New driver function bigRreg Fits regression model y = 1 alpha + X beta + e where 1 is an n x 1 vector of ones and X is an n x p matrix. The model estimates are returned as alphahat and betahat. Note: coef is NULL by design as it is reserved for future development.

Uses biglm to obtain projection rather core R qr as was the case in the previous version --- previous version now renamed bigRfit1. New driver version of bigRfit is planned.

Rank-based (R) estimation for statistical models is a robust nonparametric alternative to classical estimation procedures such as least squares. R methods have been developed for models ranging from linear models, to linear mixed models, to timeseries, to nonlinear models. Advantages of these R methods over traditional methods such as maximum-likelihood or least squares is that they require fewer assumptions, are robust to gross outliers, and are highly efficient at a wide range of distributions. See also Rfit.

bigRfit

• uses a partial ranking to save computational time
• coordinate free Newton steps w/ backtracking
• a density estimate of the scale parameter tau