R: Estimating the Error Variance in a High-Dimensional Linear Model

# Estimating the Error Variance in a High-Dimensional Linear Model

## Documentation for package ‘natural’ version 0.9.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS

## Help Pages

natural-package | natural: Natural and Organic lasso estimates of error variance in high-dimensional linear models
getLam_olasso | Get the two (theoretical) values of lambdas used in the organic lasso
getLam_slasso | Get the two (theoretical) values of lambdas used in scaled lasso
make_sparse_model | Generate sparse linear model and random samples
natural | natural: Natural and Organic lasso estimates of error variance in high-dimensional linear models
nlasso_cv | Cross-validation for natural lasso
nlasso_path | Fit a linear model with natural lasso
olasso | Error standard deviation estimation using organic lasso
olasso_cv | Cross-validation for organic lasso
olasso_path | Fit a linear model with organic lasso
olasso_slow | Solve organic lasso problem with a single value of lambda The lambda values are for slow rates, which could give less satisfying results
plot.natural.cv | plot a natural.cv object
plot.natural.path | plot a natural.path object
print.natural.path | print a natural.path object
standardize | Standardize the n -by- p design matrix X to have column means zero and ||X_j||_2^2 = n for all j