alphanorm-package: alphanorm: A package for alpha-norm regularization model
In yxwang99/alphanorm: alpha-norm regularization

Description Details Author(s) References

This package fits the alpha-norm regularization path for regression via cyclic coordinate descent and o proximal operator. It is useful in extra sparse and highly correlated model.

The alphanorm package provides five function: alphanorm, coef.alphanorm, cv.alphanorm, plot.alphanorm and predict.alphanorm

It accepts x and y for regression model and is very flexible in the choice of tuning pararmeters q and lambda. cv.alphanorm can help select the best tuning parameters using cross-validation. plot.alphanorm can produce the regularization path over a grid of values for lambda.

Guanhao Feng, Nicholas G Polson, Yuexi Wang and Jianeng Xu

Maintainer: Yuexi Wang <yxwang99@uchicago.edu>

Feng, Guanhao and Polson, Nicholas G and Wang, Yuexi and Xu, Jianeng, Sparse Regularization in Marketing and Economics (August 20, 2017). Available at SSRN: https://ssrn.com/abstract=3022856

Marjanovic, G. and V. Solo (2014). lq sparsity penalized linear regression with cyclic descent. IEEE Transactions on Signal Processing 62(6), 1464<e2><80><93>1475.

yxwang99/alphanorm documentation built on May 23, 2019, 11:34 p.m.