In glmnet_1.5 a poor default was set for the argument type which caused the program
to be very slow or even crash when nvar (p) is very large.

The argument type (now called type.gaussian) has two options,
"covariance" or "naive", and is used for the default  family="gaussion" model (squared error loss).

When type.gaussian="covariance", all inner-products between variables in the active set
and all other variables are cached, and can cause considerable speedup when nobs is large.
However, when nvar is large (>500) the matrix to be stored gets large, and this strategy becomes counterproductive.
In addition, when nvar is very large, glmnet tries to allocate a storage space for this matrix that can exceed the 
machine's memory.

When type.gaussian="naive", nothing is cached, and inner products (loop over nobs) are computed whenever needed.

In this minor upgrade,  the default is "covariance" if nvar<500, else it is "naive". We established this rule after conducting
extensive simulations.

In addition, the argument was renamed so as not to collide with the argument type to cv.glmnet, which is now renamed to
type.measure.  In both cases, abbreviations work.

-------------------------------------------------------------------
  Trevor Hastie                                   hastie@stanford.edu  
  Professor, Department of Statistics, Stanford University
  Phone: (650) 725-2231 (Statistics)          Fax: (650) 725-8977  
  (650) 498-5233 (Biostatistics)   Fax: (650) 725-6951
  URL: http://www-stat.stanford.edu/~hastie  
   address: room 104, Department of Statistics, Sequoia Hall
           390 Serra Mall, Stanford University, CA 94305-4065  
 --------------------------------------------------------------------





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