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vbsr (version 0.0.5)

Variational Bayes Spike Regression Regularized Linear Models

Description

Efficient algorithm for solving ultra-sparse regularized regression models using a variational Bayes algorithm with a spike (l0) prior. Algorithm is solved on a path, with coordinate updates, and is capable of generating very sparse models. There are very general model diagnostics for controling type-1 error included in this package.

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Version

Install

install.packages('vbsr')

Monthly Downloads

7

Version

0.0.5

License

GPL-2

Maintainer

Benjamin Logsdon

Last Published

June 5th, 2014

Functions in vbsr (0.0.5)

compute_KL

Compute an empirical Kullback Leibler (KL) divergence for an observed distribution of Z-statistics
vbsr

fit a linear model with variational Bayes spike penalty