1 scripts
mgoulartinc
RcmdrPlugin.RMTCJags:R MTC Jags 'Rcmdr' Plugin
Mixed Treatment Comparison is a methodology to compare directly and/or indirectly health strategies (drugs, treatments, devices). This package provides an 'Rcmdr' plugin to perform Mixed Treatment Comparison for binary outcome using BUGS code from Bristol University (Lu and Ades).
Maintained by Marcelo Goulart Correia. Last updated 9 years ago.
1.00 score