Fit High-Dimensional Proportional Hazards Models

Implementation of methodology designed to perform: (i) variable selection, (ii) effect estimation, and (iii) uncertainty quantification, for high-dimensional survival data. Our method uses a spike-and-slab prior with Laplace slab and Dirac spike and approximates the corresponding posterior using variational inference, a popular method in machine learning for scalable conditional inference. Although approximate, the variational posterior provides excellent point estimates and good control of the false discovery rate. For more information see Komodromos et al. (2021) .


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0.0-2 by Michael Komodromos, 6 days ago

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Authors: Michael Komodromos

Documentation:   PDF Manual  

GPL-3 license

Imports Rcpp, glmnet, survival

Linking to Rcpp, RcppEigen

See at CRAN