dglars (version 1.0.5)

dglars-package: Differential geometric LARS (dgLARS) method for some generalized linear models

Description

This package implements the differential geometric extension of the least angle regression method (Efron et al., 2004) proposed in Augugliaro et al. (2013). Using this version of the package it is possible to compute the dgLARS solution curve for the logistic and Poisson regression models. The solution curve can be computed using two different algorithms, i.e., a predictor-corrector algorithm and a cyclic coordinate descent algorithm.

Details

Package:

dglars

Type:

Package

Version:

1.0.5

Date:

2014-08-09

References

Augugliaro L., Mineo A.M. and Wit E.C. (2014) dglars: An R Package to Estimate Sparse Generalized Linear Models, Journal of Statistical Software, Vol 59(8), 1-40. http://www.jstatsoft.org/v59/i08/.

Augugliaro L., Mineo A.M. and Wit E.C. (2013) dgLARS: a differential geometric approach to sparse generalized linear models, Journal of the Royal Statistical Society. Series B., Vol 75(3), 471-498. Augugliaro L., Mineo A.M. and Wit E.C. (2012) Differential geometric LARS via cyclic coordinate descent method, in Proceeding of COMPSTAT 2012, pp. 67-79. Limassol, Cyprus.

Efron B., Hastie T., Johnstone I. and Tibshirani R. (2004) Least Angle Regression, The Annals of Statistics, Vol. 32(2), 407-499.