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S3VS - Structured Screen-and-Select Variable Selection in Linear, Generalized Linear, and Survival Models

Performs variable selection using the structured screen-and-select (S3VS) framework in linear models, generalized linear models with binary data, and survival models such as the Cox model and accelerated failure time (AFT) model.

Last updated

jagscpp

4.00 score 468 downloads

WSwavelet - Bayesian Wavelet Denoising with Wendland-Semicircle Slab Mixture

Bayesian wavelet denoising using a resolution-adaptive spike-and-slab prior whose continuous slab is a mixture of a compactly supported Wendland-type density and the semicircle density. The package provides Gaussian and Laplace working-likelihood versions, empirical-Bayes fitting of resolution-specific slab weights, robust noise-scale estimation, posterior-mean coefficient shrinkage, reconstruction by the inverse discrete wavelet transform, and diagnostic tools.

Last updated

3.70 score

BHMSMAfMRI - Bayesian Hierarchical Multi-Subject Multiscale Analysis of Functional MRI (fMRI) Data

Package BHMSMAfMRI performs Bayesian hierarchical multi-subject multiscale analysis of fMRI data as described in Sanyal & Ferreira (2012) <DOI:10.1016/j.neuroimage.2012.08.041>, or other multiscale data, using wavelet-based prior that borrows strength across subjects and provides posterior smoothed images of the effect sizes and samples from the posterior distribution.

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bayesian-hierarchical-modelsfmri-data-analysismultiscale-datawavelet-transformopenblascppopenmp

2.85 score 14 scripts 355 downloads

NLPwavelet - Bayesian Wavelet Analysis Using Non-Local Priors

Performs Bayesian wavelet analysis using individual non-local priors as described in Sanyal & Ferreira (2017) <DOI:10.1007/s13571-016-0129-3> and non-local prior mixtures as described in Sanyal (2025) <DOI:10.48550/arXiv.2501.18134>.

Last updated

openblascppopenmp

2.70 score 243 downloads

GWASinlps - Non-Local Prior Based Iterative Variable Selection Tool for Genome-Wide Association Studies

Performs variable selection with data from Genome-wide association studies (GWAS), or other high-dimensional data with continuous, binary or survival outcomes, combining in an iterative framework the computational efficiency of the structured screen-and-select variable selection strategy based on some association learning and the parsimonious uncertainty quantification provided by the use of non-local priors (see Sanyal et al., 2019 <DOI:10.1093/bioinformatics/bty472>).

Last updated

gwashigh-dimensionalnon-local-priorvariable-selectionopenblascpp

2.70 score 3 scripts 403 downloads