The Gaussian Covariate Method for Variable Selection

Documentation for package ‘gausscov’ version 0.0.7

abcql	American Business Cycle
boston	Boston data
decode	Decodes the number of a subset selected by flmmdch to give the covariates
decomp	decompose a given interaction ic into its component parts
dentx	Dental data, the 20 covariates
denty	Dental data, hardness
f1st	Stepwise selection of covariates
f2st	Repeated stepwise selection of covariates
fgeninter	generation of interactions
fgentrig	generation of sine and cosine functions
fgr1st	Calculates an independence graph using stepwise selection
fgr2st	Calculates an independence graph using repeated stepwise selection
fmch	Calculates all subsets where each included covariate is significant.
fpval	Calculates the regression coefficients, the P-values and the standard P-values for the chosen subset ind
frmch	Robust selection of covariates using Huber's psi-funtion or Hampel's redescending psi-function based on all subsets
frrg	Robust regression using Huber's psi-function or Hampel's redescending psi-function without P-values
frrgp	Robust regression using Huber's psi-function or Hampel's three part redescending psi-function providing P-values
frst	Robust stepwise selection of covariates
fselect	Selects the subsets specified by fmch.
fsimords	Simulates the number of false positives for given dimensions (n,k) and given order statistics nu
lx.original	Leukemia data
ly.original	Leukemia data
mel_temp	Melbourne minimum temperature
redwine	Redwine data
snspt	Sunspot data