# The Gaussian Covariate Method for Variable Selection

## Documentation for package ‘gausscov’ version 0.1.0

- DESCRIPTION file.

## Help Pages

abcql | American Business Cycle
boston | Boston data
decode | Decodes the number of a subset selected by fmch to give the covariates
decomp | decompose a given interaction ic into its component parts
dentx | Dental data, the 13 covariates, the dependent variable ist denty
denty | Dental data, hardness of gold fillings, the corresponding 13 covariates are dentx
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 and fmch1.
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