Package: RoBTT 1.3.1

František Bartoš

RoBTT: Robust Bayesian T-Test

An implementation of Bayesian model-averaged t-tests that allows users to draw inferences about the presence versus absence of an effect, variance heterogeneity, and potential outliers. The 'RoBTT' package estimates ensembles of models created by combining competing hypotheses and applies Bayesian model averaging using posterior model probabilities. Users can obtain model-averaged posterior distributions and inclusion Bayes factors, accounting for uncertainty in the data-generating process (Maier et al., 2024, <doi:10.3758/s13423-024-02590-5>). The package also provides a truncated likelihood version of the model-averaged t-test, enabling users to exclude potential outliers without introducing bias (Godmann et al., 2024, <doi:10.31234/osf.io/j9f3s>). Users can specify a wide range of informative priors for all parameters of interest. The package offers convenient functions for summary, visualization, and fit diagnostics.

Authors:František Bartoš [aut, cre], Maximilian Maier [aut], Henrik R Godmann [aut]

RoBTT_1.3.1.tar.gz
RoBTT_1.3.1.zip(r-4.7-x86_64)RoBTT_1.3.1.zip(r-4.7-arm64)RoBTT_1.3.1.zip(r-4.6-x86_64)RoBTT_1.3.1.zip(r-4.6-arm64)
RoBTT_1.3.1.tar.gz(r-4.7-arm64)RoBTT_1.3.1.tar.gz(r-4.7-x86_64)RoBTT_1.3.1.tar.gz(r-4.6-arm64)RoBTT_1.3.1.tar.gz(r-4.6-x86_64)
RoBTT_1.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
RoBTT/json (API)

# Install 'RoBTT' in R:
install.packages('RoBTT', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/fbartos/robtt/issues

Pkgdown/docs site:https://fbartos.github.io

Uses libs:
  • onetbb– Parallelism library for C++
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

onetbbcpp

3.30 score 20 scripts 414 downloads 16 exports 61 dependencies

Last updated from:5cdfe09719. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK357
linux-devel-x86_64OK451
source / vignettesOK766
linux-release-arm64OK376
linux-release-x86_64OK330
windows-devel-arm64OK463
windows-devel-x86_64OK591
windows-release-arm64OK493
windows-release-x86_64OK494
wasm-releaseOK299

Exports:check_RoBTTcheck_setupdiagnosticsdiagnostics_autocorrelationdiagnostics_densitydiagnostics_traceinterpretis.RoBTTpriorprior_nonerho2logsdrRoBTTRoBTT.get_optionRoBTT.optionsset_controlset_convergence_checks

Dependencies:abindbackportsBayesToolsBHbridgesamplingBrobdingnagcallrcheckmateclicodacpp11descdistributionalextraDistrfarvergenericsggplot2gluegridExtragtableinlineisobandlabelinglatticelifecycleloomagrittrMatrixmatrixStatsmvtnormnumDerivotelpillarpkgbuildpkgconfigposteriorprocessxpsQuickJSRR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRdpackrlangrstanrstantoolsS7scalesStanHeadersstringistringrtensorAtibbleutf8vctrsviridisLitewithr

Introduction to RoBTT
Introduction and Example | Equal Variance t-test | Unequal Variance t-test | Model-Averaging Over Equal and Unequal Variances | Model-Averaging Over Robust t-likelihoods | Perinull testing | Conclusions | References
Rendered fromIntroduction_to_RoBTT.Rmdusingknitr::rmarkdown

Last update: 2024-11-10
Started: 2022-09-20

Truncated Bayesian Model-Averaged T-Test
Introduction | Background | Application | Installing and Loading RoBTT | Example Data Generation | Model-Averaged Truncated Bayesian Independent Samples $t$-Test | 1. Manual Outlier Exclusion Based on Specific Cutoffs. | 2. Applying Direct Truncation Based on Standard Deviations | Conclusions | References
Rendered fromTruncated_t_test.Rmdusingknitr::rmarkdown

Last update: 2024-11-10
Started: 2024-04-05