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>.
Last updated 4 months ago
6.15 score 7 stars 3 scripts 603 downloadspsbcSpeedUp - 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>).
Last updated 5 months ago
bayesian-cox-modelsomics-datasurvival-analysis
4.78 score 3 stars 172 downloadsBayesSurvive - 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.
Last updated 2 months ago
bayesian-cox-modelsbayesian-variable-selectiongraph-learninghigh-dimensional-statisticsomics-data-integrationsurvival-analysis
4.70 score 1 stars 1 scripts 193 downloads