Package: bfw 0.4.2
Øystein Olav Skaar
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).
Authors:
bfw_0.4.2.tar.gz
bfw_0.4.2.zip(r-4.7)bfw_0.4.2.zip(r-4.6)bfw_0.4.2.zip(r-4.5)
bfw_0.4.2.tgz(r-4.6-any)bfw_0.4.2.tgz(r-4.5-any)
bfw_0.4.2.tar.gz(r-4.7-any)bfw_0.4.2.tar.gz(r-4.6-any)
bfw_0.4.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
bfw/json (API)
NEWS
| # Install 'bfw' in R: |
| install.packages('bfw', repos = c('https://oeysan.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/oeysan/bfw/issues
- jags– Just Another Gibbs Sampler for Bayesian MCMC - binary JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: * To have an engine for the BUGS language that runs on Unix * To be extensible, allowing users to write their own functions, distributions and samplers. * To be a plaftorm for experimentation with ideas in Bayesian modelling This package contains the 'jags' binary as well as the associated shared library modules loaded by the binary.
- c++– GNU Standard C++ Library v3
- Cats - Dataset with Cats
bayesian-data-analysisbayesian-statisticsjagsmcmcpsychological-sciencecpp
Last updated from:9d2b94c2b4. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 154 | ||
| source / vignettes | OK | 215 | ||
| linux-release-x86_64 | OK | 157 | ||
| macos-release-arm64 | OK | 141 | ||
| macos-oldrel-arm64 | OK | 189 | ||
| windows-devel | OK | 94 | ||
| windows-release | OK | 85 | ||
| windows-oldrel | OK | 89 | ||
| wasm-release | OK | 139 |
Exports:AddNamesbfwCapWordsChangeNamesComputeHDIContrastNamesDiagMCMCDistinctColorsETAFileNameFindEnvironmentFlattenListGammaDistGetRangeInterleaveInverseHDILayoutMatrixCombnMergeMCMCMultiGrepNormalizePadVectorParseNumberParsePlotPlotCirclizePlotDataPlotMeanPlotNominalPlotParamReadFileRemoveEmptyRemoveGarbageRemoveSpacesRunContrastsRunMCMCSingleStringStatsBernoulliStatsCovariateStatsFitStatsKappaStatsMeanStatsMetricStatsNominalStatsRegressionStatsSoftmaxSumMCMCSumToZeroTidyCodeTrimTrimSplitVectorSub
Dependencies:askpassbase64enccirclizeclicodacolorspacecpp11data.tabledigestdplyrfarverfastmapfontBitstreamVerafontLiberationfontquivergdtoolsgenericsggplot2GlobalOptionsgluegtablehtmltoolsisobandjsonlitelabelinglatticelifecyclemagrittrMASSofficeropensslpillarpkgconfigplyrpngpurrrR6raggRColorBrewerRcpprlangrunjagsrvgS7scalesshapestringistringrsyssystemfontstextshapingtibbletidyrtidyselectutf8uuidvctrsviridisLitewithrxml2zip
Fit Latent Data
Rendered fromfit_latent_data.Rmdusingknitr::rmarkdownon Jun 04 2026.Last update: 2022-02-22
Started: 2022-02-22
Fit Observed Data
Rendered fromfit_observed_data.Rmdusingknitr::rmarkdownon Jun 04 2026.Last update: 2022-02-22
Started: 2022-02-22
Plot Data
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Started: 2022-02-22
Predict Metric
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Started: 2022-02-22
Regression
Rendered fromregression.Rmdusingknitr::rmarkdownon Jun 04 2026.Last update: 2022-02-22
Started: 2022-02-22
