vbsr: Variational Bayes Spike Regression Regularized Linear Models
Version 0.0.5

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.

AuthorBenjamin Logsdon
Date of publication2014-06-05 22:50:33
MaintainerBenjamin Logsdon <ben.logsdon@sagebase.org>
LicenseGPL-2
Version0.0.5
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("vbsr")

Popular man pages

compute_KL: Compute an empirical Kullback Leibler (KL) divergence for an...
vbsr: fit a linear model with variational Bayes spike penalty
See all...

All man pages Function index File listing

Man pages

compute_KL: Compute an empirical Kullback Leibler (KL) divergence for an...
vbsr: fit a linear model with variational Bayes spike penalty

Functions

compute_KL Man page Source code
vbsr Man page Source code

Files

inst
inst/doc
inst/doc/vbsr.R
inst/doc/vbsr.rnw
inst/doc/vbsr.pdf
src
src/vbsr.h
src/vbsr.c
src/Makevars.win
NAMESPACE
R
R/vbsr.R
R/compute_KL.R
vignettes
vignettes/vbsr-003.pdf
vignettes/vbsr-007.pdf
vignettes/vbsr.rnw
vignettes/vbsr-010.pdf
vignettes/vbsr.tex
vignettes/vbsr-014.pdf
vignettes/vbsr-concordance.tex
vignettes/vbsr-015.pdf
vignettes/vbsr-011.pdf
vignettes/vbsr-002.pdf
vignettes/vbsr-006.pdf
MD5
build
build/vignette.rds
DESCRIPTION
man
man/compute_KL.Rd
man/vbsr.Rd
vbsr documentation built on May 19, 2017, 10:33 p.m.