Package: biotmle 1.17.1
biotmle: Targeted Learning with Moderated Statistics for Biomarker Discovery
Tools for differential expression biomarker discovery based on microarray and next-generation sequencing data that leverage efficient semiparametric estimators of the average treatment effect for variable importance analysis. Estimation and inference of the (marginal) average treatment effects of potential biomarkers are computed by targeted minimum loss-based estimation, with joint, stable inference constructed across all biomarkers using a generalization of moderated statistics for use with the estimated efficient influence function. The procedure accommodates the use of ensemble machine learning for the estimation of nuisance functions.
Authors:
biotmle_1.17.1.tar.gz
biotmle_1.17.1.zip(r-4.5)biotmle_1.17.1.zip(r-4.4)biotmle_1.17.1.zip(r-4.3)
biotmle_1.17.1.tgz(r-4.4-any)biotmle_1.17.1.tgz(r-4.3-any)
biotmle_1.17.1.tar.gz(r-4.5-noble)biotmle_1.17.1.tar.gz(r-4.4-noble)
biotmle_1.17.1.tgz(r-4.4-emscripten)biotmle_1.17.1.tgz(r-4.3-emscripten)
biotmle.pdf |biotmle.html✨
biotmle/json (API)
NEWS
| # Install 'biotmle' in R: |
| install.packages('biotmle', repos = c('https://nhejazi.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/nhejazi/biotmle/issues
On BioConductor:biotmle-1.31.0(bioc 3.21)biotmle-1.30.0(bioc 3.20)
regressiongeneexpressiondifferentialexpressionsequencingmicroarrayrnaseqimmunooncologybioconductorbioconductor-packagebioconductor-packagesbioinformaticsbiomarker-discoverybiostatisticscausal-inferencecomputational-biologymachine-learningstatisticstargeted-learning
Last updated 3 years agofrom:b13f8b4582. Checks:1 OK, 6 NOTE. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Jan 17 2025 |
| R-4.5-win | NOTE | Jan 17 2025 |
| R-4.5-linux | NOTE | Jan 17 2025 |
| R-4.4-win | NOTE | Jan 17 2025 |
| R-4.4-mac | NOTE | Jan 17 2025 |
| R-4.3-win | NOTE | Dec 18 2024 |
| R-4.3-mac | NOTE | Dec 18 2024 |
Exports:.biotmlebiomarkertmleeifheatmap_icmodtest_icrnaseq_ictoptablevolcano_ic
Dependencies:abindaskpassassertthatBHBiobaseBiocGenericsBiocParallelbitopsbootcaToolsclicodetoolscolorspacecpp11crayoncubaturecurlcvAUCdata.tableDelayedArraydigestdplyrdrtmlefansifarverforeachformatRfutile.loggerfutile.optionsfuturefuture.applygamgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggdendroggplot2ggsciglobalsgluegplotsgtablegtoolshttrIRangesisobanditeratorsjsonliteKernSmoothlabelinglambda.rlatticelifecyclelimmalistenvmagrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmgcvmimemunsellnlmennlsnpopensslparallellypillarpkgconfigplyrquadprogquantregR6RColorBrewerRcpprlangROCRS4ArraysS4VectorsscalessnowSparseArraySparseMstatmodSummarizedExperimentsuperheatSuperLearnersurvivalsystibbletidyselectUCSC.utilsutf8vctrsviridisLitewithrXVector
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Biomarker Evaluation with Targeted Minimum Loss Estimation of the ATE | biomarkertmle |
| Constructor for class bioTMLE | .biotmle bioTMLE-class |
| S4 class union data.frame_OR_EList | data.frame_OR_EList-class |
| Accessor for Table of Raw Efficient Influence Function Values | eif |
| TMLE procedure using ATE for Biomarker Identication from Exposure | exp_biomarkertmle |
| Heatmap for class biotmle | heatmap_ic |
| Moderated Statistical Tests for Influence Functions | modtest_ic |
| Plot p-values from moderated statistical tests for class biotmle | plot.bioTMLE |
| Utility for using voom transformation with TMLE for biomarker discovery | rnaseq_ic |
| Accessor for Results of Moderated Influence Function Hypothesis Testing | toptable |
| Volcano plot for class biotmle | volcano_ic |
