Author: Douglas Bates <bates@stat.wisc.edu>, David Brahm <brahm@alum.mit.edu>
Description: Matrix Summary Functions in C

Title: Read big text files column by column
Description: This package tries to solve the memory restrictions posed
by large text files by breaking them into its columns, which
are subsequently read individually into R.

Title: Read big text files column by column, sometimes much bigger than
by large text files by breaking them into their columns first.
These columns are later read individually into R. The package
also provides some helper functions for manipulating the newly

Description: This package tries to solve the memory restrictions posed by large text files by breaking them into their columns first. These columns are later read individually into R. The package also provides some helper functions for manipulating the newly created objects.

Title: Tests for column correlation in the presence of row correlation
Description: Given a matrix X with possibly correlated rows, tests if
the columns of X are correlated. Also estimates the
mean-squared row correlation (see paper at

Title: cold: a package for Count Longitudinal Data
Author: M. Helena Gon�alves and M. Salom� Cabral, apart from a set of
Fortran-77 subroutines written by R. Piessens and E. de
Doncker, belonging to the suite "Quadpack"
Description: cold performs regression analysis for longitudinal count
data using a self-decomposable probability distribution, a
random intercept term is also allowed. Estimation is via

Author: M. Helena Gon�alves and M. Salom� Cabral,
and E. de Doncker, belonging to the suite "Quadpack"
data using a self-decomposable probability distribution,
a random intercept term is also allowed. Estimation is via maximization

Description: Performs regression analysis for longitudinal count

Author: M. Helena Goncalves and M. Salome Cabral,
Maintainer: M. Helena Goncalves <mhgoncal@ualg.pt>
Description: Performs regression analysis for longitudinal count data,
allowing for serial dependence among observations from a given
individual and two dimensional random effects on the linear predictor.
Estimation is via maximization of the exact likelihood of a suitably
defined model. Missing values and unbalanced data are allowed;
Depends: R (>= 3.1.0), methods, stats, graphics, grDevices, utils,