Implements an algorithm for variable selection in highdimensional linear regression using the "tilted correlation", a new way of measuring the contribution of each variable to the response which takes into account high correlations among the variables in a datadriven way.
Author  Haeran Cho [aut, cre], Piotr Fryzlewicz [aut] 
Date of publication  20161226 12:25:13 
Maintainer  Haeran Cho <haeran.cho@bristol.ac.uk> 
License  GPL (>= 2) 
Version  1.1.1 
Package repository  View on CRAN 
Installation  Install the latest version of this package by entering the following in R:



All man pages Function index File listing
Man pages  

col.norm: Compute the L2 norm of each column  
get.thr: Select a threshold for sample correlation matrix  
lse.beta: Compute the least squares estimate on a given index set  
projection: Compute the projection matrix onto a given set of variables  
select.model: Select the final model  
thresh: Hardthreshold a matrix  
tilting: Variable selection via Tilted Correlation Screening algorithm  
tiltingpackage: Variable Selection via Tilted Correlation Screening Algorithm 
Functions  

col.norm  Man page Source code 
get.thr  Man page Source code 
lse.beta  Man page Source code 
projection  Man page Source code 
select.model  Man page Source code 
thresh  Man page Source code 
tilting  Man page Source code 
tiltingpackage  Man page 
Files  

NAMESPACE
 
R
 
R/package.R  
MD5
 
DESCRIPTION
 
man
 
man/lse.beta.Rd  
man/select.model.Rd  
man/thresh.Rd  
man/projection.Rd  
man/col.norm.Rd  
man/tiltingpackage.Rd  
man/get.thr.Rd  
man/tilting.Rd 
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