Package: LPRelevance 3.3
LPRelevance: Relevance-Integrated Statistical Inference Engine
Provide methods to perform customized inference at individual level by taking contextual covariates into account. Three main functions are provided in this package: (i) LASER(): it generates specially-designed artificial relevant samples for a given case; (ii) g2l.proc(): computes customized fdr(z|x); and (iii) rEB.proc(): performs empirical Bayes inference based on LASERs. The details can be found in Mukhopadhyay, S., and Wang, K (2021, <arxiv:2004.09588>).
Authors:
LPRelevance_3.3.tar.gz
LPRelevance_3.3.zip(r-4.5)LPRelevance_3.3.zip(r-4.4)LPRelevance_3.3.zip(r-4.3)
LPRelevance_3.3.tgz(r-4.4-any)LPRelevance_3.3.tgz(r-4.3-any)
LPRelevance_3.3.tar.gz(r-4.5-noble)LPRelevance_3.3.tar.gz(r-4.4-noble)
LPRelevance_3.3.tgz(r-4.4-emscripten)LPRelevance_3.3.tgz(r-4.3-emscripten)
LPRelevance.pdf |LPRelevance.html✨
LPRelevance/json (API)
| # Install 'LPRelevance' in R: |
| install.packages('LPRelevance', repos = c('https://kaijunwang19.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:1e3fb4acc1. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 27 2024 |
| R-4.5-win | OK | Oct 27 2024 |
| R-4.5-linux | OK | Oct 27 2024 |
| R-4.4-win | OK | Oct 27 2024 |
| R-4.4-mac | OK | Oct 27 2024 |
| R-4.3-win | OK | Oct 27 2024 |
| R-4.3-mac | OK | Oct 27 2024 |
Exports:eLP.polyeLP.poly.predicteLP.univarfdr.threshg2l.inferg2l.infer.bootg2l.procg2l.samplerget_bh_thresholdgetNullProbLASERLASER.rEBLP.post.convLP.smoothLPcdenLPregressionPredict.LP.polyrEB.Finite.BayesrEB.procz.lp.center
Dependencies:BayesGOFBolstad2caretclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdplyre1071fansifarverforeachfuturefuture.applygenericsggplot2glmnetglobalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavaleapslifecyclelistenvlocfdrlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnleqslvnlmennetnumDerivorthopolynomparallellypillarpkgconfigplyrpolynompROCprodlimprogressrproxypurrrR6RColorBrewerRcppRcppEigenrecipesreshape2rlangrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsVGAMviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Relevance-Integrated Statistical Inference Engine | LPRelevance-package eLP.poly eLP.poly.predict eLP.univar LP.smooth LPcden LPregression LPRelevance Predict.LP.poly |
| DTI data. | data.dti |
| A stylized simulated example. | funnel |
| Procedures for global and local inference. | fdr.thresh g2l.infer g2l.infer.boot g2l.proc getNullProb get_bh_threshold |
| Kidney data. | kidney |
| Generates Artificial RELevance Samples. | g2l.sampler LASER z.lp.center |
| Relevance-Integrated Finite Bayes. | rEB.Finite.Bayes |
| Relevance-Integrated Empirical Bayes Inference | LASER.rEB LP.post.conv rEB.proc |
