First we simulate some data | Next we run a standard mediation analysis using lavaan | Now for the Bayesian model | Time for some noise | Rerun the lavaan model | Run the Bayesian model with robust estimates
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✨
DESCRIPTION |NEWS
card.svg |card.png
bfw/json (API)
| # 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
Datasets:
- 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 | 149 | ||
| source / vignettes | OK | 225 | ||
| linux-release-x86_64 | OK | 163 | ||
| macos-release-arm64 | OK | 145 | ||
| macos-oldrel-arm64 | OK | 129 | ||
| windows-devel | OK | 82 | ||
| windows-release | OK | 94 | ||
| windows-oldrel | OK | 107 | ||
| wasm-release | OK | 169 |
Exports:AddNamesbfwCapWordsChangeNamesComputeHDIContrastNamesDiagMCMCDistinctColorsETAFileNameFindEnvironmentFlattenListGammaDistGetRangeInterleaveInverseHDILayoutMatrixCombnMergeMCMCMultiGrepNormalizePadVectorParseNumberParsePlotPlotCirclizePlotDataPlotMeanPlotNominalPlotParamReadFileRemoveEmptyRemoveGarbageRemoveSpacesRunContrastsRunMCMCSingleStringStatsBernoulliStatsCovariateStatsFitStatsKappaStatsMeanStatsMetricStatsNominalStatsRegressionStatsSoftmaxSumMCMCSumToZeroTidyCodeTrimTrimSplitVectorSub
Dependencies:askpassbase64enccirclizeclicodacolorspacecpp11data.tabledigestdplyrfarverfastmapfontBitstreamVerafontLiberationfontquivergdtoolsgenericsggplot2GlobalOptionsgluegtablehtmltoolsisobandjsonlitelabelinglatticelifecyclemagrittrMASSofficeropensslpillarpkgconfigplyrpngpurrrR6raggRColorBrewerRcpprlangrunjagsrvgS7scalesshapestringistringrsyssystemfontstextshapingtibbletidyrtidyselectutf8uuidvctrsviridisLitewithrxml2zip
Fit Latent Data
Last update: 2022-02-22
Started: 2022-02-22
Fit Observed Data
First we simulate some data | Then we create some observed data based on the latent variables | Next we run a standard observed mediation analysis using lavaan | Run the Bayesian model on observed data | Let's add some noise to the observed data | Run the lavaan model on biased data | Finally, run the Bayesian model with robust estimates on biased data
Last update: 2022-02-22
Started: 2022-02-22
Plot Data
First we simulate some data and estimate means and standard deviations | Plot the data as repeated measures | Lets add some noise | Plot the noise as repeated measures | Let's add a group | Plot the split data
Last update: 2022-02-22
Started: 2022-02-22
Predict Metric
Next we'll run the Bayesian model to analyze the cats | Uhm. That's a lot of obscure output | References
Last update: 2022-02-22
Started: 2022-02-22
Regression
First we simulate some data | Check the correlation and regression results using frequentist methods | Then the regression results using the Bayesian model | Now we add some noise | Repeat | Finally, using Bayesian model with robust estimates
Last update: 2022-02-22
Started: 2022-02-22
