https://nalin21478.r-universe.devRecent package updates in nalin21478R-universehttps://github.com/nalin21478.pnghttps://nalin21478.r-universe.devSun, 18 Oct 2026 22:14:11 GMTnaa4050@med.cornell.edu (Nalin Arora)Provides a unified interface for multi-omics prediction using early and late fusion for continuous, binary, multiclass, and survival outcomes. It supports both MultiAssayExperiment and PCL-style inputs, performs input validation and feature/sample harmonization across layers, and provides model fitting, prediction, plotting, and variable-importance utilities.https://github.com/r-universe/biocstaging/actions/runs/39176359783Sun, 18 Oct 2026 22:14:11 GMTIntegratedLearner0.99.4successhttps://biocstaging.r-universe.devhttps://github.com/BiocStaging/IntegratedLearnerIntegratedLearner.RmdIntegratedLearner.htmlIntegratedLearner2025-11-13 19:16:372026-10-18 22:14:11naa4050@med.cornell.edu (Nalin Arora)TweedSpot detects spatially variable genes (SVGs) in spatially resolved transcriptomics data using self-adaptive Tweedie generalized additive models with nonparametric spatial smoothing. The method models raw counts directly, avoiding the bias introduced by log-transformation, adapts a gene-specific Tweedie index parameter to the mean-variance relationship of each gene, and offers an optional two-part formulation to accommodate excess zeros. Inputs and outputs use the SpatialExperiment infrastructure for interoperability with the Bioconductor ecosystem.https://github.com/r-universe/biocstaging/actions/runs/38618581532Wed, 14 Oct 2026 16:02:04 GMTTweedSpot0.99.3successhttps://biocstaging.r-universe.devhttps://github.com/BiocStaging/TweedSpotTweedSpot.RmdTweedSpot.htmlTweedSpot2026-08-24 16:31:212026-10-14 07:49:15naa4050@med.cornell.edu (Nalin Arora)A toolkit for differential analysis of omics data. Implements a CPLM-based statistical workflow built on the Tweedie distribution. Unlike traditional single-omics tools, Tweedieverse is technology-agnostic and can be applied to both count and continuous measurements arising from diverse high-throughput technologies (e.g. transcript abundances from bulk and single-cell RNA-Seq studies in the form of UMI counts or non-UMI counts, microbiome taxonomic and functional profiles in the form of counts or relative abundances, and compound abundance levels or peak intensities from metabolomics and other mass spectrometry-based experiments, among others). The software includes multiple customization options such as the inclusion of random effects and multiple covariates along with several data exploration capabilities and visualization modules in a unified estimation umbrella.https://github.com/r-universe/biocstaging/actions/runs/36491322643Mon, 28 Sep 2026 16:58:52 GMTTweedieverse0.99.0successhttps://biocstaging.r-universe.devhttps://github.com/BiocStaging/TweedieverseTweedieverse.RmdTweedieverse.htmlTweedieverse2026-09-25 21:59:582026-09-28 16:53:37naa4050@med.cornell.edu (Nalin Arora)Provides access to curated paired human microbiome-metabolome study bundles available through Bioconductor ExperimentHub. The package organizes matched sample metadata, species abundance profiles, original and harmonized metabolite profiles, and study-specific annotation resources including metabolite identifier mappings, microbial producer annotations, disease associations, pathway annotations, and drug similarity matches. The resource is designed to support consistent and reproducible cross-study microbiome-metabolome analyses.https://github.com/r-universe/bioc/actions/runs/38045844913Wed, 16 Sep 2026 16:39:35 GMTHuMMANet0.99.3successhttps://bioc.r-universe.devhttps://github.com/bioc/HuMMANetHuMMANet.RmdHuMMANet.htmlHuMMANet2026-08-04 23:13:572026-09-16 16:36:16naa4050@med.cornell.edu (Nalin Arora)The DAssemble package implements an ensemble framework for differential-abundance and differential-expression analysis across bulk RNA-seq, single-cell RNA-seq, and microbiome studies. It wraps a collection of popular DA/DE methods as core methods and combines them via Cauchy Combination Tests (CCT), optionally augmented by simple enhancers such as Wilcoxon, Kolmogorov–Smirnov, and presence–absence logistic regression.https://github.com/r-universe/bioc/actions/runs/38152433276Thu, 10 Sep 2026 18:52:41 GMTDAssemble0.99.4successhttps://bioc.r-universe.devhttps://github.com/bioc/DAssembleDAssemble.RmdDAssemble.htmlDAssemble: Ensemble Differential Analysis2026-05-30 16:49:132026-09-10 18:47:11