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BayesSUR - Bayesian Seemingly Unrelated Regression Models in High-Dimensional Settings

Bayesian seemingly unrelated regression with general variable selection and dense/sparse covariance matrix. The sparse seemingly unrelated regression is described in Bottolo et al. (2021) <doi:10.1111/rssc.12490>, the software paper is in Zhao et al. (2021) <doi:10.18637/jss.v100.i11>, and the model with random effects is described in Zhao et al. (2024) <doi:10.1093/jrsssc/qlad102>.

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openblascppopenmp

5.98 score 8 stars 4 scripts 973 downloads

moewishart - Mixture-of-Experts Wishart Models for Covariance Data

Methods for maximum likelihood and Bayesian estimation for the Wishart mixture model and the mixture-of-experts Wishart (MoE-Wishart) model. The package provides four inference algorithms for these models, each implemented using the expectation–maximization (EM) algorithm for maximum likelihood estimation and a fully Bayesian approach via Gibbs-within-Metropolis–Hastings sampling.

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5.14 score 21 scripts 408 downloads

GPTCM - Generalized Promotion Time Cure Model with Bayesian Shrinkage Priors

Generalized promotion time cure model (GPTCM) via Bayesian hierarchical modeling for multiscale data integration (Zhao et al. (2025) <doi:10.48550/arXiv.2509.01001>). The Bayesian GPTCMs are applicable for both low- and high-dimensional data.

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bayesian-inferencecell-populationsmultiscale-dataomics-data-integrationsurvival-analysistumor-heterogeneitytumor-microenvironmentvariable-selectionopenblascppopenmp

4.93 score 4 scripts 449 downloads

EnrichIntersect - Enrichment Analysis and Intersecting Sankey Diagram

A flexible tool for enrichment analysis based on user-defined sets. It allows users to perform over-representation analysis of the custom sets among any specified ranked feature list, hence making enrichment analysis applicable to various types of data from different scientific fields. 'EnrichIntersect' also enables an interactive means to visualize identified associations based on, for example, the mix-lasso model (Zhao et al., 2022 <doi:10.1016/j.isci.2022.104767>) or similar methods.

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cpp

4.70 score 5 stars 260 downloads

BayesSurvive - Bayesian Survival Models for High-Dimensional Data

An implementation of Bayesian survival models with graph-structured selection priors for sparse identification of omics features predictive of survival (Madjar et al., 2021 <doi:10.1186/s12859-021-04483-z>) and its extension to use a fixed graph via a Markov Random Field (MRF) prior for capturing known structure of omics features, e.g. disease-specific pathways from the Kyoto Encyclopedia of Genes and Genomes database (Hermansen et al., 2025 <doi:10.48550/arXiv.2503.13078>).

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bayesian-cox-modelsbayesian-variable-selectiongraph-learninghigh-dimensional-statisticsomics-data-integrationsurvival-analysisopenblascppopenmp

4.60 score 4 stars 1 scripts 259 downloads

psbcSpeedUp - Penalized Semiparametric Bayesian Cox Models

Algorithms to speed up the Bayesian Lasso Cox model (Lee et al., Int J Biostat, 2011 <doi:10.2202/1557-4679.1301>) and the Bayesian Lasso Cox with mandatory variables (Zucknick et al. Biometrical J, 2015 <doi:10.1002/bimj.201400160>).

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bayesian-cox-modelsbayesian-lassoomics-datasurvival-analysisopenblascppopenmp

4.48 score 3 stars 232 downloads