Package: bqmm 0.1.0

Kailas Venkitasubramanian

bqmm: Bayesian Multilevel Quantile Regression

Fits Bayesian mixed-effects (multilevel) quantile regression models using the asymmetric Laplace working likelihood and Stan. Supports an 'lme4'-style formula interface with nested and crossed random effects, fitting one or several quantiles, post-hoc non-crossing rearrangement of fitted quantiles, and the Yang, Wang and He (2016) posterior-variance correction for valid frequentist inference from the (misspecified) asymmetric Laplace posterior.

Authors:Kailas Venkitasubramanian [aut, cre, cph]

bqmm_0.1.0.tar.gz
bqmm_0.1.0.zip(r-4.7-x86_64)bqmm_0.1.0.zip(r-4.7-arm64)bqmm_0.1.0.zip(r-4.6-x86_64)bqmm_0.1.0.zip(r-4.6-arm64)
bqmm_0.1.0.tar.gz(r-4.7-arm64)bqmm_0.1.0.tar.gz(r-4.7-x86_64)bqmm_0.1.0.tar.gz(r-4.6-arm64)bqmm_0.1.0.tar.gz(r-4.6-x86_64)
bqmm_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
bqmm/json (API)

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

Bug tracker:https://github.com/kvenkita/bqmm/issues

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

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

On CRAN:

Conda:

onetbbcpp

3.30 score 328 downloads 11 exports 59 dependencies

Last updated from:e026a6c013. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK282
linux-devel-x86_64OK322
source / vignettesOK500
linux-release-arm64OK267
linux-release-x86_64OK321
windows-devel-arm64OK416
windows-devel-x86_64OK373
windows-release-arm64OK425
windows-release-x86_64OK373
wasm-releaseOK219

Exports:aldas_drawsbqmmbqmm_priorfixeflog_likposterior_epredposterior_predictranefrearrange_quantilesVarCorr

Dependencies:abindbackportsBHbootcallrcheckmateclicpp11descdistributionalfarvergenericsggplot2gluegridExtragtableinlineisobandlabelinglatticelifecyclelme4loomagrittrMASSMatrixmatrixStatsminqanlmenloptrnumDerivotelpillarpkgbuildpkgconfigposteriorprocessxpsQuickJSRR6rbibutilsRColorBrewerRcppRcppEigenRcppParallelRdpackreformulasrlangrstanrstantoolsS7scalesStanHeaderstensorAtibbleutf8vctrsviridisLitewithr

A Primer on Bayesian Multilevel Quantile Regression
1. Why model quantiles? | Why multilevel? | Why Bayesian? | 2. The model in one page | 3. Your first model | 4. Many quantiles at once | 5. Priors and the scale parameter | 6. Getting the uncertainty right | 7. Correlated random effects | 8. Diagnostics: is the fit trustworthy? | 9. Visualising results | 10. Practical guidance and pitfalls | 11. How bqmm relates to other tools | References
Rendered frombqmm-primer.Rmdusingknitr::rmarkdown

Last update: 2026-07-10
Started: 2026-07-10

Introduction to bqmm
A first model | Several quantiles at once | Valid inference
Rendered frombqmm-intro.Rmdusingknitr::rmarkdown

Last update: 2026-07-10
Started: 2026-07-10

Multilevel structure in bqmm
Random intercepts | Random slopes | Nested and crossed grouping | Practical notes
Rendered frombqmm-multilevel.Rmdusingknitr::rmarkdown

Last update: 2026-07-10
Started: 2026-07-10

Valid inference under the asymmetric Laplace likelihood
The problem | The correction | Why not the plain Koenker sandwich? | Scope and caveats | References
Rendered frombqmm-inference.Rmdusingknitr::rmarkdown

Last update: 2026-07-10
Started: 2026-07-10