This package is considered a duplicate. The official version of this package is found at:https://florianhartig.r-universe.dev/BayesianTools

Package: BayesianTools 0.1.8

Florian Hartig

BayesianTools: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics

General-purpose MCMC and SMC samplers, as well as plots and diagnostic functions for Bayesian statistics, with a particular focus on calibrating complex system models. Implemented samplers include various Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential Monte Carlo (SMC) particle filter.

Authors:Florian Hartig [aut, cre], Francesco Minunno [aut], Stefan Paul [aut], David Cameron [ctb], Tankred Ott [ctb], Maximilian Pichler [ctb]

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BayesianTools.pdf |BayesianTools.html✨
BayesianTools/json (API)
NEWS

# Install 'BayesianTools' in R:

install.packages('BayesianTools', repos = c('https://pecanproject.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/florianhartig/bayesiantools/issues

Uses libs:

c++– GNU Standard C++ Library v3

On CRAN:

bayes ecological-models mcmc optimization smc systems-biology cpp

10.17 score 120 stars 5 packages 586 scripts 790 downloads 2 mentions 64 exports 109 dependencies

Last updated 11 months agofrom:661e126ace. Checks:OK: 4 NOTE: 5. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Dec 21 2024
R-4.5-win-x86_64	NOTE	Dec 21 2024
R-4.5-linux-x86_64	NOTE	Dec 21 2024
R-4.4-win-x86_64	NOTE	Dec 21 2024
R-4.4-mac-x86_64	NOTE	Dec 21 2024
R-4.4-mac-aarch64	NOTE	Dec 21 2024
R-4.3-win-x86_64	OK	Dec 21 2024
R-4.3-mac-x86_64	OK	Dec 21 2024
R-4.3-mac-aarch64	OK	Dec 21 2024

Exports:applySettingsDefault bridgesample calibrationTest checkBayesianSetup convertCoda correlationPlot createBayesianSetup createBetaPrior createLikelihood createMcmcSamplerList createMixWithDefaults createPosterior createPrior createPriorDensity createProposalGenerator createSmcSamplerList createTruncatedNormalPrior createUniformPrior DE DEzs DIC DREAM DREAMzs gelmanDiagnostics generateParallelExecuter generateTestDensityMultiNormal getCredibleIntervals getPanels getPossibleSamplerTypes getPredictiveDistribution getPredictiveIntervals getSample getVolume GOF likelihoodAR1 likelihoodIidNormal MAP marginalLikelihood marginalPlot mergeChains Metropolis plotDiagnostic plotSensitivity plotTimeSeries plotTimeSeriesResiduals plotTimeSeriesResults runMCMC sampleMetropolis smcSampler stopParallel testDensityBanana testDensityInfinity testDensityMultiNormal testDensityNormal testLinearModel tracePlot Twalk updateProposalGenerator VSEM vsemC VSEMcreateLikelihood VSEMcreatePAR VSEMgetDefaults WAIC

Dependencies:ape askpass base64enc boot bridgesampling Brobdingnag bslib cachem cli coda codetools colorspace commonmark cpp11 crayon crosstalk curl data.table DHARMa digest doParallel dplyr ellipse emulator evaluate expm fansi farver fastmap fontawesome foreach fs gap gap.datasets generics ggplot2 glue gmm gtable highr htmltools htmlwidgets httpuv httr IDPmisc isoband iterators jquerylib jsonlite knitr labeling later lattice lazyeval lifecycle lme4 lmtest magrittr MASS Matrix memoise mgcv mime minqa msm munsell mvtnorm nlme nloptr numDeriv openssl pillar pkgconfig plotly plyr promises purrr qgam R6 rappdirs rbibutils RColorBrewer Rcpp RcppEigen Rdpack rlang rmarkdown sandwich sass scales shiny sourcetools stringi stringr survival sys tibble tidyr tidyselect tinytex tmvtnorm utf8 vctrs viridisLite withr xfun xtable yaml zoo

Interfacing your model with R

Florian Hartig

Rendered fromInterfacingAModel.Rmdusingknitr::rmarkdownon Dec 21 2024.

Last update: 2024-01-26
Started: 2019-07-31

Bayesian Tools - General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics

Rendered fromBayesianTools.Rmdusingknitr::rmarkdownon Dec 21 2024.

