lineagefreq - Lineage Frequency Dynamics from Genomic Surveillance Counts
Models pathogen lineage frequency dynamics from genomic surveillance count data. Provides a unified interface for multinomial logistic regression, hierarchical partial-pooling models, and the Piantham approximation for relative reproduction number estimation. Features include rolling-origin backtesting, standardized forecast scoring, Compositional Adaptive Prediction Sets (CAPS) for horizon-aware calibrated forecasting, lineage collapsing, emergence detection, and sequencing power analysis. Designed for real-time public health surveillance of any variant-resolved pathogen. Methods described in Abousamra, Figgins, and Bedford (2024) <doi:10.1371/journal.pcbi.1012443>.
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epidemiologyforecastinggenomic-surveillancemultinomial-logistic-regressionsars-cov-2variant-frequency
5.20 score 1 stars 1 scripts 273 downloadssurvinger - Design-Adjusted Inference for Pathogen Lineage Surveillance
Provides tools for optimizing sequencing resource allocation and estimating pathogen lineage prevalence under real-world genomic surveillance conditions. Implements constrained allocation optimization for limited sequencing capacity across multiple regions and sample sources. Includes Horvitz-Thompson and post-stratified estimators that account for unequal sequencing rates, delay-adjusted nowcasting for right-censored reporting data, and combined design-weighted delay-corrected inference with uncertainty propagation.
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biostatisticsepidemiologyhorvitz-thompsonpathogen-surveillancesurvey-sampling
5.20 score 1 stars 20 scripts 343 downloadssyntheticdata - Synthetic Clinical Data Generation and Privacy-Preserving Validation
Generates synthetic clinical datasets that preserve statistical properties while reducing re-identification risk. Implements Gaussian copula simulation, bootstrap with noise injection, and Laplace noise perturbation, with built-in utility and privacy validation metrics. Useful for privacy-aware data sharing in multi-site clinical research. Validates synthetic data quality via distributional similarity (Kolmogorov-Smirnov), discriminative accuracy (real-vs-synthetic classifier), and nearest-neighbor privacy ratio. Methods described in Jordon et al. (2022) <doi:10.48550/arXiv.2205.03257> and Snoke et al. (2018) <doi:10.1111/rssa.12358>.
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clinical-datadifferential-privacyhealthcareprivacy-preservingsynthetic-data
4.30 score 1 stars 7 scripts 238 downloadsclinicalfair - Algorithmic Fairness Assessment for Clinical Prediction Models
Post-hoc fairness auditing toolkit for clinical prediction models. Unlike in-processing approaches that modify model training, this package evaluates existing models by computing group-wise fairness metrics (demographic parity, equalized odds, predictive parity, calibration disparity), visualizing disparities across protected attributes, and performing threshold-based mitigation. Supports intersectional analysis across multiple attributes and generates audit reports useful for fairness-oriented auditing in clinical AI settings. Methods described in Obermeyer et al. (2019) <doi:10.1126/science.aax2342> and Hardt, Price, and Srebro (2016) <doi:10.48550/arXiv.1610.02413>.
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algorithmic-fairnessbias-detectionclinical-aifairnesshealthcare
4.30 score 1 scripts 291 downloadsexposomeSC - Single-Cell Exposome-Wide Association Studies with Cross-Omic Network Inference
A framework for cell-type-specific exposome-wide association studies (sc-ExWAS) that bridges donor-level environmental exposures with single-cell resolution gene expression. The package extends SingleCellExperiment to store exposure measurements alongside transcriptomic data, and implements donor-level raw-count pseudobulk inference to avoid cellular pseudoreplication (Squair et al. 2021 <doi:10.1038/s41467-021-25960-2>). The default workflow uses robust edgeR quasi-likelihood models, exposure-independent expression-support filtering, complete-transcriptome offsets, design-rank diagnostics, and global multiplicity correction. Additional modules support dose-response modelling, exposure mixtures, cross-cohort synthesis, and cell-level gene-protein coupling. Exploratory decomposition and mediation utilities report model-based associations; they do not by themselves identify causal effects.
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singlecellgeneexpressiondifferentialexpressiontranscriptomicscellbiologystatisticalmethodnetworknetworkinferenceregression
3.78 scoreWithinHostExperiment - Within-Host Pathogen Variant QC, Diversity, and Bottleneck
Provides a SummarizedExperiment-based container and workflow functions for analysing intra-host single nucleotide variants (iSNV) from pathogen deep sequencing data. Supports replicate-aware quality control with full provenance tracking, multi-caller import harmonisation, within-host diversity quantification, and standardised export to transmission bottleneck estimation tools. Designed as infrastructure that bridges upstream variant callers (iVar, LoFreq, Freebayes) with downstream analysis packages through a common data object with uncertainty-preserving QC.
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geneticsvariantannotationsequencinggenomicvariationqualitycontrolvariantdetectiondataimportlongread
3.30 score 5 scripts