bigmemory-package         package:bigmemory         R Documentation

_b_i_g_m_e_m_o_r_y: _m_a_s_s_i_v_e _m_a_t_r_i_c_e_s _i_n (_p_o_s_s_i_b_l_y _s_h_a_r_e_d) _m_e_m_o_r_y.

_D_e_s_c_r_i_p_t_i_o_n:

     'bigmemory' implements massive matricies in C++  and supports
     their basic manipulation and exploration.   Access to and
     manipulation of a 'big.matrix' object is exposed in R by an S4 
     class whose interface is simlar to an R 'matrix'.

_D_e_t_a_i_l_s:


       Package:  bigmemory
       Type:     Package
       Version:  3.10
       Date:     2009-07-15
       License:  LGPL-3

     Multi-gigabyte data sets challenge and frustrate R users even on
     well-equipped hardware. C/C++ and Fortran programming can be
     helpful, but are cumbersome for interactive data analysis and lack
     the flexibility and power of R's rich statistical programming
     environment.  The package 'bigmemory' bridges this gap,
     implementing massive matrices  and supporting their basic
     manipulation and exploration.  It is ideal for problems involving
     the analysis in R of manageable subsets of the data, or when an
     analysis is conducted mostly in C++.  The data structures may be
     allocated to shared memory with transparent read and write
     locking, allowing separate  processes on the same computer to
     share access to a single copy of the data set.  The data
     structures may also be file-backed, allowing users to more easily
     manage and analyze data sets larger than available RAM. These
     features of 'bigmemory' open the door for powerful and
     memory-efficient parallel analyses and data mining of massive data
     sets.

     Note that 'options(bigmemory.typecast.warning)' is available and
     can be set to avoid annoying warnings that might occur if, for
     example you assign R objects (typically type double) to char,
     short, or integer 'big.matrix' objects.

     This package is still actively developed.  Please send us an email
     letting us know you are trying the package, and we'll keep you
     abreast on updates.

     NOTE: The 3.X and earlier versions support a limited number of
     columns (due to mutex limitations), roughly 50,000 on a typical
     Linux system. This restriction will be removed in versions 4.0 and
     beyond.  There were row limitations (due to a bug that has now
     been fixed) in versions 3.8 and earlier of roughly 1 billion, but
     this has been fixed in versions 3.82 and later.  We apologize for
     the inconvenience, and appreciate and and all feedback.  - Jay and
     Mike

_A_u_t_h_o_r(_s):

     John W. Emerson and Michael J. Kane

     Maintainer: Jay Emerson <john.emerson@yale.edu>

_R_e_f_e_r_e_n_c_e_s:

     See <URL: http://www.stat.yale.edu/~jay/bigmemory>.

_S_e_e _A_l_s_o:

     For example, 'big.matrix', 'mwhich', 'colmean', 'biglm'

_E_x_a_m_p_l_e_s:

     # Our examples are all trivial in size, rather than burning huge amounts
     # of memory simply to demonstrate the package functionality.

     x <- big.matrix(5, 2, type="integer", init=0)
     colnames(x)=c("alpha", "beta")
     x
     x[,]
     x[,1] <- 1:5
     x[,]
     mean(x)
     colmean(x)
     summary(x)