Last update: 2024-01-26
Started: 2016-12-26

Help page	Topics
Provides the default settings for the different samplers in runMCMC	applySettingsDefault
BayesianTools	BayesianTools-package BayesianTools
Simulation-based calibration tests	calibrationTest
Checks if an object is of class 'BayesianSetup'	checkBayesianSetup
Convert coda::mcmc objects to BayesianTools::mcmcSampler	convertCoda
Flexible function to create correlation density plots	correlationPlot
Creates a standardized collection of prior, likelihood and posterior functions, including error checks etc.	createBayesianSetup
Convenience function to create a beta prior	createBetaPrior
Creates a standardized likelihood class#'	createLikelihood
Convenience function to create an object of class mcmcSamplerList from a list of mcmc samplers	createMcmcSamplerList
Allows to mix a given parameter vector with a default parameter vector	createMixWithDefaults
Creates a standardized posterior class	createPosterior
Creates a user-defined prior class	createPrior
Fits a density function to a multivariate sample	createPriorDensity
Factory that creates a proposal generator	createProposalGenerator
Convenience function to create an object of class SMCSamplerList from a list of mcmc samplers	createSmcSamplerList
Convenience function to create a truncated normal prior	createTruncatedNormalPrior
Convenience function to create a simple uniform prior distribution	createUniformPrior
Differential-Evolution MCMC	DE
Differential-Evolution MCMC zs	DEzs
Deviance information criterion	DIC
DREAM	DREAM
DREAMzs	DREAMzs
Gelman Diagnostics	gelmanDiagnostics
Factory to generate a parallel executor of an existing function	generateParallelExecuter
Multivariate normal likelihood	generateTestDensityMultiNormal
Calculate confidence region from an MCMC or similar sample	getCredibleIntervals
Creates a DHARMa object	getDharmaResiduals
getPanels	getPanels
Returns possible sampler types	getPossibleSamplerTypes
Calculates predictive distribution based on the parameters	getPredictiveDistribution
Calculates Bayesian credible (confidence) and predictive intervals based on parameter sample	getPredictiveIntervals
Extracts the sample from a bayesianOutput	getSample getSample.data.frame getSample.double getSample.integer getSample.list getSample.matrix getSample.MCMC getSample.mcmc getSample.mcmc.list getSample.MCMC_refClass
Calculate posterior volume	getVolume
Standard GOF metrics Startvalues for sampling with nrChains > 1 : if you want to provide different start values for the different chains, provide a list	GOF
AR1 type likelihood function	likelihoodAR1
Normal / Gaussian Likelihood function	likelihoodIidNormal
calculates the Maxiumum APosteriori value (MAP)	MAP
Calcluated the marginal likelihood from a set of MCMC samples	marginalLikelihood
Plot MCMC marginals	marginalPlot
Merge Chains	mergeChains
Creates a Metropolis-type MCMC with options for covariance adaptatin, delayed rejection, Metropolis-within-Gibbs, and tempering	Metropolis
Diagnostic Plot	plotDiagnostic
Performs a one-factor-at-a-time sensitivity analysis for the posterior of a given bayesianSetup within the prior range.	plotSensitivity
Plots a time series, with the option to include confidence and prediction band	plotTimeSeries
Plots residuals of a time series	plotTimeSeriesResiduals
Creates a time series plot typical for an MCMC / SMC fit	plotTimeSeriesResults
Main wrapper function to start MCMCs, particle MCMCs and SMCs	runMCMC
SMC sampler	smcSampler
Function to close cluster in BayesianSetup	stopParallel
Banana-shaped density function	testDensityBanana
GelmanMeng test function	testDensityGelmanMeng
Test function infinity ragged	testDensityInfinity
3d Mutivariate Normal likelihood	testDensityMultiNormal
Normal likelihood	testDensityNormal
Fake model, returns a ax + b linear response to 2-param vector	testLinearModel
Trace plot for MCMC class	tracePlot
T-walk MCMC	Twalk
To update settings of an existing proposal genenerator	updateProposalGenerator
Very simple ecosystem model	VSEM
C version of the VSEM model	vsemC
Create an example dataset, and from that a likelihood or posterior for the VSEM model	VSEMcreateLikelihood
Create a random radiation (PAR) time series	VSEMcreatePAR
returns the default values for the VSEM	VSEMgetDefaults
calculates the WAIC	WAIC

Package: BayesianTools 0.1.8

BayesianTools: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics

Interfacing your model with R

Bayesian Tools - General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics

Citation

Development and contributors

Readme and manuals

Help Manual

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