Package: hsstan 0.8.2.9000

hsstan: Hierarchical Shrinkage Stan Models for Biomarker Selection

Linear and logistic regression models penalized with hierarchical shrinkage priors for selection of biomarkers (or more general variable selection), which can be fitted using Stan (Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>). It implements the horseshoe and regularized horseshoe priors (Piironen and Vehtari (2017) <doi:10.1214/17-EJS1337SI>), as well as the projection predictive selection approach to recover a sparse set of predictive biomarkers (Piironen, Paasiniemi and Vehtari (2020) <doi:10.1214/20-EJS1711>).

Authors:Marco Colombo [aut, cre], Paul McKeigue [aut], Athina Spiliopoulou [ctb]

hsstan_0.8.2.9000.tar.gz
hsstan_0.8.2.9000.zip(r-4.5)hsstan_0.8.2.9000.zip(r-4.4)hsstan_0.8.2.9000.zip(r-4.3)
hsstan_0.8.2.9000.tgz(r-4.4-x86_64)hsstan_0.8.2.9000.tgz(r-4.4-arm64)hsstan_0.8.2.9000.tgz(r-4.3-x86_64)hsstan_0.8.2.9000.tgz(r-4.3-arm64)
hsstan_0.8.2.9000.tar.gz(r-4.5-noble)hsstan_0.8.2.9000.tar.gz(r-4.4-noble)
hsstan.pdf |hsstan.html✨
hsstan/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/mcol/hsstan/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • diabetes - Diabetes data with interaction terms

On CRAN:

bayesianfeature-selectionmcmc

3.56 score 6 stars 12 scripts 276 downloads 15 exports 55 dependencies

Last updated 10 months agofrom:ccdecc0008. Checks:OK: 2 NOTE: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 26 2024
R-4.5-win-x86_64NOTEOct 26 2024
R-4.5-linux-x86_64OKOct 26 2024
R-4.4-win-x86_64NOTEOct 26 2024
R-4.4-mac-x86_64NOTEOct 26 2024
R-4.4-mac-aarch64NOTEOct 26 2024
R-4.3-win-x86_64NOTEOct 26 2024
R-4.3-mac-x86_64NOTEOct 26 2024
R-4.3-mac-aarch64NOTEOct 26 2024

Exports:bayes_R2hsstankfoldlog_liklooloo_R2nsamplesposterior_intervalposterior_linpredposterior_performanceposterior_predictposterior_summaryprojselsampler.statswaic

Dependencies:abindbackportsBHcallrcheckmateclicolorspacedescdistributionalfansifarvergenericsggplot2gluegridExtragtableinlineisobandlabelinglatticelifecycleloomagrittrMASSMatrixmatrixStatsmgcvmunsellnlmenumDerivpillarpkgbuildpkgconfigplyrposteriorpROCprocessxpsQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelrlangrstanrstantoolsscalesStanHeaderstensorAtibbleutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Hierarchical shrinkage Stan models for biomarker selectionhsstan-package
Bayesian and LOO-adjusted R-squaredbayes_R2 bayes_R2.hsstan loo_R2 loo_R2.hsstan
Diabetes data with interaction termsdiabetes
Hierarchical shrinkage modelshsstan
K-fold cross-validationkfold kfold.hsstan
Pointwise log-likelihoodlog_lik log_lik.hsstan
Predictive information criteria for Bayesian modelsloo loo.hsstan waic waic.hsstan
Number of posterior samplesnsamples nsamples.hsstan
Plot of relative explanatory power of predictorsplot.projsel
Posterior uncertainty intervalsposterior_interval posterior_interval.hsstan
Posterior distribution of the linear predictorposterior_linpred posterior_linpred.hsstan
Posterior measures of performanceposterior_performance
Posterior predictive distributionposterior_predict posterior_predict.hsstan
Posterior summaryposterior_summary posterior_summary.default posterior_summary.hsstan
Print a summary for the fitted modelprint.hsstan
Forward selection minimizing KL-divergence in projectionprojsel
Sampler statisticssampler.stats
Summary for the fitted modelsummary.hsstan